BibSLEIGH — Proceedings of the 32nd International Conference on Machine Learning

Francis R. Bach, David M. Blei
Proceedings of the 32nd International Conference on Machine Learning
ICML, 2015.

KER

@proceedings{ICML-2015,
	address       = "Lille, France",
	editor        = "Francis R. Bach and David M. Blei",
	ee            = "http://jmlr.org/proceedings/papers/v37/",
	publisher     = "{JMLR.org}",
	series        = "{JMLR Proceedings}",
	title         = "{Proceedings of the 32nd International Conference on Machine Learning}",
	volume        = 37,
	year          = 2015,
}

Contents (270 items)

ICML-2015-ZhaoZ #optimisation #probability: Stochastic Optimization with Importance Sampling for Regularized Loss Minimization (PZ, TZ), pp. 1–9.
ICML-2015-ShahZP #crowdsourcing: Approval Voting and Incentives in Crowdsourcing (NBS, DZ, YP), pp. 10–19.
ICML-2015-BounliphoneGTB #consistency #dependence: A low variance consistent test of relative dependency (WB, AG, AT, MBB), pp. 20–29.
ICML-2015-Bai0ZH #graph #kernel: An Aligned Subtree Kernel for Weighted Graphs (LB, LR, ZZ, ERH), pp. 30–39.
ICML-2015-BoutsidisKG #clustering: Spectral Clustering via the Power Method — Provably (CB, PK, AG), pp. 40–48.
ICML-2015-SunWKM #geometry #network: Information Geometry and Minimum Description Length Networks (KS, JW, AK, SMM), pp. 49–58.
ICML-2015-TristanTS #estimation #gpu #performance: Efficient Training of LDA on a GPU by Mean-for-Mode Estimation (JBT, JT, GLSJ), pp. 59–68.
ICML-2015-ZhaoYZL #adaptation #multi #probability: Adaptive Stochastic Alternating Direction Method of Multipliers (PZ, JY, TZ, PL), pp. 69–77.
ICML-2015-AgarwalB #bound #finite #optimisation: A Lower Bound for the Optimization of Finite Sums (AA, LB), pp. 78–86.
ICML-2015-YogatamaFDS #learning #word: Learning Word Representations with Hierarchical Sparse Coding (DY, MF, CD, NAS), pp. 87–96.
ICML-2015-LongC0J #adaptation #learning #network: Learning Transferable Features with Deep Adaptation Networks (ML, YC, JW, MJ), pp. 97–105.
ICML-2015-Osogami #markov #process #robust: Robust partially observable Markov decision process (TO), pp. 106–115.
ICML-2015-ZhaoMP #network #on the: On the Relationship between Sum-Product Networks and Bayesian Networks (HZ, MM, PP), pp. 116–124.
ICML-2015-MenonROW #estimation #learning: Learning from Corrupted Binary Labels via Class-Probability Estimation (AKM, BvR, CSO, BW), pp. 125–134.
ICML-2015-Yang0JZ #bound #fault #set: An Explicit Sampling Dependent Spectral Error Bound for Column Subset Selection (TY, LZ, RJ, SZ), pp. 135–143.
ICML-2015-Shamir #algorithm #convergence #exponential #probability: A Stochastic PCA and SVD Algorithm with an Exponential Convergence Rate (OS), pp. 144–152.
ICML-2015-KuklianskyS #linear #performance: Attribute Efficient Linear Regression with Distribution-Dependent Sampling (DK, OS), pp. 153–161.
ICML-2015-FetayaU #invariant #learning: Learning Local Invariant Mahalanobis Distances (EF, SU), pp. 162–168.
ICML-2015-MaLF #analysis #canonical #correlation #dataset #linear #scalability: Finding Linear Structure in Large Datasets with Scalable Canonical Correlation Analysis (ZM, YL, DPF), pp. 169–178.
ICML-2015-JiangKS #abstraction #learning #modelling: Abstraction Selection in Model-based Reinforcement Learning (NJ, AK, SS), pp. 179–188.
ICML-2015-KarN0 #precise: Surrogate Functions for Maximizing Precision at the Top (PK, HN, PJ), pp. 189–198.
ICML-2015-NarasimhanK0 #metric #optimisation #performance: Optimizing Non-decomposable Performance Measures: A Tale of Two Classes (HN, PK, PJ), pp. 199–208.
ICML-2015-BachemLK #estimation #parametricity: Coresets for Nonparametric Estimation — the Case of DP-Means (OB, ML, AK), pp. 209–217.
ICML-2015-GajaneUC #algorithm #exponential: A Relative Exponential Weighing Algorithm for Adversarial Utility-based Dueling Bandits (PG, TU, FC), pp. 218–227.
ICML-2015-BahadoriKFL #clustering #functional: Functional Subspace Clustering with Application to Time Series (MTB, DCK, YF, YL), pp. 228–237.
ICML-2015-YuCL #learning #multi #online #rank: Accelerated Online Low Rank Tensor Learning for Multivariate Spatiotemporal Streams (RY, DC, YL), pp. 238–247.
ICML-2015-JewellSB #process: Atomic Spatial Processes (SJ, NS, ABC), pp. 248–256.
ICML-2015-HazanLM #classification #rank: Classification with Low Rank and Missing Data (EH, RL, YM), pp. 257–266.
ICML-2015-RichmanM #classification #constraints: Dynamic Sensing: Better Classification under Acquisition Constraints (OR, SM), pp. 267–275.
ICML-2015-GongY #analysis #convergence #memory management: A Modified Orthant-Wise Limited Memory Quasi-Newton Method with Convergence Analysis (PG, JY), pp. 276–284.
ICML-2015-ShajarisalesJSB #linear: Telling cause from effect in deterministic linear dynamical systems (NS, DJ, BS, MB), pp. 285–294.
