Proceedings of the 28th International Conference on Machine Learning
BibSLEIGH corpus
BibSLEIGH tags
BibSLEIGH bundles
BibSLEIGH people
EDIT!
CC-BY
Open Knowledge
XHTML 1.0 W3C Rec
CSS 2.1 W3C CanRec
email twitter

Lise Getoor, Tobias Scheffer
Proceedings of the 28th International Conference on Machine Learning
ICML, 2011.

KER
DBLP
Scholar
Full names Links ISxN
@proceedings{ICML-2011,
	address       = "Bellevue, Washington, USA",
	editor        = "Lise Getoor and Tobias Scheffer",
	publisher     = "{Omnipress}",
	title         = "{Proceedings of the 28th International Conference on Machine Learning}",
	year          = 2011,
}

Contents (152 items)

ICML-2011-LiuWKC #graph
Hashing with Graphs (WL, JW, SK, SFC), pp. 1–8.
ICML-2011-ZhongK #automation #modelling #performance
Efficient Sparse Modeling with Automatic Feature Grouping (WZ, JTK), pp. 9–16.
ICML-2011-BiK #classification #multi
MultiLabel Classification on Tree- and DAG-Structured Hierarchies (WB, JTK), pp. 17–24.
ICML-2011-HeL #framework #learning #multi
A Graphbased Framework for Multi-Task Multi-View Learning (JH, RL), pp. 25–32.
ICML-2011-ZhouT #composition #matrix #named #random
GoDec: Randomized Lowrank & Sparse Matrix Decomposition in Noisy Case (TZ, DT), pp. 33–40.
ICML-2011-YuM
Unimodal Bandits (JYY, SM), pp. 41–48.
ICML-2011-DinuzzoOGP #coordination #kernel #learning
Learning Output Kernels with Block Coordinate Descent (FD, CSO, PVG, GP), pp. 49–56.
ICML-2011-MinhS
Vector-valued Manifold Regularization (HQM, VS), pp. 57–64.
ICML-2011-SugiyamaYKH #clustering #on the #parametricity
On Information-Maximization Clustering: Tuning Parameter Selection and Analytic Solution (MS, MY, MK, HH), pp. 65–72.
ICML-2011-NockMBN #adaptation #on the
On tracking portfolios with certainty equivalents on a generalization of Markowitz model: the Fool, the Wise and the Adaptive (RN, BM, EB, FN), pp. 73–80.
ICML-2011-BabenkoVDB #learning #multi
Multiple Instance Learning with Manifold Bags (BB, NV, PD, SB), pp. 81–88.
ICML-2011-JiangR #feature model
Eigenvalue Sensitive Feature Selection (YJ, JR), pp. 89–96.
ICML-2011-SuSM #classification #multi #naive bayes #scalability #using
Large Scale Text Classification using Semisupervised Multinomial Naive Bayes (JS, JSS, SM), pp. 97–104.
ICML-2011-ChoRI #adaptation #learning #strict
Enhanced Gradient and Adaptive Learning Rate for Training Restricted Boltzmann Machines (KC, TR, AI), pp. 105–112.
ICML-2011-TarlowBKK #coordination
Dynamic Tree Block Coordinate Ascent (DT, DB, PK, VK), pp. 113–120.
ICML-2011-MahoneyO #approximate #implementation
Implementing regularization implicitly via approximate eigenvector computation (MWM, LO), pp. 121–128.
ICML-2011-SocherLNM #natural language #network #parsing #recursion
Parsing Natural Scenes and Natural Language with Recursive Neural Networks (RS, CCYL, AYN, CDM), pp. 129–136.
ICML-2011-ThomasB #markov #process
Conjugate Markov Decision Processes (PST, AGB), pp. 137–144.
ICML-2011-LuB #learning #modelling
Learning Mallows Models with Pairwise Preferences (TL, CB), pp. 145–152.
ICML-2011-Scott #bound #classification
Surrogate losses and regret bounds for cost-sensitive classification with example-dependent costs (CS), pp. 153–160.
ICML-2011-JawanpuriaNR #kernel #learning #performance #using
Efficient Rule Ensemble Learning using Hierarchical Kernels (PJ, JSN, GR), pp. 161–168.
