Johannes Fürnkranz, Thorsten Joachims
Proceedings of the 27th International Conference on Machine Learning
ICML, 2010.

@proceedings{ICML-2010,
	address       = "Haifa, Israel",
	editor        = "Johannes Fürnkranz and Thorsten Joachims",
	publisher     = "{Omnipress}",
	title         = "{Proceedings of the 27th International Conference on Machine Learning}",
	year          = 2010,
}

Contents (159 items)

55 ×#learning
23 ×#multi
13 ×#modelling
12 ×#process
10 ×#performance
9 ×#algorithm
9 ×#clustering
9 ×#using
8 ×#analysis
8 ×#approximate

ICML-2010-Apte #machine learning #optimisation

The Role of Machine Learning in Business Optimization (CA), pp. 1–2.

ICML-2010-CumminsN #named #recognition #using #visual notation

FAB-MAP: Appearance-Based Place Recognition and Mapping using a Learned Visual Vocabulary Model (MJC, PMN), pp. 3–10.

ICML-2010-FelzenszwalbGMR #detection #modelling

Discriminative Latent Variable Models for Object Detection (PFF, RBG, DAM, DR), pp. 11–12.

ICML-2010-GraepelCBH #predict

Web-Scale Bayesian Click-Through rate Prediction for Sponsored Search Advertising in Microsoft’s Bing Search Engine (TG, JQC, TB, RH), pp. 13–20.

ICML-2010-Raphael #machine learning #music

Music Plus One and Machine Learning (CR), pp. 21–28.

ICML-2010-SnyderB #learning #multi

Climbing the Tower of Babel: Unsupervised Multilingual Learning (BS, RB), pp. 29–36.

ICML-2010-XuHFPJ #detection #mining #problem #scalability

Detecting Large-Scale System Problems by Mining Console Logs (WX, LH, AF, DAP, MIJ), pp. 37–46.

ICML-2010-AsuncionLIS

Particle Filtered MCMC-MLE with Connections to Contrastive Divergence (AUA, QL, ATI, PS), pp. 47–54.

ICML-2010-BardenetK #algorithm #optimisation

Surrogating the surrogate: accelerating Gaussian-process-based global optimization with a mixture cross-entropy algorithm (RB, BK), pp. 55–62.

ICML-2010-BartlettPW #constant #memory management #process

Forgetting Counts: Constant Memory Inference for a Dependent Hierarchical Pitman-Yor Process (NB, DP, FW), pp. 63–70.

ICML-2010-BhadraBBB #kernel #matrix #nondeterminism #robust

Robust Formulations for Handling Uncertainty in Kernel Matrices (SB, SB, CB, ABT), pp. 71–78.

ICML-2010-BilgicMG #learning

Active Learning for Networked Data (MB, LM, LG), pp. 79–86.

ICML-2010-BleiF #distance #process

Distance dependent Chinese restaurant processes (DMB, PIF), pp. 87–94.

ICML-2010-BontempiM #array

Causal filter selection in microarray data (GB, PEM), pp. 95–102.

ICML-2010-BordesUW #ambiguity #learning #ranking #semantics

Label Ranking under Ambiguous Supervision for Learning Semantic Correspondences (AB, NU, JW), pp. 103–110.

ICML-2010-BoureauPL #analysis #recognition #visual notation

A Theoretical Analysis of Feature Pooling in Visual Recognition (YLB, JP, YL), pp. 111–118.

ICML-2010-BouzyM #game studies #learning #matrix #multi

Multi-agent Learning Experiments on Repeated Matrix Games (BB, MM), pp. 119–126.

ICML-2010-BradleyG #learning #random

Learning Tree Conditional Random Fields (JKB, CG), pp. 127–134.

ICML-2010-BshoutyL #clustering #linear #using

Finding Planted Partitions in Nearly Linear Time using Arrested Spectral Clustering (NHB, PML), pp. 135–142.

