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Used together with:
base (69)
use (59)
data (49)
select (40)
effici (39)

Stem sampl$ (all stems)

544 papers:

DATEDATE-2015-HuangLH #multi
Volume-oriented sample preparation for reactant minimization on flow-based microfluidic biochips with multi-segment mixers (CMH, CHL, JDH), pp. 1114–1119.
HTHT-2015-SampsonMML #clustering #keyword #twitter
Surpassing the Limit: Keyword Clustering to Improve Twitter Sample Coverage (JS, FM, RM, HL), pp. 237–245.
PODSPODS-2015-HuQT #memory management
External Memory Stream Sampling (XH, MQ, YT), pp. 229–239.
VLDBVLDB-2015-KimBPIMR #agile #visualisation
Rapid Sampling for Visualizations with Ordering Guarantees (AK, EB, AGP, PI, SM, RR), pp. 521–532.
VLDBVLDB-2015-NaziZT0D #network #online #performance #social
Walk, Not Wait: Faster Sampling Over Online Social Networks (AN, ZZ, ST, NZ, GD), pp. 678–689.
VLDBVLDB-2015-Zhou0D #network #online #performance #social
Leveraging History for Faster Sampling of Online Social Networks (ZZ, NZ, GD), pp. 1034–1045.
TACASTACAS-2015-HansenWCNK #model checking #semantics #statistics
Semantic Importance Sampling for Statistical Model Checking (JPH, LW, SC, DdN, MHK), pp. 241–255.
TACASTACAS-2015-UnnoT #horn clause #recursion
Inferring Simple Solutions to Recursion-Free Horn Clauses via Sampling (HU, TT), pp. 149–163.
MSRMSR-2015-MartinHJSZ #mining #problem
The App Sampling Problem for App Store Mining (WM, MH, YJ, FS, YZ), pp. 123–133.
STOCSTOC-2015-AggarwalDRS #problem #using
Solving the Shortest Vector Problem in 2n Time Using Discrete Gaussian Sampling: Extended Abstract (DA, DD, OR, NSD), pp. 733–742.
STOCSTOC-2015-CohenP
Lp Row Sampling by Lewis Weights (MBC, RP), pp. 183–192.
CSCWCSCW-2015-ReineckeG #named #online #scalability
LabintheWild: Conducting Large-Scale Online Experiments With Uncompensated Samples (KR, KZG), pp. 1364–1378.
CSCWCSCW-2015-VashisthaCT
Increasing the Reach of Snowball Sampling: The Impact of Fixed versus Lottery Incentives (AV, EC, WT), pp. 1359–1363.
HCIDHM-EH-2015-LiuCKQF #data analysis #power management
Balancing Power Consumption and Data Analysis Accuracy Through Adjusting Sampling Rates: Seeking for the Optimal Configuration of Inertial Sensors for Power Wheelchair Users (TL, CC, MK, GQ, JF), pp. 184–192.
HCIDHM-HM-2015-SugimotoTNG #comparison #eye tracking #process
Comparison of Eye Movement During the Polishing Process of Metallographic Sample Between Expert and Nonexpert (TS, YT, HN, AG), pp. 399–410.
HCIDHM-HM-2015-SugimotoYNG #analysis #process
EMG Activity Analysis of Expert Skills on Handheld Grinding Work for Metallographic Sample (TS, HY, HN, AG), pp. 165–173.
ICEISICEIS-v1-2015-SarmentoCG #network #streaming #using
Streaming Networks Sampling using top-K Networks (RS, MC, JG), pp. 228–234.
ECIRECIR-2015-Kelly #analysis #estimation #information retrieval #statistics
Statistical Power Analysis for Sample Size Estimation in Information Retrieval Experiments with Users (DK), pp. 822–825.
ICMLICML-2015-KomiyamaHN #analysis #multi #probability #problem
Optimal Regret Analysis of Thompson Sampling in Stochastic Multi-armed Bandit Problem with Multiple Plays (JK, JH, HN), pp. 1152–1161.
ICMLICML-2015-KuklianskyS #linear #performance
Attribute Efficient Linear Regression with Distribution-Dependent Sampling (DK, OS), pp. 153–161.
ICMLICML-2015-WangFS #for free #monte carlo #privacy #probability
Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo (YXW, SEF, AJS), pp. 2493–2502.
ICMLICML-2015-Yang0JZ #bound #fault #set
An Explicit Sampling Dependent Spectral Error Bound for Column Subset Selection (TY, LZ, RJ, SZ), pp. 135–143.
ICMLICML-2015-ZhaoZ #optimisation #probability
Stochastic Optimization with Importance Sampling for Regularized Loss Minimization (PZ, TZ), pp. 1–9.
ICMLICML-2015-ZhuG #complexity #robust #towards
Towards a Lower Sample Complexity for Robust One-bit Compressed Sensing (RZ, QG), pp. 739–747.
KDDKDD-2015-ChenXZX
Differentially Private High-Dimensional Data Publication via Sampling-Based Inference (RC, QX, YZ, JX), pp. 129–138.
KDDKDD-2015-Cohen #statistics
Stream Sampling for Frequency Cap Statistics (EC), pp. 159–168.
KDDKDD-2015-IkonomovskaJD #predict #realtime #using
Real-Time Bid Prediction using Thompson Sampling-Based Expert Selection (EI, SJ, AD), pp. 1869–1878.
KDDKDD-2015-LanH #complexity #learning #multi
Reducing the Unlabeled Sample Complexity of Semi-Supervised Multi-View Learning (CL, JH), pp. 627–634.
KDDKDD-2015-Li #consistency
0-Bit Consistent Weighted Sampling (PL), pp. 665–674.
KDDKDD-2015-LimK #graph #named
MASCOT: Memory-efficient and Accurate Sampling for Counting Local Triangles in Graph Streams (YL, UK), pp. 685–694.
KDDKDD-2015-MitzenmacherPPT #clique #detection #network #scalability
Scalable Large Near-Clique Detection in Large-Scale Networks via Sampling (MM, JP, RP, CET, SCX), pp. 815–824.
KDDKDD-2015-RiondatoU #mining
Mining Frequent Itemsets through Progressive Sampling with Rademacher Averages (MR, EU), pp. 1005–1014.
KDDKDD-2015-RiondatoU15a #algorithm #learning #statistics
VC-Dimension and Rademacher Averages: From Statistical Learning Theory to Sampling Algorithms (MR, EU), pp. 2321–2322.
SEKESEKE-2015-GaoKN #set
Combining Feature Subset Selection and Data Sampling for Coping with Highly Imbalanced Software Data (KG, TMK, AN), pp. 439–444.
SIGIRSIGIR-2015-Baeza-Yates #incremental #query
Incremental Sampling of Query Logs (RABY), pp. 1093–1096.
SACSAC-2015-HsiehNKC #approximate #performance #query #recommendation
Efficient approximate thompson sampling for search query recommendation (CCH, JN, TK, JC), pp. 740–746.
SACSAC-2015-SantosOCFTT #named #similarity
Combine-and-conquer: improving the diversity in similarity search through influence sampling (LFDS, WDO, LOC, MRPF, AJMT, CTJ), pp. 994–999.
ESEC-FSEESEC-FSE-2015-BorgesFdP #execution #probability #symbolic computation
Iterative distribution-aware sampling for probabilistic symbolic execution (MB, AF, Md, CSP), pp. 866–877.
SPLCSPLC-2015-ReulingBRLK #effectiveness #generative #product line #testing
Fault-based product-line testing: effective sample generation based on feature-diagram mutation (DR, JB, SR, ML, UK), pp. 131–140.
CASECASE-2014-HoussemanDRP #reduction
Smart dynamic sampling for wafer at risk reduction in semiconductor manufacturing (SH, SDP, GRV, JP), pp. 780–785.
CASECASE-2014-HuiM #assessment #performance #reduction
Performance assessment of virtual metrology in APC applications for the viability of sampling reductions (KH, JM), pp. 750–755.
DACDAC-2014-ChenPCWMYZC #manycore #named #parallel #simulation #thread
DAPs: Dynamic Adjustment and Partial Sampling for Multithreaded/Multicore Simulation (CCC, YCP, CFC, WSW, QM, PCY, WZ, TFC), p. 6.
DATEDATE-2014-HenselK #energy
The energy benefit of level-crossing sampling including the actuator’s energy consumption (BH, KK), pp. 1–4.
DATEDATE-2014-YuSHEAB #estimation #performance #physics
Efficient performance estimation with very small sample size via physical subspace projection and maximum a posteriori estimation (LY, SS, CH, IME, DAA, DSB), pp. 1–6.
PODSPODS-2014-HuQT #independence
Independent range sampling (XH, MQ, YT), pp. 246–255.
SIGMODSIGMOD-2014-LevinK #network #pipes and filters #social #using
Stratified-sampling over social networks using mapreduce (RL, YK), pp. 863–874.
SIGMODSIGMOD-2014-WangKFGKM #framework #performance #query
A sample-and-clean framework for fast and accurate query processing on dirty data (JW, SK, MJF, KG, TK, TM), pp. 469–480.
VLDBVLDB-2014-JayachandranTKN #execution #interactive #query
Combining User Interaction, Speculative Query Execution and Sampling in the DICE System (PJ, KT, NK, AN), pp. 1697–1700.
VLDBVLDB-2014-YanCZ #bound
Error-bounded Sampling for Analytics on Big Sparse Data (YY, LJC, ZZ), pp. 1508–1519.
STOCSTOC-2014-ColeR #complexity
The sample complexity of revenue maximization (RC, TR), pp. 243–252.
ICALPICALP-v1-2014-Ben-SassonRTW #algorithm #proving
Sampling-Based Proofs of Almost-Periodicity Results and Algorithmic Applications (EBS, NRZ, MT, JW), pp. 955–966.
LATALATA-2014-Khanna #random
Matchings, Random Walks, and Sampling (SK), pp. 32–33.
CHICHI-2014-FerreiraFK #visualisation
Sample-oriented task-driven visualizations: allowing users to make better, more confident decisions (NF, DF, ACK), pp. 571–580.
CHICHI-2014-LeivaS #empirical #set
Representatively memorable: sampling the right phrase set to get the text entry experiment right (LAL, GST), pp. 1709–1712.
CHICHI-2014-YueLCSBGSZ #experience #research
Photographing information needs: the role of photos in experience sampling method-style research (ZY, EL, CJC, JS, KB, ZG, NS, G(Z), pp. 1545–1554.
HCIDHM-2014-SugimotoYG #analysis
Analysis of Expert Skills on Handheld Grinding Work for Metallographic Sample (TS, HY, AG), pp. 66–77.
HCIDUXU-DI-2014-MuukkonenHLV #mobile #process
Tracking Mobile Workers’ Daily Activities with the Contextual Activity Sampling System (HM, KH, SL, MV), pp. 289–300.
HCIDUXU-TMT-2014-LaamanenSH #design #process
Tracing Design Work through Contextual Activity Sampling (TKL, PSH, KH), pp. 142–152.
CIKMCIKM-2014-BudaCMK #database #named #performance
VFDS: An Application to Generate Fast Sample Databases (TSB, TC, JM, MK), pp. 2048–2050.
CIKMCIKM-2014-LiZLW #classification #probability
Probabilistic Classifier Chain Inference via Gibbs Sampling (LL, LZ, GL, HW), pp. 1855–1858.
CIKMCIKM-2014-RahmanH #network #strict #using
Sampling Triples from Restricted Networks using MCMC Strategy (MR, MAH), pp. 1519–1528.
CIKMCIKM-2014-ZhangZZLM #comprehension #constraints #matrix
Understanding the Sparsity: Augmented Matrix Factorization with Sampled Constraints on Unobservables (YZ, MZ, YZ, YL, SM), pp. 1189–1198.
ICMLICML-c1-2014-GopalanMM #online #problem
Thompson Sampling for Complex Online Problems (AG, SM, YM), pp. 100–108.