ICML-2015-KandasamySP #modelling #optimisation: High Dimensional Bayesian Optimisation and Bandits via Additive Models (KK, JGS, BP), pp. 295–304.
ICML-2015-Yang0JZ15a #random #reduction: Theory of Dual-sparse Regularized Randomized Reduction (TY, LZ, RJ, SZ), pp. 305–314.
ICML-2015-TewariC #bound #documentation #fault #learning #matter #question #rank: Generalization error bounds for learning to rank: Does the length of document lists matter? (AT, SC), pp. 315–323.
ICML-2015-HockingRB #detection #learning #named #segmentation: PeakSeg: constrained optimal segmentation and supervised penalty learning for peak detection in count data (TH, GR, GB), pp. 324–332.
ICML-2015-FercoqGS: Mind the duality gap: safer rules for the Lasso (OF, AG, JS), pp. 333–342.
ICML-2015-NishiharaLRPJ #analysis #convergence: A General Analysis of the Convergence of ADMM (RN, LL, BR, AP, MIJ), pp. 343–352.
ICML-2015-ZhangL #coordination #empirical #probability: Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization (YZ, XL), pp. 353–361.
ICML-2015-ZhangL15a #distributed #empirical #named #optimisation #self: DiSCO: Distributed Optimization for Self-Concordant Empirical Loss (YZ, XL), pp. 362–370.
ICML-2015-ChenS #rank: Spectral MLE: Top-K Rank Aggregation from Pairwise Comparisons (YC, CS), pp. 371–380.
ICML-2015-BachHBG #learning #performance: Paired-Dual Learning for Fast Training of Latent Variable Hinge-Loss MRFs (SHB, BH, JLBG, LG), pp. 381–390.
ICML-2015-CortesKMS #modelling: Structural Maxent Models (CC, VK, MM, US), pp. 391–399.
ICML-2015-GhoshdastidarD #clustering: A Provable Generalized Tensor Spectral Method for Uniform Hypergraph Partitioning (DG, AD), pp. 400–409.
ICML-2015-LondonHG #approximate #learning: The Benefits of Learning with Strongly Convex Approximate Inference (BL, BH, LG), pp. 410–418.
ICML-2015-XinW #adaptation #probability #rank: Pushing the Limits of Affine Rank Minimization by Adapting Probabilistic PCA (BX, DPW), pp. 419–427.
ICML-2015-MaeharaYK #game studies #multi #perspective #problem: Budget Allocation Problem with Multiple Advertisers: A Game Theoretic View (TM, AY, KiK), pp. 428–437.
ICML-2015-BlechschmidtGL #approximate #multi #optimisation #parametricity #problem: Tracking Approximate Solutions of Parameterized Optimization Problems over Multi-Dimensional (Hyper-)Parameter Domains (KB, JG, SL), pp. 438–447.
ICML-2015-IoffeS #network #normalisation: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (SI, CS), pp. 448–456.
ICML-2015-ZhangWJ #algorithm #bound #distributed #estimation #matrix #performance #rank: Distributed Estimation of Generalized Matrix Rank: Efficient Algorithms and Lower Bounds (YZ, MJW, MIJ), pp. 457–465.
ICML-2015-LiangP #process: Landmarking Manifolds with Gaussian Processes (DL, JP), pp. 466–474.
ICML-2015-ZhangP #markov #modelling: Markov Mixed Membership Models (AZ, JP), pp. 475–483.
ICML-2015-YangX #algorithm #framework: A Unified Framework for Outlier-Robust PCA-like Algorithms (WY, HX), pp. 484–493.
ICML-2015-YangX15a #analysis #component #streaming: Streaming Sparse Principal Component Analysis (WY, HX), pp. 494–503.
ICML-2015-YangX15b #clustering #distributed #divide and conquer #framework #graph: A Divide and Conquer Framework for Distributed Graph Clustering (WY, HX), pp. 504–513.
ICML-2015-AnBB #how #linear #network #question: How Can Deep Rectifier Networks Achieve Linear Separability and Preserve Distances? (SA, FB, MB), pp. 514–523.
ICML-2015-LakshmananOR #bound #learning: Improved Regret Bounds for Undiscounted Continuous Reinforcement Learning (KL, RO, DR), pp. 524–532.
ICML-2015-Betancourt #monte carlo #scalability: The Fundamental Incompatibility of Scalable Hamiltonian Monte Carlo and Naive Data Subsampling (MB), pp. 533–540.
ICML-2015-GarberH #performance #set: Faster Rates for the Frank-Wolfe Method over Strongly-Convex Sets (DG, EH), pp. 541–549.
ICML-2015-DasBB #modelling #order #parametricity: Ordered Stick-Breaking Prior for Sequential MCMC Inference of Bayesian Nonparametric Models (MKD, TB, CB), pp. 550–559.
ICML-2015-GarberHM #learning #online: Online Learning of Eigenvectors (DG, EH, TM), pp. 560–568.
ICML-2015-HoangHL #big data #framework #modelling #probability #process: A Unifying Framework of Anytime Sparse Gaussian Process Regression Models with Stochastic Variational Inference for Big Data (TNH, QMH, BKHL), pp. 569–578.
ICML-2015-DingZSMM #consistency: Yinyang K-Means: A Drop-In Replacement of the Classic K-Means with Consistent Speedup (YD, YZ, XS, MM, TM), pp. 579–587.
ICML-2015-VirtanenG #modelling: Ordinal Mixed Membership Models (SV, MG), pp. 588–596.
ICML-2015-HongYKH #learning #network #online: Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network (SH, TY, SK, BH), pp. 597–606.
ICML-2015-FlaxmanWNNS #performance #process: Fast Kronecker Inference in Gaussian Processes with non-Gaussian Likelihoods (SF, AGW, DN, HN, AJS), pp. 607–616.