ICML-2010-Busa-FeketeK #performance #using

Fast boosting using adversarial bandits (RBF, BK), pp. 143–150.

ICML-2010-CaniniSG #categorisation #learning #modelling #process

Modeling Transfer Learning in Human Categorization with the Hierarchical Dirichlet Process (KRC, MMS, TLG), pp. 151–158.

ICML-2010-CaoLY #learning #multi #predict

Transfer Learning for Collective Link Prediction in Multiple Heterogenous Domains (BC, NNL, QY), pp. 159–166.

ICML-2010-Carreira-Perpinan #algorithm #reduction

The Elastic Embedding Algorithm for Dimensionality Reduction (MÁCP), pp. 167–174.

ICML-2010-Cesa-BianchiGVZ #graph #predict #random

Random Spanning Trees and the Prediction of Weighted Graphs (NCB, CG, FV, GZ), pp. 175–182.

ICML-2010-Cesa-BianchiSS #learning #performance

Efficient Learning with Partially Observed Attributes (NCB, SSS, OS), pp. 183–190.

ICML-2010-ChakrabortyS #convergence #learning #multi #safety

Convergence, Targeted Optimality, and Safety in Multiagent Learning (DC, PS), pp. 191–198.

ICML-2010-ChangSGR #learning

Structured Output Learning with Indirect Supervision (MWC, VS, DG, DR), pp. 199–206.

ICML-2010-ChenW #modelling

Dynamical Products of Experts for Modeling Financial Time Series (YC, MW), pp. 207–214.

ICML-2010-ChengDH #ranking

Label Ranking Methods based on the Plackett-Luce Model (WC, KD, EH), pp. 215–222.

ICML-2010-ChengDH10a #classification #multi

Graded Multilabel Classification: The Ordinal Case (WC, KD, EH), pp. 223–230.

ICML-2010-CoenAF #clustering

Comparing Clusterings in Space (MHC, MHA, NF), pp. 231–238.

ICML-2010-CortesMR #algorithm #kernel #learning

Two-Stage Learning Kernel Algorithms (CC, MM, AR), pp. 239–246.

ICML-2010-CortesMR10a #bound #kernel #learning

Generalization Bounds for Learning Kernels (CC, MM, AR), pp. 247–254.

ICML-2010-CostaG #distance #kernel #performance

Fast Neighborhood Subgraph Pairwise Distance Kernel (FC, KDG), pp. 255–262.

ICML-2010-DasguptaN #clustering #mining

Mining Clustering Dimensions (SD, VN), pp. 263–270.

ICML-2010-DavisD #bottom-up #learning #markov #network

Bottom-Up Learning of Markov Network Structure (JD, PMD), pp. 271–278.

ICML-2010-DembczynskiCH #classification #multi #probability

Bayes Optimal Multilabel Classification via Probabilistic Classifier Chains (KD, WC, EH), pp. 279–286.

ICML-2010-DeselaersF #learning #multi #random

A Conditional Random Field for Multiple-Instance Learning (TD, VF), pp. 287–294.

ICML-2010-DillonBL #analysis #generative #learning

Asymptotic Analysis of Generative Semi-Supervised Learning (JVD, KB, GL), pp. 295–302.

ICML-2010-DondelingerLH #flexibility #information management #network

Heterogeneous Continuous Dynamic Bayesian Networks with Flexible Structure and Inter-Time Segment Information Sharing (FD, SL, DH), pp. 303–310.

ICML-2010-DowneyS #adaptation #difference

Temporal Difference Bayesian Model Averaging: A Bayesian Perspective on Adapting λ (CD, SS), pp. 311–318.

ICML-2010-DruckM #generative #learning #modelling #using

High-Performance Semi-Supervised Learning using Discriminatively Constrained Generative Models (GD, AM), pp. 319–326.