ICMLICML-c2-2014-BachmanFP #approximate
Sample-based approximate regularization (PB, AMF, DP), pp. 1926–1934.
ICMLICML-c2-2014-DaneshmandGSS #algorithm #complexity #network
Estimating Diffusion Network Structures: Recovery Conditions, Sample Complexity & Soft-thresholding Algorithm (HD, MGR, LS, BS), pp. 793–801.
ICMLICML-c2-2014-GrandeWH #learning #performance #process
Sample Efficient Reinforcement Learning with Gaussian Processes (RCG, TJW, JPH), pp. 1332–1340.
ICMLICML-c2-2014-TaylorGP #analysis #approximate #linear #programming
An Analysis of State-Relevance Weights and Sampling Distributions on L1-Regularized Approximate Linear Programming Approximation Accuracy (GT, CG, DP), pp. 451–459.
ICPRICPR-2014-AlmogahedK #game studies #named #using
NEATER: Filtering of Over-sampled Data Using Non-cooperative Game Theory (BAA, IAK), pp. 1371–1376.
ICPRICPR-2014-GeDGC #learning
Background Subtraction with Dynamic Noise Sampling and Complementary Learning (WG, YD, ZG, YC), pp. 2341–2346.
ICPRICPR-2014-MarteauGR #gesture #kernel #performance #recognition
Down-sampling Coupled to Elastic Kernel Machines for Efficient Recognition of Isolated Gestures (PFM, SG, CR), pp. 363–368.
ICPRICPR-2014-NguyenP #linear #multi #using
Tracking Using Multiple Linear Searches and Motion Direction Sampling (TN, TPP), pp. 2191–2196.
ICPRICPR-2014-SandhanC #dataset #hybrid #pattern matching #pattern recognition #recognition
Handling Imbalanced Datasets by Partially Guided Hybrid Sampling for Pattern Recognition (TS, JYC), pp. 1449–1453.
ICPRICPR-2014-YangYH #learning
Diversity-Based Ensemble with Sample Weight Learning (CY, XCY, HWH), pp. 1236–1241.
ICPRICPR-2014-YuCK #distance #recognition #set #using
Pill Recognition Using Imprint Information by Two-Step Sampling Distance Sets (JY, ZC, SiK), pp. 3156–3161.
KDDKDD-2014-AhmedDNK #framework #graph
Graph sample and hold: a framework for big-graph analytics (NKA, NGD, JN, RRK), pp. 1446–1455.
KDDKDD-2014-ChengL #equivalence #parallel #process
Parallel gibbs sampling for hierarchical dirichlet processes via gamma processes equivalence (DC, YL), pp. 562–571.
KDDKDD-2014-Cohen #distance #performance #query
Distance queries from sampled data: accurate and efficient (EC), pp. 681–690.
KDDKDD-2014-CormodeD #big data #tutorial
Sampling for big data: a tutorial (GC, NGD), p. 1975.
KDDKDD-2014-DundarYR #identification #towards
Batch discovery of recurring rare classes toward identifying anomalous samples (MD, HZY, BR), pp. 223–232.
KDDKDD-2014-GaddeAO #graph #learning #using
Active semi-supervised learning using sampling theory for graph signals (AG, AA, AO), pp. 492–501.
KDDKDD-2014-LiARS #complexity #modelling #topic
Reducing the sampling complexity of topic models (AQL, AA, SR, AJS), pp. 891–900.
KDDKDD-2014-PapailiopoulosKB
Provable deterministic leverage score sampling (DSP, ATK, CB), pp. 997–1006.
SEKESEKE-2014-FinlayPC #predict
Synthetic Minority Over-sampling TEchnique (SMOTE) for Predicting Software Build Outcomes (JF, RP, AMC), pp. 546–551.
SEKESEKE-2014-GaoKN #estimation #learning #quality #ranking
Comparing Two Approaches for Adding Feature Ranking to Sampled Ensemble Learning for Software Quality Estimation (KG, TMK, AN), pp. 280–285.
SIGIRSIGIR-2014-Voorhees #metric
The effect of sampling strategy on inferred measures (EMV), pp. 1119–1122.
SACSAC-2014-JunAK #detection #using
DDoS attack detection by using packet sampling and flow features (JHJ, CWA, SHK), pp. 711–712.
SACSAC-2014-KimKYP #network #online #social
Sampling in online social networks (SWK, KNK, SHY, SP), pp. 845–849.
SACSAC-2014-WangEB #evolution #orthogonal
Mirrored orthogonal sampling with pairwise selection in evolution strategies (HW, ME, TB), pp. 154–156.
FSEFSE-2014-FilieriPVG #execution #statistics #symbolic computation
Statistical symbolic execution with informed sampling (AF, CSP, WV, JG), pp. 437–448.
ICSTICST-2014-PapadakisHT #analysis #combinator #interactive #mutation testing #testing
Sampling Program Inputs with Mutation Analysis: Going Beyond Combinatorial Interaction Testing (MP, CH, YLT), pp. 1–10.
CASECASE-2013-ChenHCHW #automation #maintenance #predict
Automatic baseline-sample-selection scheme for baseline predictive maintenance (CFC, YSH, FTC, HCH, SCW), pp. 183–188.
CASECASE-2013-KurzDP #monitoring #reliability
Monitoring virtual metrology reliability in a sampling decision system (DK, CDL, JP), pp. 20–25.
DACDAC-2013-GuCL #estimation #performance #validation
Efficient moment estimation with extremely small sample size via bayesian inference for analog/mixed-signal validation (CG, EC, XL), p. 7.
DATEDATE-2013-LiZCZ #analysis #multi #simulation
Multi-level phase analysis for sampling simulation (JL, WZ, HC, BZ), pp. 649–654.
DATEDATE-2013-ParkCA #energy
Non-speculative double-sampling technique to increase energy-efficiency in a high-performance processor (JP, AC, JAA), pp. 254–257.
DATEDATE-2013-YaoYW #adaptation #analysis #modelling #online #performance
Efficient importance sampling for high-sigma yield analysis with adaptive online surrogate modeling (JY, ZY, YW), pp. 1291–1296.
DRRDRR-2013-LipskyLN #policy
Optimal policy for labeling training samples (LL, DPL, GN).
SIGMODSIGMOD-2013-FanXS #adaptation #monitoring #named #performance #realtime
FAST: differentially private real-time aggregate monitor with filtering and adaptive sampling (LF, LX, VSS), pp. 1065–1068.
SIGMODSIGMOD-2013-ZhangR #case study #scalability #towards
Towards high-throughput gibbs sampling at scale: a study across storage managers (CZ, CR), pp. 397–408.
VLDBVLDB-2013-NirkhiwaleDJ #algebra #estimation
A Sampling Algebra for Aggregate Estimation (SN, AD, CMJ), pp. 1798–1809.
VLDBVLDB-2013-PavanTTW #graph
Counting and Sampling Triangles from a Graph Stream (AP, KT, ST, KLW), pp. 1870–1881.
STOCSTOC-2013-BringmannL
Succinct sampling from discrete distributions (KB, KGL), pp. 775–782.
STOCSTOC-2013-Thorup #independence #set #similarity
Bottom-k and priority sampling, set similarity and subset sums with minimal independence (MT), pp. 371–380.
HCIHCI-AMTE-2013-BorsciMB #assessment #data transformation
A Grounded Procedure for Managing Data and Sample Size of a Home Medical Device Assessment (SB, JLM, JB), pp. 166–175.
ICEISICEIS-J-2013-RudraGA13a #approximate #query
Estimating Sufficient Sample Sizes for Approximate Decision Support Queries (AR, RPG, NRA), pp. 85–99.
ICEISICEIS-v1-2013-RudraGA #approximate #query
Selecting Adequate Samples for Approximate Decision Support Queries (AR, RPG, NRA), pp. 46–55.
CIKMCIKM-2013-FangYZ #graph #scalability
Active exploration: simultaneous sampling and labeling for large graphs (MF, JY, XZ), pp. 829–834.
ICMLICML-c1-2013-ZhuCPZ #algorithm #modelling #performance #topic
Gibbs Max-Margin Topic Models with Fast Sampling Algorithms (JZ, NC, HP, BZ), pp. 124–132.
ICMLICML-c3-2013-Agarwal #algorithm #multi #predict
Selective sampling algorithms for cost-sensitive multiclass prediction (AA), pp. 1220–1228.
ICMLICML-c3-2013-AgrawalG #linear
Thompson Sampling for Contextual Bandits with Linear Payoffs (SA, NG), pp. 127–135.
ICMLICML-c3-2013-Busa-FeketeSCWH #adaptation
Top-k Selection based on Adaptive Sampling of Noisy Preferences (RBF, BS, WC, PW, EH), pp. 1094–1102.
ICMLICML-c3-2013-LattimoreHS #learning
The Sample-Complexity of General Reinforcement Learning (TL, MH, PS), pp. 28–36.
ICMLICML-c3-2013-Meng #random #scalability
Scalable Simple Random Sampling and Stratified Sampling (XM), pp. 531–539.
KDDKDD-2013-ChiangLPY
Inferring distant-time location in low-sampling-rate trajectories (MFC, YHL, WCP, PSY), pp. 1454–1457.
KDDKDD-2013-GetoorM13a #network
Network sampling (LG, AM), p. 1528.
KDDKDD-2013-GuALH #classification #graph
Selective sampling on graphs for classification (QG, CCA, JL, JH), pp. 131–139.
KDDKDD-2013-WangY #learning #query
Querying discriminative and representative samples for batch mode active learning (ZW, JY), pp. 158–166.
RecSysRecSys-2013-SilbermannBR #recommendation
Sample selection for MCMC-based recommender systems (TS, IB, SR), pp. 403–406.
SIGIRSIGIR-2013-ZhangCWY #collaboration #optimisation
Optimizing top-n collaborative filtering via dynamic negative item sampling (WZ, TC, JW, YY), pp. 785–788.
ECOOPECOOP-2013-WuZSGSY #fault #optimisation #statistics
Simple Profile Rectifications Go a Long Way — Statistically Exploring and Alleviating the Effects of Sampling Errors for Program Optimizations (BW, MZ, XS, YG, RS, GY), pp. 654–678.
ESEC-FSEESEC-FSE-2013-RahmanPHD #bias #fault #predict
Sample size vs. bias in defect prediction (FR, DP, IH, PTD), pp. 147–157.
CGOCGO-2013-ChoMHBM #profiling
Instant profiling: Instrumentation sampling for profiling datacenter applications (HKC, TM, REH, DB, SAM), p. 10.
HPCAHPCA-2013-ArdestaniR #manycore #named #performance #using
ESESC: A fast multicore simulator using Time-Based Sampling (EKA, JR), pp. 448–459.
HPDCHPDC-2013-SuAWMWA #dataset #distributed #using
Taming massive distributed datasets: data sampling using bitmap indices (YS, GA, JW, KM, JW, JPA), pp. 13–24.
ISSTAISSTA-2013-Buda #database #generative #testing #using
Generation of test databases using sampling methods (TSB), pp. 366–369.
CASECASE-2012-KurzDP #using
Sampling Decision System in semiconductor manufacturing using Virtual Metrology (DK, CDL, JP), pp. 74–79.
DACDAC-2012-KuoHCKC #design #monte carlo #performance
Efficient trimmed-sample Monte Carlo methodology and yield-aware design flow for analog circuits (CCK, WYH, YHC, JFK, YKC), pp. 1113–1118.
DATEDATE-2012-MaricauJG #analysis #learning #multi #reliability #using
Hierarchical analog circuit reliability analysis using multivariate nonlinear regression and active learning sample selection (EM, DdJ, GGEG), pp. 745–750.
PODSPODS-2012-McGregorPTW #estimation #statistics
Space-efficient estimation of statistics over sub-sampled streams (AM, AP, ST, DPW), pp. 273–282.