ICML-2011-MartinsFASX #approach
An Augmented Lagrangian Approach to Constrained MAP Inference (AFTM, MATF, PMQA, NAS, EPX), pp. 169–176.
ICML-2011-MannorT #markov #optimisation #process
Mean-Variance Optimization in Markov Decision Processes (SM, JNT), pp. 177–184.
ICML-2011-LiP #clustering #exclamation
Time Series Clustering: Complex is Simpler! (LL, BAP), pp. 185–192.
ICML-2011-Gould #learning #linear #markov #random
Max-margin Learning for Lower Linear Envelope Potentials in Binary Markov Random Fields (SG), pp. 193–200.
ICML-2011-Clark
Inference of Inversion Transduction Grammars (AC), pp. 201–208.
ICML-2011-HuWC #coordination #kernel #learning #named #parametricity #scalability #using
BCDNPKL: Scalable Non-Parametric Kernel Learning Using Block Coordinate Descent (EH, BW, SC), pp. 209–216.
ICML-2011-Maaten #kernel #learning
Learning Discriminative Fisher Kernels (LvdM), pp. 217–224.
ICML-2011-KpotufeL #clustering #nearest neighbour
Pruning nearest neighbor cluster trees (SK, UvL), pp. 225–232.
ICML-2011-ZhaoHJY #online
Online AUC Maximization (PZ, SCHH, RJ, TY), pp. 233–240.
ICML-2011-YueJ
Beat the Mean Bandit (YY, TJ), pp. 241–248.
ICML-2011-OrabonaL #algorithm #kernel #learning #multi #optimisation
Ultra-Fast Optimization Algorithm for Sparse Multi Kernel Learning (FO, JL), pp. 249–256.
ICML-2011-Potetz #linear #problem #using
Estimating the Bayes Point Using Linear Knapsack Problems (BP), pp. 257–264.
ICML-2011-LeNCLPN #learning #on the #optimisation
On optimization methods for deep learning (QVL, JN, AC, AL, BP, AYN), pp. 265–272.
ICML-2011-CrammerG #adaptation #classification #feedback #multi #using
Multiclass Classification with Bandit Feedback using Adaptive Regularization (KC, CG), pp. 273–280.
ICML-2011-HelmboldL #on the
On the Necessity of Irrelevant Variables (DPH, PML), pp. 281–288.
ICML-2011-BarthelmeC #named
ABC-EP: Expectation Propagation for Likelihoodfree Bayesian Computation (SB, NC), pp. 289–296.
ICML-2011-GermainLLMS #approach #kernel
A PAC-Bayes Sample-compression Approach to Kernel Methods (PG, AL, FL, MM, SS), pp. 297–304.
ICML-2011-TamarCM #algorithm
Integrating Partial Model Knowledge in Model Free RL Algorithms (AT, DDC, RM), pp. 305–312.
ICML-2011-JimenezS #performance
Fast Newton-type Methods for Total Variation Regularization (ÁBJ, SS), pp. 313–320.
ICML-2011-BradleyKBG #coordination #parallel
Parallel Coordinate Descent for L1-Regularized Loss Minimization (JKB, AK, DB, CG), pp. 321–328.
ICML-2011-Shalev-ShwartzGS #constraints #rank #scalability
Large-Scale Convex Minimization with a Low-Rank Constraint (SSS, AG, OS), pp. 329–336.
ICML-2011-HannahD #approximate #problem #programming
Approximate Dynamic Programming for Storage Problems (LH, DBD), pp. 337–344.
ICML-2011-JegelkaB #combinator #online
Online Submodular Minimization for Combinatorial Structures (SJ, JAB), pp. 345–352.
ICML-2011-NorouziF
Minimal Loss Hashing for Compact Binary Codes (MN, DJF), pp. 353–360.
ICML-2011-ChenPSDC #analysis #learning #process
The Hierarchical Beta Process for Convolutional Factor Analysis and Deep Learning (BC, GP, GS, DBD, LC), pp. 361–368.
ICML-2011-GuilloryB #learning
Simultaneous Learning and Covering with Adversarial Noise (AG, JAB), pp. 369–376.
ICML-2011-ChenDC #markov #modelling #parametricity #topic
Topic Modeling with Nonparametric Markov Tree (HC, DBD, LC), pp. 377–384.