ICML-2015-RaskuttiM #algorithm #random #sketching #statistics: Statistical and Algorithmic Perspectives on Randomized Sketching for Ordinary Least-Squares (GR, MM), pp. 617–625.
ICML-2015-KordaA #approximate #bound #convergence #exponential #on the: On TD(0) with function approximation: Concentration bounds and a centered variant with exponential convergence (NK, PLA), pp. 626–634.
ICML-2015-WeissN #alias #learning: Learning Parametric-Output HMMs with Two Aliased States (RW, BN), pp. 635–644.
ICML-2015-GalCG #category theory #estimation #multi #process: Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data (YG, YC, ZG), pp. 645–654.
ICML-2015-GalT #approximate #nondeterminism #process #representation: Improving the Gaussian Process Sparse Spectrum Approximation by Representing Uncertainty in Frequency Inputs (YG, RT), pp. 655–664.
ICML-2015-RajkumarGL0 #probability #ranking #set: Ranking from Stochastic Pairwise Preferences: Recovering Condorcet Winners and Tournament Solution Sets at the Top (AR, SG, LHL, SA), pp. 665–673.
ICML-2015-CsibaQR #adaptation #coordination #probability: Stochastic Dual Coordinate Ascent with Adaptive Probabilities (DC, ZQ, PR), pp. 674–683.
ICML-2015-TanseyPSR #exponential #markov #product line #random: Vector-Space Markov Random Fields via Exponential Families (WT, OHMP, ASS, PR), pp. 684–692.
ICML-2015-HugginsNSM #markov #named #process: JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes (JHH, KN, AS, VKM), pp. 693–701.
ICML-2015-UbaruMS #approximate #fault #matrix #rank #using: Low Rank Approximation using Error Correcting Coding Matrices (SU, AM, YS), pp. 702–710.
ICML-2015-HallakSMM #learning #modelling: Off-policy Model-based Learning under Unknown Factored Dynamics (AH, FS, TAM, SM), pp. 711–719.
ICML-2015-HuangWSLC #classification #image #learning #metric #set #symmetry: Log-Euclidean Metric Learning on Symmetric Positive Definite Manifold with Application to Image Set Classification (ZH, RW, SS, XL, XC), pp. 720–729.
ICML-2015-Kandemir #learning #process #symmetry: Asymmetric Transfer Learning with Deep Gaussian Processes (MK), pp. 730–738.
ICML-2015-ZhuG #complexity #robust #towards: Towards a Lower Sample Complexity for Robust One-bit Compressed Sensing (RZ, QG), pp. 739–747.
ICML-2015-GouwsBC #distributed #named #performance #word: BilBOWA: Fast Bilingual Distributed Representations without Word Alignments (SG, YB, GC), pp. 748–756.
ICML-2015-SunLXB #clustering #multi: Multi-view Sparse Co-clustering via Proximal Alternating Linearized Minimization (JS, JL, TX, JB), pp. 757–766.
ICML-2015-KvetonSWA #learning #rank: Cascading Bandits: Learning to Rank in the Cascade Model (BK, CS, ZW, AA), pp. 767–776.
ICML-2015-FouldsKG #framework #modelling #network #probability #programming #topic: Latent Topic Networks: A Versatile Probabilistic Programming Framework for Topic Models (JRF, SHK, LG), pp. 777–786.
ICML-2015-EneN #coordination #random: Random Coordinate Descent Methods for Minimizing Decomposable Submodular Functions (AE, HLN), pp. 787–795.
ICML-2015-NarayanPA #metaprogramming: α-β Divergences Discover Micro and Macro Structures in Data (KSN, AP, PA), pp. 796–804.
ICML-2015-HeinrichLS #game studies #self: Fictitious Self-Play in Extensive-Form Games (JH, ML, DS), pp. 805–813.
ICML-2015-SwaminathanJ #feedback #learning: Counterfactual Risk Minimization: Learning from Logged Bandit Feedback (AS, TJ), pp. 814–823.
ICML-2015-KricheneBTB #algorithm: The Hedge Algorithm on a Continuum (WK, MB, CJT, AMB), pp. 824–832.
ICML-2015-BelangerK #linear: A Linear Dynamical System Model for Text (DB, SMK), pp. 833–842.
ICML-2015-SrivastavaMS #learning #using #video: Unsupervised Learning of Video Representations using LSTMs (NS, EM, RS), pp. 843–852.
ICML-2015-SunSK #message passing #modelling #visual notation: Message Passing for Collective Graphical Models (TS, DS, AK), pp. 853–861.
ICML-2015-WangZ #clustering #named #parametricity: DP-space: Bayesian Nonparametric Subspace Clustering with Small-variance Asymptotics (YW, JZ), pp. 862–870.
ICML-2015-HeRFGL #modelling #named #network #topic: HawkesTopic: A Joint Model for Network Inference and Topic Modeling from Text-Based Cascades (XH, TR, JRF, LG, YL), pp. 871–880.
ICML-2015-GermainGML #estimation #named: MADE: Masked Autoencoder for Distribution Estimation (MG, KG, IM, HL), pp. 881–889.
ICML-2015-WuS #algorithm #learning #modelling #online: An Online Learning Algorithm for Bilinear Models (YW, SS), pp. 890–898.
ICML-2015-Papachristoudis #adaptation: Adaptive Belief Propagation (GP, JWF), pp. 899–907.
ICML-2015-HanMS #probability #scalability: Large-scale log-determinant computation through stochastic Chebyshev expansions (IH, DM, JS), pp. 908–917.
ICML-2015-KusnerGGW #optimisation: Differentially Private Bayesian Optimization (MJK, JRG, RG, KQW), pp. 918–927.
ICML-2015-HegdeIS #framework: A Nearly-Linear Time Framework for Graph-Structured Sparsity (CH, PI, LS), pp. 928–937.