SIGMODSIGMOD-2012-QianCJ
Sample-driven schema mapping (LQ, MJC, HVJ), pp. 73–84.
SIGMODSIGMOD-2012-RosnerHGPB #correlation #performance #word
Fast sampling word correlations of high dimensional text data (abstract only) (FR, AH, MG, MP, AB), p. 866.
SIGMODSIGMOD-2012-SarmaLGMH #performance #scalability
Efficient spatial sampling of large geographical tables (ADS, HL, HG, JM, AYH), pp. 193–204.
ITiCSEITiCSE-2012-FletcherG #object-oriented #programming
Sample courseware for introductory OO programming (RF, RG), p. 370.
TACASTACAS-2012-BarbotHP #model checking #statistics
Coupling and Importance Sampling for Statistical Model Checking (BB, SH, CP), pp. 331–346.
PLDIPLDI-2012-GodefroidT #automation #encoding #synthesis
Automated synthesis of symbolic instruction encodings from I/O samples (PG, AT), pp. 441–452.
ICALPICALP-v1-2012-BringmannP #performance
Efficient Sampling Methods for Discrete Distributions (KB, KP), pp. 133–144.
ICALPICALP-v1-2012-LaekhanukitGS #approach #problem
A Rounding by Sampling Approach to the Minimum Size k-Arc Connected Subgraph Problem (BL, SOG, MS), pp. 606–616.
CSCWCSCW-2012-TripathiB #experience #modelling #predict
Predicting creativity in the wild: experience sample and sociometric modeling of teams (PT, WB), pp. 1203–1212.
ICEISICEIS-v1-2012-RudraGA #approximate #performance #query
An Efficient Sampling Scheme for Approximate Processing of Decision Support Queries (AR, RPG, NA), pp. 16–26.
ICEISICEIS-v3-2012-HamadaASO #classification #documentation #taxonomy #using
A Classification Method of Open-ended Questionnaires using Category-based Dictionary from Sampled Documents (KH, MA, MS, HO), pp. 193–198.
CIKMCIKM-2012-BhuiyanMH #interactive #mining
Interactive pattern mining on hidden data: a sampling-based solution (MB, SM, MAH), pp. 95–104.
CIKMCIKM-2012-CaiZ #injection #learning #rank
Variance maximization via noise injection for active sampling in learning to rank (WC, YZ), pp. 1809–1813.
CIKMCIKM-2012-LiangZ #classification #performance
An efficient and simple under-sampling technique for imbalanced time series classification (GL, CZ), pp. 2339–2342.
CIKMCIKM-2012-OhY #framework #named
iSampling: framework for developing sampling methods considering user’s interest (JO, HY), pp. 1667–1671.
ICMLICML-2012-AhnBW #probability
Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring (SA, AKB, MW), p. 230.
ICMLICML-2012-AzarMK #complexity #generative #learning #on the
On the Sample Complexity of Reinforcement Learning with a Generative Model (MGA, RM, BK), p. 222.
ICMLICML-2012-QuadriantoCL #clique #graph #persistent #set
The Most Persistent Soft-Clique in a Set of Sampled Graphs (NQ, CC, CHL), p. 32.
ICMLICML-2012-VaroquauxGT #clustering #correlation #design
Small-sample brain mapping: sparse recovery on spatially correlated designs with randomization and clustering (GV, AG, BT), p. 178.
ICPRICPR-2012-AbouelenienY #analysis #fault #named #performance
SampleBoost: Improving boosting performance by destabilizing weak learners based on weighted error analysis (MA, XY), pp. 585–588.
ICPRICPR-2012-Filip
Restoring illumination and view dependent data from sparse samples (JF), pp. 1391–1394.
ICPRICPR-2012-HanWHBR #generative #graph #probability
Sampling graphs from a probabilistic generative model (LH, RCW, ERH, LB, PR), pp. 1643–1646.
ICPRICPR-2012-HuangH #invariant #using
Binary invariant cross color descriptor using galaxy sampling (GHH, CRH), pp. 2610–2613.
ICPRICPR-2012-LiL12a #image #invariant #segmentation
Scale-invariant sampling for supervised image segmentation (YL, ML), pp. 1399–1402.
ICPRICPR-2012-LiuCZZ
Fusion of low-and high-dimensional approaches by trackers sampling for generic human motion tracking (YL, JC, HZ, HZ), pp. 898–901.
ICPRICPR-2012-LoWCC #3d #estimation #locality #people
Acceleration of vanishing point-based line sampling scheme for people localization and height estimation via 3D line sampling (KHL, CJW, JHC, HTC), pp. 2788–2791.
ICPRICPR-2012-RaymondMOH #markov #network
Map matching with Hidden Markov Model on sampled road network (RR, TM, TO, NH), pp. 2242–2245.
ICPRICPR-2012-RiechertZMK #estimation #hybrid #realtime #recursion #using
Real-time disparity estimation using line-wise hybrid recursive matching and cross-bilateral median up-sampling (CR, FZ, MM, PK), pp. 3168–3171.
ICPRICPR-2012-TianC12a #image #optimisation #using
Depth image up-sampling using ant colony optimization (JT, LC), pp. 3795–3798.
KDDKDD-2012-BellareIPR
Active sampling for entity matching (KB, SI, AGP, VR), pp. 1131–1139.
KDDKDD-2012-BoleyMG #linear #using
Linear space direct pattern sampling using coupling from the past (MB, SM, TG), pp. 69–77.
KDDKDD-2012-ChattopadhyayWFDPY #probability
Batch mode active sampling based on marginal probability distribution matching (RC, ZW, WF, ID, SP, JY), pp. 741–749.
KDDKDD-2012-DasguptaKS #social
Social sampling (AD, RK, DS), pp. 235–243.
KDDKDD-2012-LiZ
Sampling minimal frequent boolean (DNF) patterns (GL, MJZ), pp. 87–95.
OOPSLAOOPSLA-2012-BaoZZ #nondeterminism #program analysis
White box sampling in uncertain data processing enabled by program analysis (TB, YZ, XZ), pp. 897–914.
SACSAC-2012-ParkDNDKK #identification #memory management #named
HotDataTrap: a sampling-based hot data identification scheme for flash memory (DP, BKD, YN, DHCD, YK, YK), pp. 1610–1617.
SACSAC-2012-ZanioliFC #information management #named #static analysis
SAILS: static analysis of information leakage with sample (MZ, PF, AC), pp. 1308–1313.
LCTESLCTES-2012-ZuluagaKMP #design #predict
“Smart” design space sampling to predict Pareto-optimal solutions (MZ, AK, PAM, MP), pp. 119–128.
CAVCAV-2012-JegourelLS #model checking #optimisation #parametricity #statistics
Cross-Entropy Optimisation of Importance Sampling Parameters for Statistical Model Checking (CJ, AL, SS), pp. 327–342.
CAVCAV-2012-ZutshiST #abstraction #relational
Timed Relational Abstractions for Sampled Data Control Systems (AZ, SS, AT), pp. 343–361.
ISSTAISSTA-2012-ZhaiXCT #approach #named #parallel #thread
CARISMA: a context-sensitive approach to race-condition sample-instance selection for multithreaded applications (KZ, BX, WKC, THT), pp. 221–231.
LICSLICS-2012-KomuravelliPC #learning #probability
Learning Probabilistic Systems from Tree Samples (AK, CSP, EMC), pp. 441–450.
DACDAC-2011-DongL #performance #predict
Efficient SRAM failure rate prediction via Gibbs sampling (CD, XL), pp. 200–205.
DATEDATE-2011-LuJTL #architecture #parametricity #performance #simulation
Efficient parameter variation sampling for architecture simulations (FL, RJ, GT, SL), pp. 1578–1583.
DATEDATE-2011-RoyBC
Waste-aware dilution and mixing of biochemical samples with digital microfluidic biochips (SR, BBB, KC), pp. 1059–1064.
ICDARICDAR-2011-LouradourK #categorisation #documentation #feature model #image #performance
Sample-Dependent Feature Selection for Faster Document Image Categorization (JL, CK), pp. 309–313.
ICDARICDAR-2011-WanLZ #online #recognition
On-line Chinese Character Recognition System for Overlapping Samples (XW, CL, YZ), pp. 799–803.
PODSPODS-2011-CohenK #using
Get the most out of your sample: optimal unbiased estimators using partial information (EC, HK), pp. 13–24.
SIGMODSIGMOD-2011-HuangWYL #algorithm #network
Sampling based algorithms for quantile computation in sensor networks (ZH, LW, KY, YL), pp. 745–756.
SIGMODSIGMOD-2011-ZhangZD #corpus #estimation #mining #performance
Mining a search engine’s corpus: efficient yet unbiased sampling and aggregate estimation (MZ, NZ, GD), pp. 793–804.
VLDBVLDB-2011-CohenCD #flexibility #summary
Structure-Aware Sampling: Flexible and Accurate Summarization (EC, GC, NGD), pp. 819–830.
ICPCICPC-2011-PanchenkoKPZ #database #precise #query #scalability #source code #using
Precise and Scalable Querying of Syntactical Source Code Patterns Using Sample Code Snippets and a Database (OP, JK, HP, AZ), pp. 41–50.
ICPCICPC-2011-PirzadehSHM #concept #execution
The Concept of Stratified Sampling of Execution Traces (HP, SS, AHL, AM), pp. 225–226.
STOCSTOC-2011-ValiantV
Estimating the unseen: an n/log(n)-sample estimator for entropy and support size, shown optimal via new CLTs (GV, PV), pp. 685–694.
ICALPICALP-v1-2011-ChakrabortyGM #performance
Efficient Sample Extractors for Juntas with Applications (SC, DGS, AM), pp. 545–556.
FMFM-2011-BonakdarpourNF #runtime #verification
Sampling-Based Runtime Verification (BB, SN, SF), pp. 88–102.
CHICHI-2011-Miller #architecture
Item sampling for information architecture (CSM), pp. 2211–2214.
CHICHI-2011-PaekH #set
Sampling representative phrase sets for text entry experiments: a procedure and public resource (TP, BJPH), pp. 2477–2480.
CSCWCSCW-2011-FetterG #experience #named
PRIMIExperience: experience sampling via instant messaging (MF, TG), pp. 629–632.
HCIDUXU-v1-2011-SeoLL #development #experience #mobile
An Experience Sampling System for Context-Aware Mobile Application Development (JS, SL, GL), pp. 648–657.
CIKMCIKM-2011-BrauerRMB #information management #regular expression
Enabling information extraction by inference of regular expressions from sample entities (FB, RR, AM, WMB), pp. 1285–1294.
CIKMCIKM-2011-LinC #data fusion #learning #query
Query sampling for learning data fusion (TCL, PJC), pp. 141–146.
ICMLICML-2011-DauphinGB #learning #re-engineering #scalability
Large-Scale Learning of Embeddings with Reconstruction Sampling (YD, XG, YB), pp. 945–952.
ICMLICML-2011-GermainLLMS #approach #kernel
A PAC-Bayes Sample-compression Approach to Kernel Methods (PG, AL, FL, MM, SS), pp. 297–304.
ICMLICML-2011-GhavamzadehLMH #analysis
Finite-Sample Analysis of Lasso-TD (MG, AL, RM, MWH), pp. 1177–1184.
ICMLICML-2011-OrabonaC #algorithm
Better Algorithms for Selective Sampling (FO, NCB), pp. 433–440.
ICMLICML-2011-Reyzin #predict
Boosting on a Budget: Sampling for Feature-Efficient Prediction (LR), pp. 529–536.
ICMLICML-2011-WickRBCM #graph #named
SampleRank: Training Factor Graphs with Atomic Gradients (MLW, KR, KB, AC, AM), pp. 777–784.
KDDKDD-2011-BoleyLPG #performance #random
Direct local pattern sampling by efficient two-step random procedures (MB, CL, DP, TG), pp. 582–590.