ICML-2011-KuwadekarN #classification #learning #modelling #relational
Relational Active Learning for Joint Collective Classification Models (AK, JN), pp. 385–392.
ICML-2011-KumarD #approach #clustering #multi
A Co-training Approach for Multi-view Spectral Clustering (AK, HDI), pp. 393–400.
ICML-2011-HarelM #learning #multi
Learning from Multiple Outlooks (MH, SM), pp. 401–408.
ICML-2011-CossalterYZ #adaptation #approximate #kernel #predict #scalability
Adaptive Kernel Approximation for Large-Scale Non-Linear SVM Prediction (MC, RY, LZ), pp. 409–416.
ICML-2011-Garcia-GarciaLS
Risk-Based Generalizations of f-divergences (DGG, UvL, RSR), pp. 417–424.
ICML-2011-QuadriantoL #learning #multi
Learning Multi-View Neighborhood Preserving Projections (NQ, CHL), pp. 425–432.
ICML-2011-OrabonaC #algorithm
Better Algorithms for Selective Sampling (FO, NCB), pp. 433–440.
ICML-2011-RobbianoC #learning #plugin #ranking
Minimax Learning Rates for Bipartite Ranking and Plug-in Rules (SR, SC), pp. 441–448.
ICML-2011-JetchevT #feedback #retrieval #using
Task Space Retrieval Using Inverse Feedback Control (NJ, MT), pp. 449–456.
ICML-2011-VirtanenKK
Bayesian CCA via Group Sparsity (SV, AK, SK), pp. 457–464.
ICML-2011-DeisenrothR #approach #modelling #named #policy
PILCO: A Model-Based and Data-Efficient Approach to Policy Search (MPD, CER), pp. 465–472.
ICML-2011-KarasuyamaT #algorithm
Suboptimal Solution Path Algorithm for Support Vector Machine (MK, IT), pp. 473–480.
ICML-2011-SunGRS #difference #fault #incremental
Incremental Basis Construction from Temporal Difference Error (YS, FJG, MBR, JS), pp. 481–488.
ICML-2011-GerrishB #predict
Predicting Legislative Roll Calls from Text (SG, DMB), pp. 489–496.
ICML-2011-NakajimaSB #automation #on the
On Bayesian PCA: Automatic Dimensionality Selection and Analytic Solution (SN, MS, SDB), pp. 497–504.
ICML-2011-Bylander #learning #linear #multi #polynomial
Learning Linear Functions with Quadratic and Linear Multiplicative Updates (TB), pp. 505–512.
ICML-2011-GlorotBB #adaptation #approach #classification #learning #scalability #sentiment
Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach (XG, AB, YB), pp. 513–520.
ICML-2011-KangGS #learning #multi
Learning with Whom to Share in Multi-task Feature Learning (ZK, KG, FS), pp. 521–528.
ICML-2011-Reyzin #predict
Boosting on a Budget: Sampling for Feature-Efficient Prediction (LR), pp. 529–536.
ICML-2011-IkonomovskaGZD
Speeding-Up Hoeffding-Based Regression Trees With Options (EI, JG, BZ, SD), pp. 537–544.
ICML-2011-MeyerBS #approach #constraints #linear
Linear Regression under Fixed-Rank Constraints: A Riemannian Approach (GM, SB, RS), pp. 545–552.
ICML-2011-LuoDNH #graph
Cauchy Graph Embedding (DL, CHQD, FN, HH), pp. 553–560.
ICML-2011-Gomez-RodriguezBS #network
Uncovering the Temporal Dynamics of Diffusion Networks (MGR, DB, BS), pp. 561–568.
ICML-2011-GaoK #multi
Multiclass Boosting with Hinge Loss based on Output Coding (TG, DK), pp. 569–576.
ICML-2011-JegelkaB11a #approximate #bound #using
Approximation Bounds for Inference using Cooperative Cuts (SJ, JAB), pp. 577–584.
ICML-2011-Hernandez-OralloFR #classification #cost analysis #performance #visualisation
Brier Curves: a New Cost-Based Visualisation of Classifier Performance (JHO, PAF, CFR), pp. 585–592.