ICML-2015-LuoXZL #matrix: Support Matrix Machines (LL, YX, ZZ, WJL), pp. 938–947.
ICML-2015-NockPF: Rademacher Observations, Private Data, and Boosting (RN, GP, AF), pp. 948–956.
ICML-2015-KusnerSKW #documentation #word: From Word Embeddings To Document Distances (MJK, YS, NIK, KQW), pp. 957–966.
ICML-2015-MatthewCYW #empirical: Bayesian and Empirical Bayesian Forests (TM, CSC, JY, MW), pp. 967–976.
ICML-2015-Pouget-AbadieH #framework #graph: Inferring Graphs from Cascades: A Sparse Recovery Framework (JPA, TH), pp. 977–986.
ICML-2015-LeeR #distributed #linear #optimisation #polynomial: Distributed Box-Constrained Quadratic Optimization for Dual Linear SVM (CPL, DR), pp. 987–996.
ICML-2015-SuiGBK #optimisation #process: Safe Exploration for Optimization with Gaussian Processes (YS, AG, JWB, AK), pp. 997–1005.
ICML-2015-BlumH #contest #machine learning #reliability: The Ladder: A Reliable Leaderboard for Machine Learning Competitions (AB, MH), pp. 1006–1014.
ICML-2015-FilipponeE #linear #probability #process #scalability: Enabling scalable stochastic gradient-based inference for Gaussian processes by employing the Unbiased LInear System SolvEr (ULISSE) (MF, RE), pp. 1015–1024.
ICML-2015-GarnettHS #process: Finding Galaxies in the Shadows of Quasars with Gaussian Processes (RG, SH, JS), pp. 1025–1033.
ICML-2015-CohenH #learning #online: Following the Perturbed Leader for Online Structured Learning (AC, TH), pp. 1034–1042.
ICML-2015-SteinhardtL #modelling: Reified Context Models (JS, PL), pp. 1043–1052.
ICML-2015-Abbasi-YadkoriB #crowdsourcing #markov #problem #scalability: Large-Scale Markov Decision Problems with KL Control Cost and its Application to Crowdsourcing (YAY, PLB, XC, AM), pp. 1053–1062.
ICML-2015-SteinhardtL15a #learning #modelling #predict: Learning Fast-Mixing Models for Structured Prediction (JS, PL), pp. 1063–1072.
ICML-2015-Hernandez-Lobato #feature model #multi #probability: A Probabilistic Model for Dirty Multi-task Feature Selection (DHL, JMHL, ZG), pp. 1073–1082.
ICML-2015-WangALB #learning #multi #on the #representation: On Deep Multi-View Representation Learning (WW, RA, KL, JAB), pp. 1083–1092.
ICML-2015-PiechHNPSG #feedback #learning #student: Learning Program Embeddings to Propagate Feedback on Student Code (CP, JH, AN, MP, MS, LJG), pp. 1093–1102.
ICML-2015-ZhouZ #problem: Safe Subspace Screening for Nuclear Norm Regularized Least Squares Problems (QZ, QZ), pp. 1103–1112.
ICML-2015-WenKA #combinator #learning #performance #scalability: Efficient Learning in Large-Scale Combinatorial Semi-Bandits (ZW, BK, AA), pp. 1113–1122.
ICML-2015-ManoelKTZ #approximate #estimation #message passing: Swept Approximate Message Passing for Sparse Estimation (AM, FK, EWT, LZ), pp. 1123–1132.
ICML-2015-CarpentierV #infinity: Simple regret for infinitely many armed bandits (AC, MV), pp. 1133–1141.
ICML-2015-ChaoSMS #exponential #integration #monte carlo: Exponential Integration for Hamiltonian Monte Carlo (WLC, JS, DM, FS), pp. 1142–1151.
ICML-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.
ICML-2015-IzbickiS #performance: Faster cover trees (MI, CRS), pp. 1162–1170.
ICML-2015-JohnsonG #named #optimisation #scalability: Blitz: A Principled Meta-Algorithm for Scaling Sparse Optimization (TJ, CG), pp. 1171–1179.
ICML-2015-GaninL #adaptation: Unsupervised Domain Adaptation by Backpropagation (YG, VSL), pp. 1180–1189.
ICML-2015-LiuHW #collaboration: Non-Linear Cross-Domain Collaborative Filtering via Hyper-Structure Transfer (YFL, CYH, SHW), pp. 1190–1198.
ICML-2015-KimXVS #process: Manifold-valued Dirichlet Processes (HJK, JX, BCV, VS), pp. 1199–1208.
ICML-2015-WangWLCW #learning #multi #segmentation: Multi-Task Learning for Subspace Segmentation (YW, DPW, QL, WC, IJW), pp. 1209–1217.
ICML-2015-SalimansKW #markov #monte carlo: Markov Chain Monte Carlo and Variational Inference: Bridging the Gap (TS, DPK, MW), pp. 1218–1226.
ICML-2015-LiuFFM #modelling #relational #scalability: Scalable Model Selection for Large-Scale Factorial Relational Models (CL, LF, RF, YM), pp. 1227–1235.
ICML-2015-BarbosaENW #dataset #distributed #power of: The Power of Randomization: Distributed Submodular Maximization on Massive Datasets (RdPB, AE, HLN, JW), pp. 1236–1244.
ICML-2015-EggelingKG #big data: Dealing with small data: On the generalization of context trees (RE, MK, IG), pp. 1245–1253.
ICML-2015-YuanHTLC #modelling: Non-Gaussian Discriminative Factor Models via the Max-Margin Rank-Likelihood (XY, RH, ET, RL, LC), pp. 1254–1263.
ICML-2015-BenavoliCMZ #algorithm #parametricity: A Bayesian nonparametric procedure for comparing algorithms (AB, GC, FM, MZ), pp. 1264–1272.