KDDKDD-2011-ChenDWSNL #analysis #process
Activity analysis based on low sample rate smart meters (FC, JD, BW, SS, MRN, CTL), pp. 240–248.
KDDKDD-2011-DalviKMR #nearest neighbour #using
Sampling hidden objects using nearest-neighbor oracles (NND, RK, AM, VR), pp. 1325–1333.
KDDKDD-2011-KongFY #classification #graph
Dual active feature and sample selection for graph classification (XK, WF, PSY), pp. 654–662.
KDDKDD-2011-MaiyaB #bias #network #towards
Benefits of bias: towards better characterization of network sampling (ASM, TYBW), pp. 105–113.
KDIRKDIR-2011-PrachuabsupakijS #classification #multi #set #using
Multi-class Data Classification for Imbalanced Data Set using Combined Sampling Approaches (WP, NS), pp. 166–171.
KMISKMIS-2011-AndresLLC #assessment
Solvency Assessment in an Unbalanced Sample (JdA, PL, FSL, FJdCJ), pp. 283–286.
MLDMMLDM-2011-Kessl #mining #parallel #using
Static Load Balancing of Parallel Mining of Frequent Itemsets Using Reservoir Sampling (RK), pp. 553–567.
SEKESEKE-2011-RadulovicG #modelling #quality #semantics
Extending Software Quality Models — A Sample In The Domain of Semantic Technologies (FR, RGC), pp. 25–30.
SIGIRSIGIR-2011-HoobinPZ #corpus
Sample selection for dictionary-based corpus compression (CH, SJP, JZ), pp. 1137–1138.
ESEC-FSEESEC-FSE-2011-CostacheKK #design #implementation #process #validation
Design and validation of feature-based process model tailoring: a sample implementation of PDE (DC, GK, MK), pp. 464–467.
LCTESLCTES-2011-ThomasFK #execution #monitoring
Lowering overhead in sampling-based execution monitoring and tracing (JJT, SF, DK), pp. 101–110.
CASECASE-2010-YuanZ #video
Micro-mixing of highly viscous bio-samples observed with a miniature wireless video sensor (LY, YFZ), pp. 180–185.
DACDAC-2010-SingheeC
Pareto sampling: choosing the right weights by derivative pursuit (AS, PC), pp. 913–916.
DATEDATE-2010-JaffariA10a #analysis #correlation #performance #variability
Correlation controlled sampling for efficient variability analysis of analog circuits (JJ, MA), pp. 1305–1308.
DATEDATE-2010-QaziTDSC #analysis #performance #reduction
Loop flattening & spherical sampling: Highly efficient model reduction techniques for SRAM yield analysis (MQ, MT, LD, DS, AC), pp. 801–806.
DATEDATE-2010-VillenaS #named #order #reduction
HORUS — high-dimensional Model Order Reduction via low moment-matching upgraded sampling (JFV, LMS), pp. 465–470.
PODSPODS-2010-CormodeMYZ #distributed
Optimal sampling from distributed streams (GC, SM, KY, QZ), pp. 77–86.
SIGMODSIGMOD-2010-KohlerZSST
Sampling dirty data for matching attributes (HK, XZ, SWS, YS, KLT), pp. 63–74.
SIGMODSIGMOD-2010-WuOT #multi #online #query
Continuous sampling for online aggregation over multiple queries (SW, BCO, KLT), pp. 651–662.
VLDBVLDB-2010-BeskalesIG #constraints #dependence #functional
Sampling the Repairs of Functional Dependency Violations under Hard Constraints (GB, IFI, LG), pp. 197–207.
ICSMEICSM-2010-ZhangW #quality
Sampling program quality (HZ, RW), pp. 1–10.
PASTEPASTE-2010-NarayanappaBR
Property-aware program sampling (HN, MSB, HR), pp. 45–52.
STOCSTOC-2010-Varadarajan #geometry #set
Weighted geometric set cover via quasi-uniform sampling (KRV), pp. 641–648.
CIKMCIKM-2010-BennettC #classification #online
Online stratified sampling: evaluating classifiers at web-scale (PNB, VRC), pp. 1581–1584.
CIKMCIKM-2010-KaliciakSWP #hybrid #image #novel #retrieval
Novel local features with hybrid sampling technique for image retrieval (LK, DS, NW, JP), pp. 1557–1560.
ICMLICML-2010-LazaricGM10a #analysis
Finite-Sample Analysis of LSTD (AL, MG, RM), pp. 615–622.
ICPRICPR-2010-ChakrabortyG #recognition #speech
Role of Synthetically Generated Samples on Speech Recognition in a Resource-Scarce Language (RC, UG), pp. 1618–1621.
ICPRICPR-2010-LeeCL #algorithm #data-driven #graph #markov #monte carlo #using
A Graph Matching Algorithm Using Data-Driven Markov Chain Monte Carlo Sampling (JL, MC, KML), pp. 2816–2819.
ICPRICPR-2010-RenHHLC #detection
A Sample Pre-mapping Method Enhancing Boosting for Object Detection (HR, XH, CKH, LL, XC), pp. 3005–3008.
ICPRICPR-2010-ShangDJ #image
Compressive Sampling Recovery for Natural Images (FS, HD, YJ), pp. 2206–2209.
ICPRICPR-2010-Strand
Interpolation and Sampling on a Honeycomb Lattice (RS), pp. 2222–2225.
ICPRICPR-2010-Strand10a #3d #grid #re-engineering
Sampling and Ideal Reconstruction on the 3D Diamond Grid (RS), pp. 4609–4612.
ICPRICPR-2010-TakalaCP #clustering #network #sequence
Boosting Clusters of Samples for Sequence Matching in Camera Networks (VT, YC, MP), pp. 400–403.
ICPRICPR-2010-TanakaIKM #fault #kernel
A Relationship Between Generalization Error and Training Samples in Kernel Regressors (AT, HI, MK, MM), pp. 1421–1424.
ICPRICPR-2010-ZhangW10a #modelling #random #validation
LLN-based Model-Driven Validation of Data Points for Random Sample Consensus Methods (LZ, DW), pp. 3436–3439.
OOPSLAOOPSLA-2010-JinTLL #concurrent #debugging
Instrumentation and sampling strategies for cooperative concurrency bug isolation (GJ, AVT, BL, SL), pp. 241–255.
SACSAC-2010-AquinoOW #algorithm #network
A wavelet-based sampling algorithm for wireless sensor networks applications (ALLdA, RARO, EFW), pp. 1604–1608.
SACSAC-2010-FernandesLR #classification #random
The impact of random samples in ensemble classifiers (PF, LL, DDAR), pp. 1002–1009.
SACSAC-2010-MahrsiPHR
Spatiotemporal sampling for trajectory streams (MKEM, CP, GH, FR), pp. 1627–1628.
CGOCGO-2010-ChenVHLRYCZ #compilation #hardware
Taming hardware event samples for FDO compilation (DC, NV, RH, SwL, VR, PY, WC, WZ), pp. 42–52.
LCTESLCTES-2010-FischmeisterB #execution #monitoring
Sampling-based program execution monitoring (SF, YB), pp. 133–142.
ICLPICLP-2010-Fierens10 #logic #modelling #performance #probability
Improving the Efficiency of Gibbs Sampling for Probabilistic Logical Models by Means of Program Specialization (DF), pp. 74–83.
ICSTICST-2010-YanCZZZ #clustering #execution
A Dynamic Test Cluster Sampling Strategy by Leveraging Execution Spectra Information (SY, ZC, ZZ, CZ, YZ), pp. 147–154.
CASECASE-2009-BorgstromJBSK
Field-tests of a redundantly actuated cable-driven robot for environmental sampling applications (PHB, BLJ, MAB, GSS, WJK), pp. 615–620.
CASECASE-2009-ChenHB
Optimum sampling for track PEB CD Integrated Metrology (AC, SH, JB), pp. 439–442.
CASECASE-2009-YuanZ #using
Micro-mixing of highly viscous bio-samples using orbital shaking (LY, YFZ), pp. 579–584.
DACDAC-2009-VeetilSBSR #analysis #dependence #performance
Efficient smart sampling based full-chip leakage analysis for intra-die variation considering state dependence (VV, DS, DB, SS, SR), pp. 154–159.
DACDAC-2009-VillenaS #automation #modelling #multi #named
ARMS — automatic residue-minimization based sampling for multi-point modeling techniques (JFV, LMS), pp. 951–956.
ICDARICDAR-2009-GalballyFMO #evaluation #using #verification
Evaluation of Brute-force Attack to Dynamic Signature Verification Using Synthetic Samples (JG, JF, MMD, JOG), pp. 131–135.
ICDARICDAR-2009-GalballyFMO09a
Improving the Enrollment in Dynamic Signature Verfication with Synthetic Samples (JG, JF, MMD, JOG), pp. 1295–1299.
ICDARICDAR-2009-LeungL #generative #recognition
Recognition of Handwritten Chinese Characters by Combining Regularization, Fisher’s Discriminant and Distorted Sample Generation (KCL, CHL), pp. 1026–1030.
PODSPODS-2009-BravermanOZ
Optimal sampling from sliding windows (VB, RO, CZ), pp. 147–156.
VLDBVLDB-2009-CohenKS #coordination #multi
Coordinated Weighted Sampling for Estimating Aggregates Over Multiple Weight Assignments (EC, HK, SS), pp. 646–657.
VLDBVLDB-2009-HasanZ #graph
Output Space Sampling for Graph Patterns (MAH, MJZ), pp. 730–741.
TACASTACAS-2009-MateescuW #adaptation
Hierarchical Adaptive State Space Caching Based on Level Sampling (RM, AW), pp. 215–229.
MSRMSR-2009-Mockus #scalability #source code #towards #version control
Amassing and indexing a large sample of version control systems: Towards the census of public source code history (AM), pp. 11–20.
PLDIPLDI-2009-MarinoMN #concurrent #detection #effectiveness #lightweight #named
LiteRace: effective sampling for lightweight data-race detection (DM, MM, SN), pp. 134–143.
ICALPICALP-v1-2009-AndoniIOR
External Sampling (AA, PI, KO, RR), pp. 83–94.
HCIHCI-NT-2009-Lew #experience #what
What Do Users Really Do? Experience Sampling in the 21st Century (GSL), pp. 314–319.
CIKMCIKM-2009-ZhangXSYD #evaluation #learning #named
ROSE: retail outlet site evaluation by learning with both sample and feature preference (BZ, MX, JYS, WJY, JD), pp. 1397–1404.
ECIRECIR-2009-DonmezC #learning #optimisation #rank
Active Sampling for Rank Learning via Optimizing the Area under the ROC Curve (PD, JGC), pp. 78–89.
ICMLICML-2009-Cesa-BianchiGO #bound #classification #robust
Robust bounds for classification via selective sampling (NCB, CG, FO), pp. 121–128.
ICMLICML-2009-Doshi-VelezG #process
Accelerated sampling for the Indian Buffet Process (FDV, ZG), pp. 273–280.
ICMLICML-2009-KumarMT #approximate #composition #on the
On sampling-based approximate spectral decomposition (SK, MM, AT), pp. 553–560.
ICMLICML-2009-SindhwaniML #design #nondeterminism
Uncertainty sampling and transductive experimental design for active dual supervision (VS, PM, RDL), pp. 953–960.
ICMLICML-2009-ZhouSL #learning #multi
Multi-instance learning by treating instances as non-I.I.D. samples (ZHZ, YYS, YFL), pp. 1249–1256.
KDDKDD-2009-DonmezCS #learning
Efficiently learning the accuracy of labeling sources for selective sampling (PD, JGC, JGS), pp. 259–268.
KDDKDD-2009-Last #data mining #mining
Improving data mining utility with projective sampling (ML), pp. 487–496.
MLDMMLDM-2009-SakaiI #clustering #performance #random
Fast Spectral Clustering with Random Projection and Sampling (TS, AI), pp. 372–384.