ICML-2010-DuchiMJ #algorithm #consistency #on the #ranking

On the Consistency of Ranking Algorithms (JCD, LWM, MIJ), pp. 327–334.

ICML-2010-KrishnamurthyT

Inverse Optimal Control with Linearly-Solvable MDPs (KD, ET), pp. 335–342.

ICML-2010-El-HayCFK

Continuous-Time Belief Propagation (TEH, IC, NF, RK), pp. 343–350.

ICML-2010-FaivishevskyG #algorithm #clustering #parametricity

Nonparametric Information Theoretic Clustering Algorithm (LF, JG), pp. 351–358.

ICML-2010-GaudelS #feature model #game studies

Feature Selection as a One-Player Game (RG, MS), pp. 359–366.

ICML-2010-GavishNC #graph #learning #multi #theory and practice

Multiscale Wavelets on Trees, Graphs and High Dimensional Data: Theory and Applications to Semi Supervised Learning (MG, BN, RRC), pp. 367–374.

ICML-2010-GerrishB #approach

A Language-based Approach to Measuring Scholarly Impact (SG, DMB), pp. 375–382.

ICML-2010-GoldbergE #classification

Boosting Classifiers with Tightened L0-Relaxation Penalties (NG, JE), pp. 383–390.

ICML-2010-GomesK #data type #learning #parametricity

Budgeted Nonparametric Learning from Data Streams (RG, AK), pp. 391–398.

ICML-2010-GregorL #approximate #learning #performance

Learning Fast Approximations of Sparse Coding (KG, YL), pp. 399–406.

ICML-2010-GrubbB #composition #learning #network

Boosted Backpropagation Learning for Training Deep Modular Networks (AG, JAB), pp. 407–414.

ICML-2010-GuilloryB #interactive #set

Interactive Submodular Set Cover (AG, JAB), pp. 415–422.

ICML-2010-HariharanZVV #classification #multi #scalability

Large Scale Max-Margin Multi-Label Classification with Priors (BH, LZM, SVNV, MV), pp. 423–430.

ICML-2010-HarpaleY #adaptation #learning #multi

Active Learning for Multi-Task Adaptive Filtering (AH, YY), pp. 431–438.

ICML-2010-HoffmanBC #matrix #music #parametricity

Bayesian Nonparametric Matrix Factorization for Recorded Music (MDH, DMB, PRC), pp. 439–446.

ICML-2010-HonorioS #learning #modelling #multi #visual notation

Multi-Task Learning of Gaussian Graphical Models (JH, DS), pp. 447–454.

ICML-2010-HuangG #independence #learning #ranking

Learning Hierarchical Riffle Independent Groupings from Rankings (JH, CG), pp. 455–462.

ICML-2010-HueV #kernel #learning #on the

On learning with kernels for unordered pairs (MH, JPV), pp. 463–470.

ICML-2010-JaggiS #algorithm #problem

A Simple Algorithm for Nuclear Norm Regularized Problems (MJ, MS), pp. 471–478.

ICML-2010-JanzingHS

Telling cause from effect based on high-dimensional observations (DJ, POH, BS), pp. 479–486.

ICML-2010-JenattonMOB #learning #taxonomy

Proximal Methods for Sparse Hierarchical Dictionary Learning (RJ, JM, GO, FRB), pp. 487–494.

ICML-2010-JiXYY #3d #network #recognition

3D Convolutional Neural Networks for Human Action Recognition (SJ, WX, MY, KY), pp. 495–502.

ICML-2010-JojicGK #composition

Accelerated dual decomposition for MAP inference (VJ, SG, DK), pp. 503–510.

ICML-2010-KalyanakrishnanS #multi #performance #theory and practice

Efficient Selection of Multiple Bandit Arms: Theory and Practice (SK, PS), pp. 511–518.

ICML-2010-KimSD #algorithm #scalability

A scalable trust-region algorithm with application to mixed-norm regression (DK, SS, ISD), pp. 519–526.