ICML-2015-Suzuki #convergence: Convergence rate of Bayesian tensor estimator and its minimax optimality (TS), pp. 1273–1282.
ICML-2015-WuGS #combinator #feedback #finite #identification #on the: On Identifying Good Options under Combinatorially Structured Feedback in Finite Noisy Environments (YW, AG, CS), pp. 1283–1291.
ICML-2015-NaessethLS #monte carlo: Nested Sequential Monte Carlo Methods (CAN, FL, TBS), pp. 1292–1301.
ICML-2015-ShethWK #modelling: Sparse Variational Inference for Generalized GP Models (RS, YW, RK), pp. 1302–1311.
ICML-2015-SchaulHGS #approximate: Universal Value Function Approximators (TS, DH, KG, DS), pp. 1312–1320.
ICML-2015-PerolatSPP #approximate #game studies #markov #programming: Approximate Dynamic Programming for Two-Player Zero-Sum Markov Games (JP, BS, BP, OP), pp. 1321–1329.
ICML-2015-SharmaKD #on the: On Greedy Maximization of Entropy (DS, AK, AD), pp. 1330–1338.
ICML-2015-WangLWC #metadata #process: Metadata Dependent Mondrian Processes (YW, BL, YW, FC), pp. 1339–1347.
ICML-2015-ChangYXY #detection #semantics #using: Complex Event Detection using Semantic Saliency and Nearly-Isotonic SVM (XC, YY, EPX, YY), pp. 1348–1357.
ICML-2015-HayashiMF: Rebuilding Factorized Information Criterion: Asymptotically Accurate Marginal Likelihood (KH, SiM, RF), pp. 1358–1366.
ICML-2015-LimKPJ #performance #scalability #set: Double Nyström Method: An Efficient and Accurate Nyström Scheme for Large-Scale Data Sets (WL, MK, HP, KJ), pp. 1367–1375.
ICML-2015-KairouzOV #composition #difference #privacy #theorem: The Composition Theorem for Differential Privacy (PK, SO, PV), pp. 1376–1385.
ICML-2015-PlessisNS #learning: Convex Formulation for Learning from Positive and Unlabeled Data (MCdP, GN, MS), pp. 1386–1394.
ICML-2015-MiyauchiIFK: Threshold Influence Model for Allocating Advertising Budgets (AM, YI, TF, NK), pp. 1395–1404.
ICML-2015-DanielyGS #adaptation #learning #online: Strongly Adaptive Online Learning (AD, AG, SSS), pp. 1405–1411.
ICML-2015-XuJZ #algorithm #matrix: CUR Algorithm for Partially Observed Matrices (MX, RJ, ZHZ), pp. 1412–1421.
ICML-2015-WangWS #analysis #clustering: A Deterministic Analysis of Noisy Sparse Subspace Clustering for Dimensionality-reduced Data (YW, YXW, AS), pp. 1422–1431.
ICML-2015-SibonyCJ #learning #ranking #statistics: MRA-based Statistical Learning from Incomplete Rankings (ES, SC, JJ), pp. 1432–1441.
ICML-2015-HugginsT: Risk and Regret of Hierarchical Bayesian Learners (JH, JT), pp. 1442–1451.
ICML-2015-Lopez-PazMST #learning #towards: Towards a Learning Theory of Cause-Effect Inference (DLP, KM, BS, IT), pp. 1452–1461.
ICML-2015-GregorDGRW #generative #image #named #network: DRAW: A Recurrent Neural Network For Image Generation (KG, ID, AG, DJR, DW), pp. 1462–1471.
ICML-2015-AmidU #learning #multi: Multiview Triplet Embedding: Learning Attributes in Multiple Maps (EA, AU), pp. 1472–1480.
ICML-2015-DeisenrothN #distributed #process: Distributed Gaussian Processes (MPD, JWN), pp. 1481–1490.
ICML-2015-TangS #approach #composition: Guaranteed Tensor Decomposition: A Moment Approach (GT, PS), pp. 1491–1500.
ICML-2015-ZhouZS #analysis #bound #convergence #fault #first-order: ℓ₁_,p-Norm Regularization: Error Bounds and Convergence Rate Analysis of First-Order Methods (ZZ, QZ, AMCS), pp. 1501–1510.
ICML-2015-HanXA #consistency #estimation #modelling #multi: Consistent estimation of dynamic and multi-layer block models (QH, KSX, EA), pp. 1511–1520.
ICML-2015-TagortiS #bound #convergence #fault #on the: On the Rate of Convergence and Error Bounds for LSTD(λ) (MT, BS), pp. 1521–1529.
ICML-2015-RezendeM #normalisation: Variational Inference with Normalizing Flows (DJR, SM), pp. 1530–1538.
ICML-2015-MacdonaldHH #modelling #process: Controversy in mechanistic modelling with Gaussian processes (BM, CFH, DH), pp. 1539–1547.
ICML-2015-CilibertoMPR #learning #multi: Convex Learning of Multiple Tasks and their Structure (CC, YM, TAP, LR), pp. 1548–1557.
ICML-2015-OsadchyHK #classification: K-hyperplane Hinge-Minimax Classifier (MO, TH, DK), pp. 1558–1566.
ICML-2015-LesnerS #approximate #policy: Non-Stationary Approximate Modified Policy Iteration (BL, BS), pp. 1567–1575.
ICML-2015-SerrurierP #evaluation #learning: Entropy evaluation based on confidence intervals of frequency estimates : Application to the learning of decision trees (MS, HP), pp. 1576–1584.
ICML-2015-YouV #geometry: Geometric Conditions for Subspace-Sparse Recovery (CY, RV), pp. 1585–1593.
ICML-2015-ShahKG #algorithm #empirical #probability #process: An Empirical Study of Stochastic Variational Inference Algorithms for the Beta Bernoulli Process (AS, DAK, ZG), pp. 1594–1603.