SIGIRSIGIR-2009-CormackC #email #on the
On the relative age of spam and ham training samples for email filtering (GVC, JMMdC), pp. 744–745.
SIGIRSIGIR-2009-SmuckerAC #evaluation #information retrieval #statistics #testing
Agreement among statistical significance tests for information retrieval evaluation at varying sample sizes (MDS, JA, BC), pp. 630–631.
SIGIRSIGIR-2009-ThomasS #named
SUSHI: scoring scaled samples for server selection (PT, MS), pp. 419–426.
SIGIRSIGIR-2009-YangWGH #learning #query #ranking #web
Query sampling for ranking learning in web search (LY, LW, BG, XSH), pp. 754–755.
SACSAC-2009-JesiMNS #approach
Prestige-based peer sampling service: interdisciplinary approach to secure gossip (GPJ, EM, SKN, MvS), pp. 1209–1213.
SACSAC-2009-WangCH #learning #multi #music #retrieval
Music retrieval based on a multi-samples selection strategy for support vector machine active learning (TW, GC, PH), pp. 1750–1751.
ASEASE-2008-DwyerDE #cost analysis #monitoring
Reducing the Cost of Path Property Monitoring Through Sampling (MBD, MD, SGE), pp. 228–237.
ASEASE-2008-Yom-TovTUH #automation #concurrent #debugging #random #source code
Automatic Debugging of Concurrent Programs through Active Sampling of Low Dimensional Random Projections (EYT, RT, SU, SH), pp. 307–316.
CASECASE-2008-BrantleyLCC #design #empirical #optimisation #probability
Optimal sampling in design of experiment for simulation-based stochastic optimization (MWB, LHL, CHC, AC), pp. 388–393.
CASECASE-2008-HooverMR #3d #detection #image #matrix #using
Pose detection of 3-D objects using images sampled on SO(3), spherical harmonics, and wigner-D matrices (RCH, AAM, RGR), pp. 47–52.
CASECASE-2008-YuanZC #automation
Automatic mixing of highly viscous bio-samples (LY, YFZ, WC), pp. 900–905.
SIGMODSIGMOD-2008-GemullaL #bound
Sampling time-based sliding windows in bounded space (RG, WL), pp. 379–392.
SIGMODSIGMOD-2008-LiHYLS #framework #statistics
Sampling cube: a framework for statistical olap over sampling data (XL, JH, ZY, JGL, YS), pp. 779–790.
SIGMODSIGMOD-2008-PanZW #clustering #composition #dataset #matrix #named #performance #scalability
CRD: fast co-clustering on large datasets utilizing sampling-based matrix decomposition (FP, XZ, WW), pp. 173–184.
VLDBVLDB-2008-Bar-YossefG #mining #query
Mining search engine query logs via suggestion sampling (ZBY, MG), pp. 54–65.
VLDBVLDB-2008-HadjieleftheriouYKS #query #set #similarity
Hashed samples: selectivity estimators for set similarity selection queries (MH, XY, NK, DS), pp. 201–212.
VLDBVLDB-2008-NathG #maintenance #online #random #scalability
Online maintenance of very large random samples on flash storage (SN, PBG), pp. 970–983.
CHICHI-2008-KapoorH #case study #comparative #experience #modelling #predict
Experience sampling for building predictive user models: a comparative study (AK, EH), pp. 657–666.
ICMLICML-2008-BowlingJBS #evaluation #game studies
Strategy evaluation in extensive games with importance sampling (MHB, MJ, NB, DS), pp. 72–79.
ICMLICML-2008-DasguptaH #learning
Hierarchical sampling for active learning (SD, DH), pp. 208–215.
ICMLICML-2008-DonmezC #learning #optimisation #rank #reduction
Optimizing estimated loss reduction for active sampling in rank learning (PD, JGC), pp. 248–255.
ICMLICML-2008-GaelSTG #infinity #markov
Beam sampling for the infinite hidden Markov model (JVG, YS, YWT, ZG), pp. 1088–1095.
ICMLICML-2008-LazaricRB #learning
Transfer of samples in batch reinforcement learning (AL, MR, AB), pp. 544–551.
ICMLICML-2008-SilverSM #learning
Sample-based learning and search with permanent and transient memories (DS, RSS, MM), pp. 968–975.
ICPRICPR-2008-KuksaHP #detection #kernel #performance
Fast protein homology and fold detection with sparse spatial sample kernels (PPK, PHH, VP), pp. 1–4.
ICPRICPR-2008-LachezeB #image #locality #using
Image sampling for localization using entropy (LL, RB), pp. 1–4.
ICPRICPR-2008-LiuLLJT #graph #probability #representation
Layered shape matching and registration: Stochastic sampling with hierarchical graph representation (XL, LL, HL, HJ, WT), pp. 1–4.
ICPRICPR-2008-SekiH #3d #multi #realtime #using
Real-time 3D tracking using multiple sample points (AS, HH), pp. 1–4.
KDDKDD-2008-ChenW #classification #feature model #metric #named #performance #problem
FAST: a roc-based feature selection metric for small samples and imbalanced data classification problems (XwC, MW), pp. 124–132.
KDDKDD-2008-PorteousNIASW #performance
Fast collapsed gibbs sampling for latent dirichlet allocation (IP, DN, ATI, AUA, PS, MW), pp. 569–577.
KDDKDD-2008-YuJ #kernel #using
Training structural svms with kernels using sampled cuts (CNJY, TJ), pp. 794–802.
SIGIRSIGIR-2008-Thomas #multi
Generalising multiple capture-recapture to non-uniform sample sizes (PT), pp. 839–840.
SIGIRSIGIR-2008-YilmazKA #performance
A simple and efficient sampling method for estimating AP and NDCG (EY, EK, JAA), pp. 603–610.
SPLCSPLC-2008-CzarneckiSW #feature model #modelling
Sample Spaces and Feature Models: There and Back Again (KC, SS, AW), pp. 22–31.
ISMMISMM-2008-ZhongC #approximate #locality
Sampling-based program locality approximation (YZ, WC), pp. 91–100.
ICSTICST-2008-KodreZR #approach #statistics #testing
Statistical Sampling Based Approach to Alleviate Log Replay Testing (RK, HZ, DJR), pp. 533–536.
ISSTAISSTA-2008-QuCR #empirical #testing
Configuration-aware regression testing: an empirical study of sampling and prioritization (XQ, MBC, GR), pp. 75–86.
ASEASE-2007-YuanCM #array #automation #sequence #testing #user interface
Covering array sampling of input event sequences for automated gui testing (XY, MBC, AMM), pp. 405–408.
CASECASE-2007-LiW #analysis #approach #reliability
An Importance Sampling Based Approach for Reliability Analysis (FL, TW), pp. 956–961.
CASECASE-2007-YuanZCC #automation #using
Automatic Mixing of Bio-Samples Using Micro-Channel and Centrifugation (LY, YFZ, WC, MC), pp. 634–639.
DATEDATE-2007-ZhuP #algorithm #graph #probability #random
Random sampling of moment graph: a stochastic Krylov-reduction algorithm (ZZ, JRP), pp. 1502–1507.
ICDARICDAR-2007-Martinez-DiazFFO #on the #verification
On The Effects of Sampling Rate and Interpolation in HMM-Based Dynamic Signature Verification (MMD, JFA, MRF, JOG), pp. 1113–1117.
PODSPODS-2007-GemullaLH #evolution #maintenance #multi
Maintaining bernoulli samples over evolving multisets (RG, WL, PJH), pp. 93–102.
SIGMODSIGMOD-2007-DasguptaDM #approach #database #random
A random walk approach to sampling hidden databases (AD, GD, HM), pp. 629–640.
SIGMODSIGMOD-2007-LarsonLZZ #assurance #estimation #quality #using
Cardinality estimation using sample views with quality assurance (PÅL, WL, JZ, PZ), pp. 175–186.
SIGMODSIGMOD-2007-LarsonLZZ07a #estimation #self
Exploiting self-monitoring sample views for cardinality estimation (PÅL, WL, JZ, PZ), pp. 1073–1075.
FoSSaCSFoSSaCS-2007-AbdullaKY #automaton
Sampled Universality of Timed Automata (PAA, PK, WY), pp. 2–16.
TACASTACAS-2007-PandyaKL #abstraction #logic #on the
On Sampling Abstraction of Continuous Time Logic with Durations (PKP, SNK, KL), pp. 246–260.
STOCSTOC-2007-DeshpandeV #approximate #reduction
Sampling-based dimension reduction for subspace approximation (AD, KRV), pp. 641–650.
STOCSTOC-2007-NissimRS #data analysis
Smooth sensitivity and sampling in private data analysis (KN, SR, AS), pp. 75–84.
ICALPICALP-2007-BlomerN
Sampling Methods for Shortest Vectors, Closest Vectors and Successive Minima (JB, SN), pp. 65–77.
ICALPICALP-2007-IshaiMSW #approximate #multi
Private Multiparty Sampling and Approximation of Vector Combinations (YI, TM, MJS, RNW), pp. 243–254.
ICALPICALP-2007-MotwaniPX
Estimating Sum by Weighted Sampling (RM, RP, YX), pp. 53–64.
HCIHCI-IDU-2007-Berkman #approach #interactive #testing #usability
General Interaction Expertise: An Approach for Sampling in Usability Testing of Consumer Products (AEB), pp. 397–406.
ICMLICML-2007-ChenJ #classification #feature model #set
Minimum reference set based feature selection for small sample classifications (XwC, JCJ), pp. 153–160.
ICMLICML-2007-Shah #bound
Sample compression bounds for decision trees (MS), pp. 799–806.
SIGIRSIGIR-2007-RowlandsHS #enterprise #evaluation
Workload sampling for enterprise search evaluation (TR, DH, RSS), pp. 887–888.
SIGIRSIGIR-2007-Soboroff #comparison
A comparison of pooled and sampled relevance judgments (IS), pp. 785–786.
SIGIRSIGIR-2007-ThomasH
Evaluating sampling methods for uncooperative collections (PT, DH), pp. 503–510.
OOPSLAOOPSLA-2007-BuytaertGHAEB #compilation #using
Using hpm-sampling to drive dynamic compilation (DB, AG, MH, MA, LE, KDB), pp. 553–568.
SACSAC-2007-AppelPSTT #clustering
Biased box sampling — a density-biased sampling for clustering (APA, AAP, EPMdS, AJMT, CTJ), pp. 445–446.
SACSAC-2007-SchusterS #documentation #estimation #natural language #query #random #web
NL sampler: random sampling of web documents based on natural language with query hit estimation (DS, AS), pp. 812–818.
SACSAC-2007-ZhangZZMY #algorithm #random #streaming
A priority random sampling algorithm for time-based sliding windows over weighted streaming data (LZ, ZL, YQZ, YM, ZY), pp. 453–456.
ESEC-FSEESEC-FSE-2007-SankaranarayananCJI #constraints #feedback #generative #monte carlo #using
State space exploration using feedback constraint generation and Monte-Carlo sampling (SS, RMC, GJ, FI), pp. 321–330.
DACDAC-2006-KanjJN #analysis #design
Mixture importance sampling and its application to the analysis of SRAM designs in the presence of rare failure events (RK, RVJ, SRN), pp. 69–72.
DATEDATE-2006-MajidzadehS #design #higher-order #novel
Arbitrary design of high order noise transfer function for a novel class of reduced-sample-rate sigma-delta-pipeline ADCs (VM, OS), pp. 138–143.
DATEDATE-2006-YavariSR06a
Double-sampling single-loop sigma-delta modulator topologies for broadband applications (MY, OS, ÁRV), pp. 399–404.
SIGMODSIGMOD-2006-KleinGRL #named #query
Derby/S: a DBMS for sample-based query answering (AK, RG, PR, WL), pp. 757–759.
