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

@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)

40 ×#learning
15 ×#kernel
14 ×#modelling
14 ×#multi
10 ×#classification
10 ×#on the
10 ×#using
9 ×#algorithm
8 ×#named
8 ×#process

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.