ICML-2015-ZhuSG #memory management #recursion: Long Short-Term Memory Over Recursive Structures (XDZ, PS, HG), pp. 1604–1612.
ICML-2015-BlundellCKW #network #nondeterminism: Weight Uncertainty in Neural Network (CB, JC, KK, DW), pp. 1613–1622.
ICML-2015-YuB #learning: Learning Submodular Losses with the Lovasz Hinge (JY, MBB), pp. 1623–1631.
ICML-2015-NutiniSLFK #coordination #performance #random: Coordinate Descent Converges Faster with the Gauss-Southwell Rule Than Random Selection (JN, MWS, IHL, MPF, HAK), pp. 1632–1641.
ICML-2015-LengWCZL #distributed: Hashing for Distributed Data (CL, JW, JC, XZ, HL), pp. 1642–1650.
ICML-2015-HuHDX #distributed #parametricity #scalability: Large-scale Distributed Dependent Nonparametric Trees (ZH, QH, AD, EPX), pp. 1651–1659.
ICML-2015-SzorenyiBWH #approach #multi: Qualitative Multi-Armed Bandits: A Quantile-Based Approach (BS, RBF, PW, EH), pp. 1660–1668.
ICML-2015-XuRYLJ: Deep Edge-Aware Filters (LX, JR, QY, RL, JJ), pp. 1669–1678.
ICML-2015-LimCX #clustering #framework #optimisation: A Convex Optimization Framework for Bi-Clustering (SHL, YC, HX), pp. 1679–1688.
ICML-2015-XiaoBBFER #feature model #question: Is Feature Selection Secure against Training Data Poisoning? (HX, BB, GB, GF, CE, FR), pp. 1689–1698.
ICML-2015-Hernandez-Lobato15a #constraints #optimisation #predict: Predictive Entropy Search for Bayesian Optimization with Unknown Constraints (JMHL, MAG, MWH, RPA, ZG), pp. 1699–1707.
ICML-2015-PerrotH #analysis #learning #metric: A Theoretical Analysis of Metric Hypothesis Transfer Learning (MP, AH), pp. 1708–1717.
ICML-2015-LiSZ #generative #network: Generative Moment Matching Networks (YL, KS, RSZ), pp. 1718–1727.
ICML-2015-AsterisKDYC #graph: Stay on path: PCA along graph paths (MA, ATK, AGD, HGY, BC), pp. 1728–1736.
ICML-2015-GuptaAGN #learning #precise: Deep Learning with Limited Numerical Precision (SG, AA, KG, PN), pp. 1737–1746.
ICML-2015-WangY #learning #matrix #multi: Safe Screening for Multi-Task Feature Learning with Multiple Data Matrices (JW, JY), pp. 1747–1756.
ICML-2015-CohenW #exponential #product line: Harmonic Exponential Families on Manifolds (TC, MW), pp. 1757–1765.
ICML-2015-ClarkS #game studies #network: Training Deep Convolutional Neural Networks to Play Go (CC, AJS), pp. 1766–1774.
ICML-2015-WilsonN #kernel #process #scalability: Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP) (AGW, HN), pp. 1775–1784.
ICML-2015-ChenSYU #learning #modelling: Learning Deep Structured Models (LCC, AGS, ALY, RU), pp. 1785–1794.
ICML-2015-AvronH #community #detection #personalisation #rank #using: Community Detection Using Time-Dependent Personalized PageRank (HA, LH), pp. 1795–1803.
ICML-2015-DjolongaK #modelling #scalability: Scalable Variational Inference in Log-supermodular Models (JD, AK), pp. 1804–1813.
ICML-2015-LloydGOR #process: Variational Inference for Gaussian Process Modulated Poisson Processes (CML, TG, MAO, SJR), pp. 1814–1822.
ICML-2015-GanCHCC #analysis #modelling #scalability #topic: Scalable Deep Poisson Factor Analysis for Topic Modeling (ZG, CC, RH, DEC, LC), pp. 1823–1832.
ICML-2015-GornitzBK #detection #markov: Hidden Markov Anomaly Detection (NG, MLB, MK), pp. 1833–1842.
ICML-2015-QiuXHLC #estimation #matrix #process #robust: Robust Estimation of Transition Matrices in High Dimensional Heavy-tailed Vector Autoregressive Processes (HQ, SX, FH, HL, BC), pp. 1843–1851.
ICML-2015-RamaswamyT0 #classification: Convex Calibrated Surrogates for Hierarchical Classification (HGR, AT, SA), pp. 1852–1860.
ICML-2015-Hernandez-Lobato15b #learning #network #probability #scalability: Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks (JMHL, RA), pp. 1861–1869.
ICML-2015-BerlindU #nearest neighbour: Active Nearest Neighbors in Changing Environments (CB, RU), pp. 1870–1879.
ICML-2015-LiuY #graph #learning #predict: Bipartite Edge Prediction via Transductive Learning over Product Graphs (HL, YY), pp. 1880–1888.
ICML-2015-SchulmanLAJM #optimisation #policy #trust: Trust Region Policy Optimization (JS, SL, PA, MIJ, PM), pp. 1889–1897.
ICML-2015-GongZSTG: Discovering Temporal Causal Relations from Subsampled Data (MG, KZ, BS, DT, PG), pp. 1898–1906.
ICML-2015-ParkNZSD #collaboration #ranking #scalability: Preference Completion: Large-scale Collaborative Ranking from Pairwise Comparisons (DP, JN, JZ, SS, ISD), pp. 1907–1916.
ICML-2015-GeigerZSGJ #component #identification #process: Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components (PG, KZ, BS, MG, DJ), pp. 1917–1925.
ICML-2015-NeyshaburS #on the #symmetry: On Symmetric and Asymmetric LSHs for Inner Product Search (BN, NS), pp. 1926–1934.