ICML-2011-BrouarddS #kernel #predict
Semi-supervised Penalized Output Kernel Regression for Link Prediction (CB, FdB, MS), pp. 593–600.
ICML-2011-NikolenkoS #contest #rating
A New Bayesian Rating System for Team Competitions (SIN, AS), pp. 601–608.
ICML-2011-SujeethLBRCWAOO #domain-specific language #machine learning #named #parallel
OptiML: An Implicitly Parallel Domain-Specific Language for Machine Learning (AKS, HL, KJB, TR, HC, MW, ARA, MO, KO), pp. 609–616.
ICML-2011-ZhuCX #infinity #kernel #process
Infinite SVM: a Dirichlet Process Mixture of Large-margin Kernel Machines (JZ, NC, EPX), pp. 617–624.
ICML-2011-LiZSC #integration #learning #modelling #on the #taxonomy #topic
On the Integration of Topic Modeling and Dictionary Learning (LL, MZ, GS, LC), pp. 625–632.
ICML-2011-MarlinKM #bound #modelling
Piecewise Bounds for Estimating Bernoulli-Logistic Latent Gaussian Models (BMM, MEK, KPM), pp. 633–640.
ICML-2011-UrnerSB #predict
Access to Unlabeled Data can Speed up Prediction Time (RU, SSS, SBD), pp. 641–648.
ICML-2011-RoyLM #bound #polynomial #source code
From PAC-Bayes Bounds to Quadratic Programs for Majority Votes (JFR, FL, MM), pp. 649–656.
ICML-2011-FlachHR #classification #performance
A Coherent Interpretation of AUC as a Measure of Aggregated Classification Performance (PAF, JHO, CFR), pp. 657–664.
ICML-2011-FrancZS #modelling #probability
Support Vector Machines as Probabilistic Models (VF, AZ, BS), pp. 665–672.
ICML-2011-TamuzLBSK #adaptation #kernel #learning
Adaptively Learning the Crowd Kernel (OT, CL, SB, OS, AK), pp. 673–680.
ICML-2011-WellingT #learning #probability
Bayesian Learning via Stochastic Gradient Langevin Dynamics (MW, YWT), pp. 681–688.
ICML-2011-NgiamKKNLN #learning #multimodal
Multimodal Deep Learning (JN, AK, MK, JN, HL, AYN), pp. 689–696.
ICML-2011-KimS #kernel #on the #robust
On the Robustness of Kernel Density M-Estimators (JK, CDS), pp. 697–704.
ICML-2011-RaiD #process
Beam Search based MAP Estimates for the Indian Buffet Process (PR, HDI), pp. 705–712.
ICML-2011-DekelGSX #distributed #online #predict
Optimal Distributed Online Prediction (OD, RGB, OS, LX), pp. 713–720.
ICML-2011-KnowlesGG #algorithm #message passing
Message Passing Algorithms for the Dirichlet Diffusion Tree (DAK, JVG, ZG), pp. 721–728.
ICML-2011-PengHMU #set
Convex Max-Product over Compact Sets for Protein Folding (JP, TH, DAM, RU), pp. 729–736.
ICML-2011-ChakrabortyS #learning
Structure Learning in Ergodic Factored MDPs without Knowledge of the Transition Function’s In-Degree (DC, PS), pp. 737–744.
ICML-2011-HockingVBJ #algorithm #clustering #named #using
Clusterpath: an Algorithm for Clustering using Convex Fusion Penalties (TH, JPV, FRB, AJ), pp. 745–752.
ICML-2011-ShiehHA
Tree preserving embedding (AS, TH, EA), pp. 753–760.
ICML-2011-AroraGKF #clustering #matrix
Clustering by Left-Stochastic Matrix Factorization (RA, MRG, AK, MF), pp. 761–768.
ICML-2011-LiuLC #infinity #policy #representation
The Infinite Regionalized Policy Representation (ML, XL, LC), pp. 769–776.
ICML-2011-WickRBCM #graph #named
SampleRank: Training Factor Graphs with Atomic Gradients (MLW, KR, KB, AC, AM), pp. 777–784.
ICML-2011-ZhangDC #infinity
Tree-Structured Infinite Sparse Factor Model (XZ, DBD, LC), pp. 785–792.
ICML-2011-VattaniCG #personalisation #rank
Preserving Personalized Pagerank in Subgraphs (AV, DC, MG), pp. 793–800.