VLDBVLDB-2006-Aggarwal #evolution #on the
On Biased Reservoir Sampling in the Presence of Stream Evolution (CCA), pp. 607–618.
VLDBVLDB-2006-GemullaLH #dataset #evolution #maintenance
A Dip in the Reservoir: Maintaining Sample Synopses of Evolving Datasets (RG, WL, PJH), pp. 595–606.
STOCSTOC-2006-DubrovI #complexity #on the #performance
On the randomness complexity of efficient sampling (BD, YI), pp. 711–720.
STOCSTOC-2006-FischerRV #adaptation #convergence #performance
Fast convergence to Wardrop equilibria by adaptive sampling methods (SF, HR, BV), pp. 653–662.
STOCSTOC-2006-LeviRS #algorithm #modelling #probability
Provably near-optimal sampling-based algorithms for Stochastic inventory control models (RL, RR, DBS), pp. 739–748.
STOCSTOC-2006-Szegedy
The DLT priority sampling is essentially optimal (MS), pp. 150–158.
CHICHI-2006-IachelloTAHS #experience #privacy #prototype #ubiquitous
Prototyping and sampling experience to evaluate ubiquitous computing privacy in the real world (GI, KNT, GDA, GRH, MMS), pp. 1009–1018.
ICEISICEIS-DISI-2006-LongboZMYY #algorithm #data type #random
Random Sampling Algorithms for Landmark Windows over Data Streams (LZ, ZL, YM, WY, JY), pp. 103–107.
ICMLICML-2006-HertzBW #classification #kernel #learning
Learning a kernel function for classification with small training samples (TH, ABH, DW), pp. 401–408.
ICMLICML-2006-VeeramachaneniOA #detection
Active sampling for detecting irrelevant features (SV, EO, PA), pp. 961–968.
ICPRICPR-v1-2006-HuaWCW #algorithm
A Pixel-wise Object Tracking Algorithm with Target and Background Sample (CH, HW, QC, TW), pp. 739–742.
ICPRICPR-v1-2006-IshiyamaIS
A Compact Model of Human Postures Extracting Common Motion from Individual Samples (RI, HI, SS), pp. 187–190.
ICPRICPR-v1-2006-ShanLC #image #recognition #robust
Face Recognition Robust to Head Pose from One Sample Image (TS, BCL, SC), pp. 515–518.
ICPRICPR-v2-2006-ChengW #feedback #image #multi #retrieval
Multi-View Sampling for Relevance Feedback in Image Retrieval (JC, KW), pp. 881–884.
ICPRICPR-v2-2006-FangQ #classification
Car/Non-Car Classification in an Informative Sample Subspace (JF, GQ), pp. 962–965.
ICPRICPR-v2-2006-ZhengYYW #classification #effectiveness #image #problem
Effective classification image space which can solve small sample size problem (YJZ, JYY, JY, XW), pp. 861–864.
ICPRICPR-v3-2006-BalciAF
Subpixel Alignment of MRI Data Under Cartesian and Log-Polar Sampling (MB, MA, HF), pp. 607–610.
ICPRICPR-v3-2006-KierA #classification #multi #predict
Predicting the benefit of sample size extension in multiclass k-NN classification (CK, TA), pp. 332–335.
ICPRICPR-v3-2006-KostlerPRH #adaptation
Adaptive variational sinogram interpolation of sparsely sampled CT data (HK, MP, UR, JH), pp. 778–781.
ICPRICPR-v3-2006-TanakaFKI #theorem
A Theoretical and Experimental Consideration on Interference in Resolutions between Sampling Theorem and OK-Quantization Theory (YT, TF, HK, TI), pp. 869–872.
KDDKDD-2006-AbeZL06a #detection
Outlier detection by sampling with accuracy guarantees (MW, CJ), pp. 767–772.
KDDKDD-2006-FanD #bias #classification #framework #performance #testing
Reverse testing: an efficient framework to select amongst classifiers under sample selection bias (WF, ID), pp. 147–156.
KDDKDD-2006-LeskovecF #graph #scalability
Sampling from large graphs (JL, CF), pp. 631–636.
KDDKDD-2006-YeW #analysis
Regularized discriminant analysis for high dimensional, low sample size data (JY, TW), pp. 454–463.
SIGIRSIGIR-2006-AzzopardiBC #adaptation #distributed #information retrieval
Adaptive query-based sampling for distributed IR (LA, MB, FC), pp. 605–606.
SIGIRSIGIR-2006-CaverleeLB #approach #distributed #query
Distributed query sampling: a quality-conscious approach (JC, LL, JB), pp. 340–347.
OOPSLAOOPSLA-2006-SahavechaphanC #mining #named
XSnippet: mining For sample code (NS, KTC), pp. 413–430.
SACSAC-2006-PechenizkiyPT #feature model #learning #reduction
The impact of sample reduction on PCA-based feature extraction for supervised learning (MP, SP, AT), pp. 553–558.
ICSEICSE-2006-Sindhgatta #information retrieval #source code #using
Using an information retrieval system to retrieve source code samples (RS), pp. 905–908.
CASECASE-2005-MeldrumHFSMRPMCDW #analysis
Sample preparation in glass capillaries for high-throughput biochemical analyses (DRM, MH, CHF, MSS, SKM, TTHR, WHP, SEM, DLC, DAD, PJW), pp. 7–12.
DACDAC-2005-Li #analysis #grid #performance #power management #simulation
Power grid simulation via efficient sampling-based sensitivity analysis and hierarchical symbolic relaxation (PL), pp. 664–669.
DACDAC-2005-Vasudevan #simulation
Simulation of the effects of timing jitter in track-and-hold and sample-and-hold circuits (VV), pp. 397–402.
DATEDATE-2005-MartensG #integration #orthogonal #polynomial #simulation #using
Time-Domain Simulation of Sampled Weakly Nonlinear Systems Using Analytical Integration and Orthogonal Polynomial Series (EM, GGEG), pp. 120–125.
DATEDATE-2005-ScholiveBRFR #generative #testing
Mutation Sampling Technique for the Generation of Structural Test Data (MS, VB, CR, MLF, BR), pp. 1022–1023.
ICDARICDAR-2005-LiwickiB #database #recognition
Enhancing Training Data for Handwriting Recognition of Whiteboard Notes with Samples from a Different Database (ML, HB), pp. 550–554.
SIGMODSIGMOD-2005-JohnsonMR #algorithm
Sampling Algorithms in a Stream Operator (TJ, SM, IR), pp. 1–12.
VLDBVLDB-2005-CormodeMR #data type #mining
Summarizing and Mining Inverse Distributions on Data Streams via Dynamic Inverse Sampling (GC, SM, IR), pp. 25–36.
STOCSTOC-2005-AchlioptasCKM #bias #graph #on the
On the bias of traceroute sampling: or, power-law degree distributions in regular graphs (DA, AC, DK, CM), pp. 694–703.
ICALPICALP-2005-RadhakrishnanRS #fourier #on the #power of #problem #random
On the Power of Random Bases in Fourier Sampling: Hidden Subgroup Problem in the Heisenberg Group (JR, MR, PS), pp. 1399–1411.
CIKMCIKM-2005-AfratiLL #adaptation #query #using #web
Answering aggregation queries on hierarchical web sites using adaptive sampling (FNA, PVL, CL), pp. 237–238.
ICMLICML-2005-LavioletteM #bound #classification
PAC-Bayes risk bounds for sample-compressed Gibbs classifiers (FL, MM), pp. 481–488.
ICMLICML-2005-LeiteB #classification #performance #predict
Predicting relative performance of classifiers from samples (RL, PB), pp. 497–503.
ICMLICML-2005-SinghPGBB #analysis #learning
Active learning for sampling in time-series experiments with application to gene expression analysis (RS, NP, DKG, BB, ZBJ), pp. 832–839.
ICMLICML-2005-SzepesvariM #bound #finite
Finite time bounds for sampling based fitted value iteration (CS, RM), pp. 880–887.
ICMLICML-2005-WangLBS #online #optimisation
Bayesian sparse sampling for on-line reward optimization (TW, DJL, MHB, DS), pp. 956–963.
KDDKDD-2005-Scholz #mining
Sampling-based sequential subgroup mining (MS), pp. 265–274.
KDDKDD-2005-Yu #ranking #retrieval
SVM selective sampling for ranking with application to data retrieval (HY), pp. 354–363.
SIGIRSIGIR-2005-TrieschniggK #approach #detection #scalability #topic
Scalable hierarchical topic detection: exploring a sample based approach (DT, WK), pp. 655–656.
POPLPOPL-2005-ParkPT #probability
A probabilistic language based upon sampling functions (SP, FP, ST), pp. 171–182.
LCTESLCTES-2005-XuL
A sample-based cache mapping scheme (RX, ZL), pp. 166–174.
PPoPPPPoPP-2005-CongHHP #data mining #framework #mining #parallel
A sampling-based framework for parallel data mining (SC, JH, JH, DAP), pp. 255–265.
PODSPODS-2004-HershbergerS #adaptation #data type #geometry #problem
Adaptive Sampling for Geometric Problems over Data Streams (JH, SS), pp. 252–262.
SIGMODSIGMOD-2004-ChaudhuriDS #effectiveness #estimation #statistics
Effective Use of Block-Level Sampling in Statistics Estimation (SC, GD, US), pp. 287–298.
SIGMODSIGMOD-2004-GryzGLZ #database #query
Query Sampling in DB2 Universal Database (JG, JG, LL, CZ), pp. 839–843.
SIGMODSIGMOD-2004-HaasK #database
A Bi-Level Bernoulli Scheme for Database Sampling (PJH, CK), pp. 275–286.
SIGMODSIGMOD-2004-IpeirotisG #database #using
When one Sample is not Enough: Improving Text Database Selection Using Shrinkage (PGI, LG), pp. 767–778.
SIGMODSIGMOD-2004-JermainePA #maintenance #online #random #scalability
Online Maintenance of Very Large Random Samples (CJ, AP, SA), pp. 299–310.
STOCSTOC-2004-GuptaPRS #algorithm #approximate #optimisation #probability
Boosted sampling: approximation algorithms for stochastic optimization (AG, MP, RR, AS), pp. 417–426.
ICALPICALP-2004-CzumajS #approximate #clustering #random
Sublinear-Time Approximation for Clustering Via Random Sampling (AC, CS), pp. 396–407.
ICEISICEIS-v1-2004-CortesO #quality #relational
Relational Sampling for Data Quality Auditing and Decision Support (BC, JNO), pp. 376–382.
ICMLICML-2004-Zadrozny #bias #classification #learning
Learning and evaluating classifiers under sample selection bias (BZ).
ICPRICPR-v1-2004-ChenL #image #invariant #recognition
Illumination and Expression Invariant Face Recognition with One Sample Image (SC, BCL), pp. 300–303.
ICPRICPR-v2-2004-FangQ #detection #learning
Learning Sample Subspace with Application to Face Detection (JF, GQ), pp. 423–426.
ICPRICPR-v2-2004-WashizawaY #classification #kernel #pattern matching #pattern recognition #recognition
Kernel Sample Space Projection Classifier for Pattern Recognition (YW, YY), pp. 435–438.
ICPRICPR-v3-2004-BenoitF #kernel
Steerable Kernels for Arbitrarily-Sampled Spaces (SB, FPF), pp. 578–581.
ICPRICPR-v3-2004-ChenYC #distance #multi #recognition
Inter-Subspace Distance: A New Method for Face Recognition with Multiple Samples (JHC, SLY, CSC), pp. 140–143.
ICPRICPR-v3-2004-JuszczakD
Selective Sampling Based on the Variation in Label Assignments (PJ, RPWD), pp. 375–378.
ICPRICPR-v3-2004-KuhlKWK #classification #using
Training of Classifiers Using Virtual Samples Only (AK, LK, CW, UK), pp. 418–421.