ICML-2015-JiaoV #kernel #permutation: The Kendall and Mallows Kernels for Permutations (YJ, JPV), pp. 1935–1944.
ICML-2015-RajanHSFJ #locality #multi: Bayesian Multiple Target Localization (PR, WH, RS, PIF, BJ), pp. 1945–1953.
ICML-2015-WeiIB #learning #set: Submodularity in Data Subset Selection and Active Learning (KW, RKI, JAB), pp. 1954–1963.
ICML-2015-BachmanP #collaboration #generative #network #probability: Variational Generative Stochastic Networks with Collaborative Shaping (PB, DP), pp. 1964–1972.
ICML-2015-MaSJJRT #distributed #optimisation: Adding vs. Averaging in Distributed Primal-Dual Optimization (CM, VS, MJ, MIJ, PR, MT), pp. 1973–1982.
ICML-2015-NanWS #random: Feature-Budgeted Random Forest (FN, JW, VS), pp. 1983–1991.
ICML-2015-LibbrechtHBN #graph: Entropic Graph-based Posterior Regularization (ML, MMH, JAB, WSN), pp. 1992–2001.
ICML-2015-LeC #learning #metric #using: Unsupervised Riemannian Metric Learning for Histograms Using Aitchison Transformations (TL, MC), pp. 2002–2011.
ICML-2015-ZukW #matrix #metric #rank: Low-Rank Matrix Recovery from Row-and-Column Affine Measurements (OZ, AW), pp. 2012–2020.
ICML-2015-GiguereRLM #algorithm #kernel #predict #problem #string: Algorithms for the Hard Pre-Image Problem of String Kernels and the General Problem of String Prediction (SG, AR, FL, MM), pp. 2021–2029.
ICML-2015-LianHRLC #multi #predict #process: A Multitask Point Process Predictive Model (WL, RH, VR, JEL, LC), pp. 2030–2038.
ICML-2015-ZhuE #approach #hybrid #probability #random #using: A Hybrid Approach for Probabilistic Inference using Random Projections (MZ, SE), pp. 2039–2047.
ICML-2015-XuBKCCSZB #generative #image #visual notation: Show, Attend and Tell: Neural Image Caption Generation with Visual Attention (KX, JB, RK, KC, ACC, RS, RSZ, YB), pp. 2048–2057.
ICML-2015-ChangKADL #education #learning: Learning to Search Better than Your Teacher (KWC, AK, AA, HDI, JL), pp. 2058–2066.
ICML-2015-ChungGCB #feedback #network: Gated Feedback Recurrent Neural Networks (JC, ÇG, KC, YB), pp. 2067–2075.
ICML-2015-Soltanmohammadi #data fusion: Context-based Unsupervised Data Fusion for Decision Making (ES, MNP, MvdS), pp. 2076–2084.
ICML-2015-LebretPC #image: Phrase-based Image Captioning (RL, POP, RC), pp. 2085–2094.
ICML-2015-RegierMMAHLSP #generative #image #named: Celeste: Variational inference for a generative model of astronomical images (JR, AM, JM, RPA, MDH, DL, DS, P), pp. 2095–2103.
ICML-2015-PrasadPR #analysis #axiom #rank: Distributional Rank Aggregation, and an Axiomatic Analysis (AP, HHP, PDR), pp. 2104–2112.
ICML-2015-MaclaurinDA #learning #optimisation: Gradient-based Hyperparameter Optimization through Reversible Learning (DM, DKD, RPA), pp. 2113–2122.
ICML-2015-AllamanisTGW #modelling #natural language #source code: Bimodal Modelling of Source Code and Natural Language (MA, DT, ADG, YW), pp. 2123–2132.
ICML-2015-HanawalSVM: Cheap Bandits (MKH, VS, MV, RM), pp. 2133–2142.
ICML-2015-ChazalFLMRW #persistent: Subsampling Methods for Persistent Homology (FC, BF, FL, BM, AR, LAW), pp. 2143–2151.
ICML-2015-Romera-ParedesT #approach #learning: An embarrassingly simple approach to zero-shot learning (BRP, PHST), pp. 2152–2161.
ICML-2015-YiCP #algorithm #performance: Binary Embedding: Fundamental Limits and Fast Algorithm (XY, CC, EP), pp. 2162–2170.
ICML-2015-SnoekRSKSSPPA #network #optimisation #scalability #using: Scalable Bayesian Optimization Using Deep Neural Networks (JS, OR, KS, RK, NS, NS, MMAP, P, RPA), pp. 2171–2180.
ICML-2015-GlobersonRSY #how #predict #question: How Hard is Inference for Structured Prediction? (AG, TR, DS, CY), pp. 2181–2190.
ICML-2015-AnavaHZ #online #predict: Online Time Series Prediction with Missing Data (OA, EH, AZ), pp. 2191–2199.
ICML-2015-PachecoS #approach #pseudo: Proteins, Particles, and Pseudo-Max-Marginals: A Submodular Approach (JP, EBS), pp. 2200–2208.
ICML-2015-JerniteRS #approach #learning #markov #modelling #performance #random: A Fast Variational Approach for Learning Markov Random Field Language Models (YJ, AMR, DS), pp. 2209–2217.
ICML-2015-ScholkopfHWFJSP #fault: Removing systematic errors for exoplanet search via latent causes (BS, DWH, DW, DFM, DJ, CJSG, JP), pp. 2218–2226.
ICML-2015-SamoR #parametricity #process #scalability: Scalable Nonparametric Bayesian Inference on Point Processes with Gaussian Processes (YLKS, SR), pp. 2227–2236.
ICML-2015-AhnCGMW #clustering #correlation #data type: Correlation Clustering in Data Streams (KJA, GC, SG, AM, AW), pp. 2237–2246.
ICML-2015-TangSX #learning #network: Learning Scale-Free Networks by Dynamic Node Specific Degree Prior (QT, SS, JX), pp. 2247–2255.