ICML-2010-KimT

Local Minima Embedding (MK, FDlT), pp. 527–534.

ICML-2010-KimT10a #learning #multi #process

Gaussian Processes Multiple Instance Learning (MK, FDlT), pp. 535–542.

ICML-2010-KimX #multi

Tree-Guided Group Lasso for Multi-Task Regression with Structured Sparsity (SK, EPX), pp. 543–550.

ICML-2010-KokD #learning #logic #markov #network #using

Learning Markov Logic Networks Using Structural Motifs (SK, PMD), pp. 551–558.

ICML-2010-KolarPX #on the #parametricity

On Sparse Nonparametric Conditional Covariance Selection (MK, APP, EPX), pp. 559–566.

ICML-2010-KrauseC #representation #taxonomy

Submodular Dictionary Selection for Sparse Representation (AK, VC), pp. 567–574.

ICML-2010-KulisB #learning #online

Implicit Online Learning (BK, PLB), pp. 575–582.

ICML-2010-LangT #probability #reasoning #relational

Probabilistic Backward and Forward Reasoning in Stochastic Relational Worlds (TL, MT), pp. 583–590.

ICML-2010-LayB #classification #predict #using

Supervised Aggregation of Classifiers using Artificial Prediction Markets (NL, AB), pp. 591–598.

ICML-2010-LazaricG #learning #multi

Bayesian Multi-Task Reinforcement Learning (AL, MG), pp. 599–606.

ICML-2010-LazaricGM #algorithm #analysis #classification #policy

Analysis of a Classification-based Policy Iteration Algorithm (AL, MG, RM), pp. 607–614.

ICML-2010-LazaricGM10a #analysis

Finite-Sample Analysis of LSTD (AL, MG, RM), pp. 615–622.

ICML-2010-RouxF #performance

A fast natural Newton method (NLR, AWF), pp. 623–630.

ICML-2010-LiKL #approximate #scalability

Making Large-Scale Nyström Approximation Possible (ML, JTK, BLL), pp. 631–638.

ICML-2010-LiangJK #approach #learning #source code

Learning Programs: A Hierarchical Bayesian Approach (PL, MIJ, DK), pp. 639–646.

ICML-2010-LiangS #interactive #learning #multi #on the

On the Interaction between Norm and Dimensionality: Multiple Regimes in Learning (PL, NS), pp. 647–654.

ICML-2010-LinC #clustering

Power Iteration Clustering (FL, WWC), pp. 655–662.

ICML-2010-LiuLY #rank #representation #robust #segmentation

Robust Subspace Segmentation by Low-Rank Representation (GL, ZL, YY), pp. 663–670.

ICML-2010-LiuY #graph #robust

Robust Graph Mode Seeking by Graph Shift (HL, SY), pp. 671–678.

ICML-2010-LiuHC #graph #learning #scalability

Large Graph Construction for Scalable Semi-Supervised Learning (WL, JH, SFC), pp. 679–686.

ICML-2010-LiuNLL #analysis #graph #learning #relational

Learning Temporal Causal Graphs for Relational Time-Series Analysis (YL, ANM, ACL, YL), pp. 687–694.

ICML-2010-LizotteBM #analysis #learning #multi #performance #random

Efficient Reinforcement Learning with Multiple Reward Functions for Randomized Controlled Trial Analysis (DJL, MHB, SAM), pp. 695–702.

ICML-2010-LongS #approximate #simulation #strict

Restricted Boltzmann Machines are Hard to Approximately Evaluate or Simulate (PML, RAS), pp. 703–710.

ICML-2010-MackeyWJ #matrix

Mixed Membership Matrix Factorization (LWM, DJW, MIJ), pp. 711–718.

ICML-2010-MaeiSBS #approximate #learning #towards

Toward Off-Policy Learning Control with Function Approximation (HRM, CS, SB, RSS), pp. 719–726.