ICPRICPR-v3-2004-SprevakAW #assessment #classification
A Non-Random Data Sampling Method for Classification Model Assessment (DS, FA, HW), pp. 406–409.
ICPRICPR-v4-2004-BradleyL #estimation #using
Sample Size Estimation using the Receiver Operating Characteristic Curve (APB, IDL), pp. 428–431.
ICPRICPR-v4-2004-FrancoLM #approach #feedback
A New Approach for Relevance Feedback Through Positive and Negative Samples (AF, AL, DM), pp. 905–908.
KDDKDD-2004-JiangPRTZ #array #clustering #mining
Mining coherent gene clusters from gene-sample-time microarray data (DJ, JP, MR, CT, AZ), pp. 430–439.
SIGIRSIGIR-2004-FanL #classification #semantics #video
Semantic video classification by integrating unlabeled samples for classifier training (JF, HL), pp. 592–593.
SACSAC-2004-TrutaFB #metric
Disclosure risk measures for the sampling disclosure control method (TMT, FF, DCBJ), pp. 301–306.
ISMMISMM-2004-JumpBM
Dynamic object sampling for pretenuring (MJ, SMB, KSM), pp. 152–162.
DATEDATE-2003-VanasscheGS #analysis #detection #domain model #modelling
Time-Varying, Frequency-Domain Modeling and Analysis of Phase-Locked Loops with Sampling Phase-Frequency Detectors (PV, GGEG, WMCS), pp. 10238–10243.
SIGMODSIGMOD-2003-BabcockCD #approximate #query
Dynamic Sample Selection for Approximate Query Processing (BB, SC, GD), pp. 539–550.
VLDBVLDB-2003-Jermaine #approximate #estimation #robust
Robust Estimation With Sampling and Approximate Pre-Aggregation (CJ), pp. 886–897.
IWPCIWPC-2003-ChanHMY #execution #object-oriented #scalability #visualisation
Scaling an Object-Oriented System Execution Visualizer through Sampling (AC, RH, GCM, ATTY), pp. 237–244.
PLDIPLDI-2003-LiblitAZJ #debugging
Bug isolation via remote program sampling (BL, AA, AXZ, MIJ), pp. 141–154.
STOCSTOC-2003-Bar-Yossef #bound
Sampling lower bounds via information theory (ZBY), pp. 335–344.
STOCSTOC-2003-ItohTT #independence #on the #permutation #strict
On the sample size of k-restricted min-wise independent permutations and other k-wise distributions (TI, YT, JT), pp. 710–719.
ICALPICALP-2003-PoulalhonS
Optimal Coding and Sampling of Triangulations (DP, GS), pp. 1080–1094.
ECIRECIR-2003-XuYTXW #classification #using
Representative Sampling for Text Classification Using Support Vector Machines (ZX, KY, VT, XX, JW), pp. 393–407.
ICMLICML-2003-Strens #optimisation
Evolutionary MCMC Sampling and Optimization in Discrete Spaces (MJAS), pp. 736–743.
KDDKDD-2003-DuMouchelA #design
Applications of sampling and fractional factorial designs to model-free data squashing (WD, DKA), pp. 511–516.
KDDKDD-2003-TsamardinosAS #markov #performance
Time and sample efficient discovery of Markov blankets and direct causal relations (IT, CFA, ARS), pp. 673–678.
CGOCGO-2003-ChenHC #hardware #monitoring #performance #using
Dynamic Trace Selection Using Performance Monitoring Hardware Sampling (HC, WCH, DyC), pp. 79–90.
SOSPSOSP-2003-ManiatisRRBGM
Preserving peer replicas by rate-limited sampled voting (PM, DSHR, MR, MB, TJG, YM), pp. 44–59.
SIGMODSIGMOD-2002-Aggarwal #estimation #framework #nearest neighbour #reduction
Hierarchical subspace sampling: a unified framework for high dimensional data reduction, selectivity estimation and nearest neighbor search (CCA), pp. 452–463.
VLDBVLDB-2002-ChoN #detection #effectiveness #using
Effective Change Detection Using Sampling (JC, AN), pp. 514–525.
VLDBVLDB-2002-IpeirotisG #database #distributed #web
Distributed Search over the Hidden Web: Hierarchical Database Sampling and Selection (PGI, LG), pp. 394–405.
STOCSTOC-2002-AlonVKK #approximate #problem #random
Random sampling and approximation of MAX-CSP problems (NA, WFdlV, RK, MK), pp. 232–239.
STOCSTOC-2002-GilbertGIMS #fourier
Near-optimal sparse fourier representations via sampling (ACG, SG, PI, SM, MS), pp. 152–161.
STOCSTOC-2002-KargerL #graph #random
Random sampling in residual graphs (DRK, MSL), pp. 63–66.
ICALPICALP-2002-DuchonFLS #random
Random Sampling from Boltzmann Principles (PD, PF, GL, GS), pp. 501–513.
CIKMCIKM-2002-NanopoulosMT #algorithm #effectiveness #performance
An efficient and effective algorithm for density biased sampling (AN, YM, YT), pp. 398–404.
ICMLICML-2002-LiuMY #feature model
Feature Selection with Selective Sampling (HL, HM, LY), pp. 395–402.
ICMLICML-2002-StrensBE #markov #monte carlo #optimisation #using
Markov Chain Monte Carlo Sampling using Direct Search Optimization (MJAS, MB, NE), pp. 602–609.
ICPRICPR-v1-2002-YuLC #multi #synthesis
Multiresolution Block Sampling-Based Method for Texture Synthesis (YY, JL, CWC), pp. 239–242.
ICPRICPR-v2-2002-LiZ #algorithm #visual notation
Visual Contour Tracking Based on Sequential Importance Sampling/Resampling Algorithm (PL, TZ), pp. 564–568.
ICPRICPR-v3-2002-HuangLLM #problem
Solving the Small Sample Size Problem of LDA (RH, QL, HL, SM), pp. 29–32.
ICPRICPR-v3-2002-LiY #clustering #normalisation
Normalized Sampling for Color Clustering in Medical Diagnosis (CHL, PCY), pp. 819–822.
KDDKDD-2002-ChenHS #algorithm
A new two-phase sampling based algorithm for discovering association rules (BC, PJH, PS), pp. 462–468.
SIGIRSIGIR-2002-SiC #using
Using sampled data and regression to merge search engine results (LS, JPC), pp. 19–26.
SACSAC-2002-DolinBR #co-evolution #distributed #effectiveness
Co-evolving an effective fitness sample: experiments in symbolic regression and distributed robot control (BD, FHBI, EGR), pp. 553–559.
CAVCAV-2002-YounesS #probability #using #verification
Probabilistic Verification of Discrete Event Systems Using Acceptance Sampling (HLSY, RGS), pp. 223–235.
ICTSSTestCom-2002-Veciana-NoguesCDS #metric #verification
Verifying IP Meters from Sampled Measurements (CVN, ACA, JDP, JSP), pp. 39–54.
ICDARICDAR-2001-BarakatB #classification #geometry
Training with Positive and Negative Data Samples: Effects on a Classifier for Hand-Drawn Geometric Shapes (HB, DB), pp. 1017–1021.
VLDBVLDB-2001-Gibbons #query
Distinct Sampling for Highly-Accurate Answers to Distinct Values Queries and Event Reports (PBG), pp. 541–550.
STOCSTOC-2001-Bar-YossefKS #algorithm #bound
Sampling algorithms: lower bounds and applications (ZBY, RK, DS), pp. 266–275.
ICMLICML-2001-RoyM #estimation #fault #learning #reduction #towards
Toward Optimal Active Learning through Sampling Estimation of Error Reduction (NR, AM), pp. 441–448.
SACSAC-2001-BadgerK #taxonomy
Picking fruit from the tree of life: comments on taxonomic sampling and quartet methods (JHB, PEK), pp. 61–67.
DACDAC-2000-LuJCF #performance #using
Efficient variable ordering using aBDD based sampling (YL, JJ, EMC, MF), pp. 687–692.
DATEDATE-2000-JantschB
Composite Signal Flow: A Computational Model Combining Events, Sampled Streams, and Vectors (AJ, PB), pp. 154–160.
PODSPODS-2000-Lynch #adaptation #analysis
Analysis and Application of Adaptive Sampling (JFL), pp. 260–267.
SIGMODSIGMOD-2000-AcharyaGP #approximate #query
Congressional Samples for Approximate Answering of Group-By Queries (SA, PBG, VP), pp. 487–498.
SIGMODSIGMOD-2000-PalmerF #clustering #data mining #mining
Density Biased Sampling: An Improved Method for Data Mining and Clustering (CRP, CF), pp. 82–92.
SIGMODSIGMOD-2000-WaasG #cost analysis #execution #query
Counting, Enumerating, and Sampling of Execution Plans in a Cost-Based Query Optimizer (FW, CAGL), pp. 499–509.
VLDBVLDB-2000-GantiLR #approximate #named #query #self
ICICLES: Self-Tuning Samples for Approximate Query Answering (VG, MLL, RR), pp. 176–187.
STOCSTOC-2000-Grover #agile #quantum
Rapid sampling though quantum computing (LKG), pp. 618–626.
CIKMCIKM-2000-MakawitaTL #database #using
Sampling from Databases Using B+-Trees (DPM, KLT, HL), pp. 158–164.
ICPRICPR-v2-2000-MitaniH #classification #design #nearest neighbour
Classifier Design Based on the Use of Nearest Neighbor Samples (YM, YH), pp. 2769–2772.
ICPRICPR-v2-2000-MiyamotoHM #classification #design #polynomial
Use of Bootstrap Samples in Quadratic Classifier Design (TM, YH, YM), pp. 2789–2792.
ICPRICPR-v2-2000-Schulerud #analysis #bias #fault #feature model #linear
Bias of Error Rates in Linear Discriminant Analysis Caused by Feature Selection and Sample Size (HS), pp. 2372–2377.
ICPRICPR-v3-2000-ThiemH #design
Biology-Inspired Design of Digital Gabor Filters upon a Hexagonal Sampling Scheme (JT, GH), pp. 3449–3452.
KDDKDD-2000-SchefferW #algorithm
A sequential sampling algorithm for a general class of utility criteria (TS, SW), pp. 330–334.
ASPLOSASPLOS-2000-BurrowsELVWWW #flexibility #performance
Efficient and Flexible Value Sampling (MB, ÚE, STL, MTV, CAW, KW, WEW), pp. 160–167.
ISMMISMM-2000-AgesenG #performance
Efficient Object Sampling via Weak References (OA, AG), pp. 121–126.
ICDARICDAR-1999-KhanH #recognition #using
Recognition of Real-Life Character Samples using a Structural Variation and Degradation Model (NAK, HH), pp. 225–228.
SIGMODSIGMOD-1999-ChaudhuriMN #on the #random
On Random Sampling over Joins (SC, RM, VRN), pp. 263–274.
SIGMODSIGMOD-1999-MankuRL #dataset #online #order #performance #random #scalability #statistics
Random Sampling Techniques for Space Efficient Online Computation of Order Statistics of Large Datasets (GSM, SR, BGL), pp. 251–262.
STOCSTOC-1999-HalesH #fourier #quantum
Quantum Fourier Sampling Simplified (LH, SH), pp. 330–338.
STOCSTOC-1999-PonzioRV #communication #complexity #pointer
The Communication Complexity of Pointer Chasing: Applications of Entropy and Sampling (SP, JR, SV), pp. 602–611.
STOCSTOC-1999-Schaeffer #random #scalability
Random Sampling of Large Planar Maps and Convex Polyhedra (GS), pp. 760–769.
STOCSTOC-1999-Servedio #complexity #learning
Computational Sample Complexity and Attribute-Efficient Learning (RAS), pp. 701–710.
KDDKDD-1999-ProvostJO #performance
Efficient Progressive Sampling (FJP, DJ, TO), pp. 23–32.