ICML-2015-Sohl-DicksteinW #learning #using: Deep Unsupervised Learning using Nonequilibrium Thermodynamics (JSD, EAW, NM, SG), pp. 2256–2265.
ICML-2015-TraskGR #modelling #order #scalability #word: Modeling Order in Neural Word Embeddings at Scale (AT, DG, MR), pp. 2266–2275.
ICML-2015-GeCWG #distributed #modelling #process: Distributed Inference for Dirichlet Process Mixture Models (HG, YC, MW, ZG), pp. 2276–2284.
ICML-2015-ChenWTWC #network: Compressing Neural Networks with the Hashing Trick (WC, JTW, ST, KQW, YC), pp. 2285–2294.
ICML-2015-GeZ #matrix: Intersecting Faces: Non-negative Matrix Factorization With New Guarantees (RG, JZ), pp. 2295–2303.
ICML-2015-GrosseS #matrix #scalability: Scaling up Natural Gradient by Sparsely Factorizing the Inverse Fisher Matrix (RBG, RS), pp. 2304–2313.
ICML-2015-VanseijenS #learning: A Deeper Look at Planning as Learning from Replay (HV, RS), pp. 2314–2322.
ICML-2015-BeygelzimerKL #adaptation #algorithm #online: Optimal and Adaptive Algorithms for Online Boosting (AB, SK, HL), pp. 2323–2331.
ICML-2015-SaRO #convergence #matrix #probability #problem: Global Convergence of Stochastic Gradient Descent for Some Non-convex Matrix Problems (CDS, CR, KO), pp. 2332–2341.
ICML-2015-JozefowiczZS #architecture #empirical #network: An Empirical Exploration of Recurrent Network Architectures (RJ, WZ, IS), pp. 2342–2350.
ICML-2015-SunQW #optimisation #taxonomy #using: Complete Dictionary Recovery Using Nonconvex Optimization (JS, QQ, JW), pp. 2351–2360.
ICML-2015-Bou-AmmarTE #learning #policy #sublinear: Safe Policy Search for Lifelong Reinforcement Learning with Sublinear Regret (HBA, RT, EE), pp. 2361–2369.
ICML-2015-HsiehYD #named #parallel #probability: PASSCoDe: Parallel ASynchronous Stochastic dual Co-ordinate Descent (CJH, HFY, ISD), pp. 2370–2379.
ICML-2015-ThomasTG #policy: High Confidence Policy Improvement (PST, GT, MG), pp. 2380–2388.
ICML-2015-MarietS #algorithm #fixpoint #learning #process: Fixed-point algorithms for learning determinantal point processes (ZM, SS), pp. 2389–2397.
ICML-2015-NarasimhanRS0 #algorithm #consistency #metric #multi #performance: Consistent Multiclass Algorithms for Complex Performance Measures (HN, HGR, AS, SA), pp. 2398–2407.
ICML-2015-MartensG #approximate #network #optimisation: Optimizing Neural Networks with Kronecker-factored Approximate Curvature (JM, RBG), pp. 2408–2417.
ICML-2015-YenLZRD #approach #modelling #process: A Convex Exemplar-based Approach to MAD-Bayes Dirichlet Process Mixture Models (IEHY, XL, KZ, PKR, ISD), pp. 2418–2426.
ICML-2015-PhamRFA #learning #multi #novel: Multi-instance multi-label learning in the presence of novel class instances (ATP, RR, XZF, JPA), pp. 2427–2435.
ICML-2015-RalaivolaA: Entropy-Based Concentration Inequalities for Dependent Variables (LR, MRA), pp. 2436–2444.
ICML-2015-HsiehND #learning #matrix: PU Learning for Matrix Completion (CJH, NN, ISD), pp. 2445–2453.
ICML-2015-AybatWI #distributed #optimisation: An Asynchronous Distributed Proximal Gradient Method for Composite Convex Optimization (NSA, ZW, GI), pp. 2454–2462.
ICML-2015-YangRV #clustering: Sparse Subspace Clustering with Missing Entries (CY, DR, RV), pp. 2463–2472.
ICML-2015-GuanSBMBB #linear: Moderated and Drifting Linear Dynamical Systems (JG, KS, EB, CM, EB, KB), pp. 2473–2482.
ICML-2015-LeeY #category theory #predict #strict: Boosted Categorical Restricted Boltzmann Machine for Computational Prediction of Splice Junctions (TL, SY), pp. 2483–2492.
ICML-2015-WangFS #for free #monte carlo #privacy #probability: Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo (YXW, SEF, AJS), pp. 2493–2502.
ICML-2015-TheisH #probability #streaming: A trust-region method for stochastic variational inference with applications to streaming data (LT, MDH), pp. 2503–2511.
ICML-2015-WinnerBS: Inference in a Partially Observed Queuing Model with Applications in Ecology (KW, GB, DS), pp. 2512–2520.
ICML-2015-HuangGS #analysis #component #independence: Deterministic Independent Component Analysis (RH, AG, CS), pp. 2521–2530.
ICML-2015-GasseAE #classification #composition #multi #on the #set: On the Optimality of Multi-Label Classification under Subset Zero-One Loss for Distributions Satisfying the Composition Property (MG, AA, HE), pp. 2531–2539.
ICML-2015-FrostigGKS #algorithm #approximate #empirical #named #performance #probability: Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization (RF, RG, SK, AS), pp. 2540–2548.
ICML-2015-GuL #algorithm #fault: A New Generalized Error Path Algorithm for Model Selection (BG, CXL), pp. 2549–2558.

Francis R. Bach, David M. BleiProceedings of the 32nd International Conference on Machine LearningICML, 2015.

Contents (270 items)

Francis R. Bach, David M. Blei
Proceedings of the 32nd International Conference on Machine Learning
ICML, 2015.