ICML-2011-XiaoZW #classification #orthogonal
Hierarchical Classification via Orthogonal Transfer (LX, DZ, MW), pp. 801–808.
ICML-2011-NickelTK #learning #multi
A Three-Way Model for Collective Learning on Multi-Relational Data (MN, VT, HPK), pp. 809–816.
ICML-2011-Neumann #policy
Variational Inference for Policy Search in changing situations (GN), pp. 817–824.
ICML-2011-BuffoniCGU #learning #standard
Learning Scoring Functions with Order-Preserving Losses and Standardized Supervision (DB, CC, PG, NU), pp. 825–832.
ICML-2011-RifaiVMGB #feature model
Contractive Auto-Encoders: Explicit Invariance During Feature Extraction (SR, PV, XM, XG, YB), pp. 833–840.
ICML-2011-Lazaro-GredillaT #process
Variational Heteroscedastic Gaussian Process Regression (MLG, MKT), pp. 841–848.
ICML-2011-LiuI #bound #difference #using
Bounding the Partition Function using Holder’s Inequality (QL, ATI), pp. 849–856.
ICML-2011-VuAHS #modelling #network
Dynamic Egocentric Models for Citation Networks (DQV, AUA, DRH, PS), pp. 857–864.
ICML-2011-SmallWBT #learning
The Constrained Weight Space SVM: Learning with Ranked Features (KS, BCW, CEB, TAT), pp. 865–872.
ICML-2011-ChenXCS #matrix #robust
Robust Matrix Completion and Corrupted Columns (YC, HX, CC, SS), pp. 873–880.
ICML-2011-GeramifardDRRH #dependence #online
Online Discovery of Feature Dependencies (AG, FD, JR, NR, JPH), pp. 881–888.
ICML-2011-PaisleyCB #process
Variational Inference for Stick-Breaking Beta Process Priors (JWP, LC, DMB), pp. 889–896.
ICML-2011-BabesMLS #learning #multi
Apprenticeship Learning About Multiple Intentions (MB, VNM, KS, MLL), pp. 897–904.
ICML-2011-Sohl-DicksteinBD #learning #probability
Minimum Probability Flow Learning (JSD, PB, MRD), pp. 905–912.
ICML-2011-DoshiWTR #infinity #network
Infinite Dynamic Bayesian Networks (FD, DW, JBT, NR), pp. 913–920.
ICML-2011-CoatesN #encoding
The Importance of Encoding Versus Training with Sparse Coding and Vector Quantization (AC, AYN), pp. 921–928.
ICML-2011-Cuturi #kernel #performance
Fast Global Alignment Kernels (MC), pp. 929–936.
ICML-2011-BazzaniFLMT #learning #network #policy #recognition #video
Learning attentional policies for tracking and recognition in video with deep networks (LB, NdF, HL, VM, JAT), pp. 937–944.
ICML-2011-DauphinGB #learning #re-engineering #scalability
Large-Scale Learning of Embeddings with Reconstruction Sampling (YD, XG, YB), pp. 945–952.
ICML-2011-ChenWC #automation #composition
Automatic Feature Decomposition for Single View Co-training (MC, KQW, YC), pp. 953–960.
ICML-2011-ShinCK #kernel
Mapping kernels for trees (KS, MC, TK), pp. 961–968.
ICML-2011-MachartPARG #kernel #learning #probability #rank
Stochastic Low-Rank Kernel Learning for Regression (PM, TP, SA, LR, HG), pp. 969–976.
ICML-2011-NaganoKA
Size-constrained Submodular Minimization through Minimum Norm Base (KN, YK, KA), pp. 977–984.
ICML-2011-LadickyT #linear
Locally Linear Support Vector Machines (LL, PHST), pp. 985–992.
ICML-2011-KadriRPDR #functional #kernel
Functional Regularized Least Squares Classication with Operator-valued Kernels (HK, AR, PP, ED, AR), pp. 993–1000.
ICML-2011-JalaliCSX #clustering #graph #optimisation
Clustering Partially Observed Graphs via Convex Optimization (AJ, YC, SS, HX), pp. 1001–1008.