MLDMMLDM-1999-ReczkoKMGO #estimation #image #network
Neural Networks in MR Image Estimation from Sparsely Sampled Scans (MR, DAK, VM, DGD, DvO), pp. 75–86.
SIGIRSIGIR-1999-CormackLP #precise #random
Estimating Precision by Random Sampling (poster abstract) (GVC, OL, CRP), pp. 273–274.
SIGMODSIGMOD-1998-ChaudhuriMN #how #question #random
Random Sampling for Histogram Construction: How much is enough? (SC, RM, VRN), pp. 436–447.
SIGMODSIGMOD-1998-GibbonsM #approximate #query #statistics #summary
New Sampling-Based Summary Statistics for Improving Approximate Query Answers (PBG, YM), pp. 331–342.
STOCSTOC-1998-Huber #approximate
Exact Sampling and Approximate Counting Techniques (MH), pp. 31–40.
ICALPICALP-1998-DyerG #algorithm #polynomial
A Genuinely Polynomial-Time Algorithms for Sampling Two-Rowed Contingency Tables (MED, CSG), pp. 339–350.
ICALPICALP-1998-MartinezR
Optimal Sampling Strategies in Quicksort (CM, SR), pp. 327–338.
ICPRICPR-1998-Duin #analysis #problem #relational #scalability
Relational discriminant analysis and its large sample size problem (RPWD), pp. 445–449.
ICPRICPR-1998-MahlmeisterSS #image
Sample-guided progressive image coding (UM, MS, GS), pp. 1257–1259.
ICPRICPR-1998-PetersS #realtime
A real-time variable sampling technique: DIEM (MWP, AS), pp. 316–321.
KDDKDD-1998-OliverRGBBW
Analysing Rock Samples for the Mars Lander (JJO, TR, PG, WLB, RAB, SRW), pp. 299–303.
SACSAC-1998-LiWP #modelling #process #simulation
Modelling and simulation of fractional ARIMA processes based on importance sampling (JSL, AW, RPZ), pp. 453–455.
HPCAHPCA-1998-VengroffG #branch #estimation #performance #predict #re-engineering
Partial Sampling with Reverse State Reconstruction: A New Technique for Branch Predictor Performance Estimation (DEV, GRG), pp. 342–351.
ICDARICDAR-1997-NakagawaHYSHA #database #online #sequence
On-line Handwritten Character Pattern Database Sampled in a Sequence of Sentences without any Writing Instructions (MN, TH, YY, SiS, LH, KA), pp. 376–381.
ICDARICDAR-1997-Yamada #feature model #recognition
Non-uniformly Sampled Feature Extraction Method for Kanji Character Recognition (KY), pp. 200–205.
ITiCSEITiCSE-WGR-1997-JoyceKGKKLLSW #design #guidelines #recommendation #repository
Developing laboratories for the SIGCSE computing laboratory repository: guidelines, recommendations, and sample labs (report of the ITiCSE 1997 working group on designing laboratory materials for computing courses) (DTJ, DK, JGP, EBK, WK, CL, KL, ES, RAW), pp. 1–12.
STOCSTOC-1997-Fill #algorithm #markov
An Interruptible Algorithm for Perfect Sampling via Markov Chains (JAF), pp. 688–695.
STOCSTOC-1997-KannanV
Sampling Lattice Points (RK, SV), pp. 696–700.
STOCSTOC-1997-Karger #graph #random #using
Using Random Sampling to Find Maximum Flows in Uncapacitated Undirected Graphs (DRK), pp. 240–249.
SIGMODSIGMOD-1996-GangulyGMS #estimation
Bifocal Sampling for Skew-Resistant Join Size Estimation (SG, PBG, YM, AS), pp. 271–281.
VLDBVLDB-1996-Toivonen #database #scalability
Sampling Large Databases for Association Rules (HT), pp. 134–145.
STOCSTOC-1996-Zuckerman
Randomness-Optimal Sampling, Extractors, and Constructive Leader Election (DZ), pp. 286–295.
ICALPICALP-1996-HenzingerT #algorithm #graph
Improved Sampling with Applications to Dynamic Graph Algorithms (MRH, MT), pp. 290–299.
ICPRICPR-1996-ChenJ #behaviour #network #on the
On the small sample behavior of the class-sensitive neural network (CHC, AJ), pp. 209–213.
ICPRICPR-1996-GriffinKWM
Techniques for the interpretation of thermal paint coated samples (AG, JK, TW, JM), pp. 959–963.
ICPRICPR-1996-KimK #multi #recognition
Multi-font and multi-size character recognition based on the sampling and quantization of an unwrapped contour (MKK, YBK), pp. 170–174.
ICPRICPR-1996-SkurichinaD #classification
Stabilizing classifiers for very small sample sizes (MS, RPWD), pp. 891–896.
ICPRICPR-1996-Young #analysis #image
Sampling density for image analysis (ITY), pp. 840–843.
KDDKDD-1996-JohnL #data mining #mining
Static Versus Dynamic Sampling for Data Mining (GHJ, PL), pp. 367–370.
SACSAC-1996-Duben #statistics #using
Conformational statistics of the nitrogen linkage in glycopeptides using umbrella sampling (AJD), pp. 38–43.
ICDARICDAR-v1-1995-LoprestiZNS #recognition
Spatial sampling effects in optical character recognition (DPL, JZ, GN, PS), pp. 309–314.
ICDARICDAR-v1-1995-Yamada #recognition
Optimal sampling intervals for Gabor features and printed Japanese character recognition (KY), pp. 150–153.
VLDBVLDB-1995-HaasNSS #estimation
Sampling-Based Estimation of the Number of Distinct Values of an Attribute (PJH, JFN, SS, LS), pp. 311–322.
CIKMCIKM-1995-IkejiF #adaptation #query #using
Computation of Partial Query Results Using An Adaptive Stratified Sampling Technique (ACI, FF), pp. 145–149.
ICMLICML-1995-DaganE #classification #probability
Committee-Based Sampling For Training Probabilistic Classifiers (ID, SPE), pp. 150–157.
ICMLICML-1995-Niyogi #complexity #learning
Free to Choose: Investigating the Sample Complexity of Active Learning of Real Valued Functions (PN), pp. 405–412.
SACSAC-1995-Duben #modelling #statistics #using
Conformational statistics of models for serine- and threonine-linked glycopeptides using umbrella sampling (AJD), pp. 108–112.
DATEEDAC-1994-DumasGLP #effectiveness #fault
Effectiveness of a Variable Sampling Time Strategy for Delay Fault Diagnosis (DD, PG, CL, SP), pp. 518–523.
PODSPODS-1994-HaasNS #estimation #on the
On the Relative Cost of Sampling for Join Selectivity Estimation (PJH, JFN, ANS), pp. 14–24.
PODSPODS-1994-KivinenM #information management #power of
The Power of Sampling in Knowledge Discovery (JK, HM), pp. 77–85.
STOCSTOC-1994-Karger #design #network #problem #random
Random sampling in cut, flow, and network design problems (DRK), pp. 648–657.
ICMLICML-1994-CravenS #network #query #using
Using Sampling and Queries to Extract Rules from Trained Neural Networks (MC, JWS), pp. 37–45.
ICMLICML-1994-LewisC #learning #nondeterminism
Heterogenous Uncertainty Sampling for Supervised Learning (DDL, JC), pp. 148–156.
ICMLICML-1994-Skalak #algorithm #feature model #prototype #random
Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms (DBS), pp. 293–301.
ICMLICML-1994-WeissI
Small Sample Decision tree Pruning (SMW, NI), pp. 335–342.
ICDARICDAR-1993-CaesarGM93a #adaptation #classification #scalability #set
Utilization of large disordered sample sets for classifier adaptation in complex domains (TC, JMG, EM), pp. 790–793.
ICDARICDAR-1993-TakeshitaNK #bias #distance #on the
On the bias of Mahalanobis distance due to limited sample size effect (TT, SN, FK), pp. 171–174.
ICSMECSM-1993-Talbert #assurance #quality
Representative Sampling Within Software Quality Assurance (NBT), pp. 174–179.
STOCSTOC-1993-KollerM #constraints
Constructing small sample spaces satisfying given constraints (DK, NM), pp. 268–277.
CHIINTERCHI-1993-Weiler #tool support #usability
Software for the usability lab: a sampling of current tools (PW), pp. 57–60.
SACSAC-1993-Sabharwal #approach #parametricity
An Intelligent Approach to Discrete Sampling of Parametric Curves (CLS), pp. 397–401.
FSEFSE-1993-PodgurskiY #analysis #clustering #testing
Partition testing, stratified sampling, and cluster analysis (AP, CY), pp. 169–181.
SIGMODSIGMOD-1992-HaasS #estimation #query
Sequential Sampling Procedures for Query Size Estimation (PJH, ANS), pp. 341–350.
VLDBVLDB-1992-Antoshenkov #pseudo #random
Random Sampling from Pseudo-Ranked B+ Trees (GA), pp. 375–382.
STOCSTOC-1992-Ben-David #detection #finite #question
Can Finite Samples Detect Singularities of Real-Valued Functions? (SBD), pp. 390–399.
STOCSTOC-1992-Schulman
Sample Spaces Uniform on Neighborhoods (LJS), pp. 17–25.
ICSEICSE-1992-PodgurskiP #automation #behaviour #component #retrieval #reuse
Behavior Sampling: A Technique for Automated Retrieval of Reusable Components (AP, LP), pp. 349–360.
HTHT-1991-EganLKLRLL #hypermedia #library
Hypertext for the Electronic Library? CORE Sample Results (DEE, ML, RDK, CCL, JRR, MLL, TKL), pp. 299–312.
STOCSTOC-1991-ApplegateK #integration
Sampling and Integration of Near Log-Concave functions (DA, RK), pp. 156–163.
CHICHI-1991-MorchG #concept #named
JANUS: basic concepts and sample dialog (AIM, AG), pp. 457–458.
ISSTATAV-1991-Podgurski #algorithm #reliability
Reliability, Sampling, and Algorithmic Randomness (AP), pp. 11–20.
PODSPODS-1990-LiptonN #adaptation #estimation #query
Query Size Estimation by Adaptive Sampling (RJL, JFN), pp. 40–46.
SIGMODSIGMOD-1990-LiptonNS #adaptation #estimation
Practical Selectivity Estimation through Adaptive Sampling (RJL, JFN, DAS), pp. 1–11.
SIGMODSIGMOD-1990-OlkenRX #random
Random Sampling from Hash Files (FO, DR, PX), pp. 375–386.
STOCSTOC-1990-Vishkin #pattern matching #performance
Deterministic Sampling-A New Technique for Fast Pattern Matching (UV), pp. 170–180.
VLDBVLDB-1989-OlkenR #random
Random Sampling from B+ Trees (FO, DR), pp. 269–277.
STOCSTOC-1989-ReifS #geometry #named #random
Polling: A New Randomized Sampling Technique for Computational Geometry (JHR, SS), pp. 394–404.
FMVDME-1988-JagerGK #functional #named
SAMPLE — A Functional Language (MJ, MG, SK), pp. 202–217.
VLDBVLDB-1986-OlkenR #database #random #relational
Simple Random Sampling from Relational Databases (FO, DR), pp. 160–169.
STOCSTOC-1986-Clarkson #geometry #random
Further Applications of Random Sampling to Computational Geometry (KLC), pp. 414–423.
ICALPICALP-1984-Willard #algorithm #difference #problem #retrieval
Sampling Algorithms for Differential Batch Retrieval Problems (Extended Abstract) (DEW), pp. 514–526.
DACDAC-1973-DoonerVL #design #interactive #process
An interactive graphic and process controlled system for composing and sampling loom constrained designs (NPD, PV, JRL), pp. 247–252.

Bibliography of Software Language Engineering in Generated Hypertext (BibSLEIGH) is created and maintained by Dr. Vadim Zaytsev.
Hosted as a part of SLEBOK on GitHub.