ICML-2011-YangR #learning #on the #using #visual notation
On the Use of Variational Inference for Learning Discrete Graphical Model (EY, PDR), pp. 1009–1016.
ICML-2011-SutskeverMH #generative #network
Generating Text with Recurrent Neural Networks (IS, JM, GEH), pp. 1017–1024.
ICML-2011-AgovicBC #matrix #probability
Probabilistic Matrix Addition (AA, AB, SC), pp. 1025–1032.
ICML-2011-MartensS #learning #network #optimisation
Learning Recurrent Neural Networks with Hessian-Free Optimization (JM, IS), pp. 1033–1040.
ICML-2011-EisensteinAX #generative #modelling
Sparse Additive Generative Models of Text (JE, AA, EPX), pp. 1041–1048.
ICML-2011-GabillonLGS #classification #policy
Classification-based Policy Iteration with a Critic (VG, AL, MG, BS), pp. 1049–1056.
ICML-2011-DasK #algorithm #approximate #set #taxonomy
Submodular meets Spectral: Greedy Algorithms for Subset Selection, Sparse Approximation and Dictionary Selection (AD, DK), pp. 1057–1064.
ICML-2011-ParikhSX #algorithm #modelling #visual notation
A Spectral Algorithm for Latent Tree Graphical Models (APP, LS, EPX), pp. 1065–1072.
ICML-2011-GuanDJ #feature model #probability
A Unified Probabilistic Model for Global and Local Unsupervised Feature Selection (YG, JGD, MIJ), pp. 1073–1080.
ICML-2011-LiZ #towards
Towards Making Unlabeled Data Never Hurt (YFL, ZHZ), pp. 1081–1088.
ICML-2011-SaxeKCBSN #learning #on the #random
On Random Weights and Unsupervised Feature Learning (AMS, PWK, ZC, MB, BS, AYN), pp. 1089–1096.
ICML-2011-DudikLL #evaluation #learning #policy #robust
Doubly Robust Policy Evaluation and Learning (MD, JL, LL), pp. 1097–1104.
ICML-2011-NgiamCKN #energy #learning #modelling
Learning Deep Energy Models (JN, ZC, PWK, AYN), pp. 1105–1112.
ICML-2011-KotlowskiDH #ranking
Bipartite Ranking through Minimization of Univariate Loss (WK, KD, EH), pp. 1113–1120.
ICML-2011-LeeW #identification #learning #online #probability
Manifold Identification of Dual Averaging Methods for Regularized Stochastic Online Learning (SL, SJW), pp. 1121–1128.
ICML-2011-AgarwalNW #composition #matrix
Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions (AA, SN, MJW), pp. 1129–1136.
ICML-2011-VainsencherDM #estimation #online
Bundle Selling by Online Estimation of Valuation Functions (DV, OD, SM), pp. 1137–1144.
ICML-2011-CourvilleBB #image #modelling
Unsupervised Models of Images by Spikeand-Slab RBMs (ACC, JB, YB), pp. 1145–1152.
ICML-2011-KamisettyXL #approximate #correlation #using
Approximating Correlated Equilibria using Relaxations on the Marginal Polytope (HK, EPX, CJL), pp. 1153–1160.
ICML-2011-YanRFD #learning
Active Learning from Crowds (YY, RR, GF, JGD), pp. 1161–1168.
ICML-2011-WaughZB #equilibrium #problem
Computational Rationalization: The Inverse Equilibrium Problem (KW, BDZ, DB), pp. 1169–1176.
ICML-2011-GhavamzadehLMH #analysis
Finite-Sample Analysis of Lasso-TD (MG, AL, RM, MWH), pp. 1177–1184.
ICML-2011-PazisP #scalability #set
Generalized Value Functions for Large Action Sets (JP, RP), pp. 1185–1192.
ICML-2011-KuleszaT #named #process
k-DPPs: Fixed-Size Determinantal Point Processes (AK, BT), pp. 1193–1200.
ICML-2011-SwerskyRBMF #energy #modelling #on the
On Autoencoders and Score Matching for Energy Based Models (KS, MR, DB, BMM, NdF), pp. 1201–1208.
ICML-2011-GrubbB #algorithm #optimisation
Generalized Boosting Algorithms for Convex Optimization (AG, DB), pp. 1209–1216.

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.