ICML-2010-Mahmud #learning

Constructing States for Reinforcement Learning (MMHM), pp. 727–734.

ICML-2010-Martens #learning #optimisation

Deep learning via Hessian-free optimization (JM), pp. 735–742.

ICML-2010-Martens10a #learning #linear

Learning the Linear Dynamical System with ASOS (JM), pp. 743–750.

ICML-2010-MasaeliFD #feature model #reduction

From Transformation-Based Dimensionality Reduction to Feature Selection (MM, GF, JGD), pp. 751–758.

ICML-2010-Masnadi-ShiraziV #elicitation #probability

Risk minimization, probability elicitation, and cost-sensitive SVMs (HMS, NV), pp. 759–766.

ICML-2010-McAuleyC #performance

Exploiting Data-Independence for Fast Belief-Propagation (JJM, TSC), pp. 767–774.

ICML-2010-McFeeL #learning #metric #rank

Metric Learning to Rank (BM, GRGL), pp. 775–782.

ICML-2010-MeshiSJG #approximate #learning

Learning Efficiently with Approximate Inference via Dual Losses (OM, DS, TSJ, AG), pp. 783–790.

ICML-2010-MinMYBZ

Deep Supervised t-Distributed Embedding (MRM, LvdM, ZY, AJB, ZZ), pp. 791–798.

ICML-2010-MorimuraSKHT #approximate #learning #parametricity

Nonparametric Return Distribution Approximation for Reinforcement Learning (TM, MS, HK, HH, TT), pp. 799–806.

ICML-2010-NairH #linear #strict

Rectified Linear Units Improve Restricted Boltzmann Machines (VN, GEH), pp. 807–814.

ICML-2010-NakajimaS #matrix

Implicit Regularization in Variational Bayesian Matrix Factorization (SN, MS), pp. 815–822.

ICML-2010-NegahbanW #estimation #matrix #rank #scalability

Estimation of (near) low-rank matrices with noise and high-dimensional scaling (SN, MJW), pp. 823–830.

ICML-2010-NiuDJ #clustering #multi

Multiple Non-Redundant Spectral Clustering Views (DN, JGD, MIJ), pp. 831–838.

ICML-2010-OntanonP #approach #induction #learning #multi

Multiagent Inductive Learning: an Argumentation-based Approach (SO, EP), pp. 839–846.

ICML-2010-PaisleyZWGC #process

A Stick-Breaking Construction of the Beta Process (JWP, AKZ, CWW, GSG, LC), pp. 847–854.

ICML-2010-PanagiotakopoulosT

The Margin Perceptron with Unlearning (CP, PT), pp. 855–862.

ICML-2010-PardoeS

Boosting for Regression Transfer (DP, PS), pp. 863–870.

ICML-2010-PetrikTPZ #approximate #feature model #linear #markov #process #source code #using

Feature Selection Using Regularization in Approximate Linear Programs for Markov Decision Processes (MP, GT, RP, SZ), pp. 871–878.

ICML-2010-LiPSG #learning #parametricity

Budgeted Distribution Learning of Belief Net Parameters (LL, BP, CS, RG), pp. 879–886.

ICML-2010-PoonZCW #clustering #modelling

Variable Selection in Model-Based Clustering: To Do or To Facilitate (LKMP, NLZ, TC, YW), pp. 887–894.

ICML-2010-DinculescuP #approximate #predict

Approximate Predictive Representations of Partially Observable Systems (MD, DP), pp. 895–902.

ICML-2010-ReisingerWSM #modelling #topic

Spherical Topic Models (JR, AW, BS, RJM), pp. 903–910.

ICML-2010-Ruping #classification #estimation

SVM Classifier Estimation from Group Probabilities (SR), pp. 911–918.

ICML-2010-Ryabko #clustering #process

Clustering processes (DR), pp. 919–926.

ICML-2010-SaatciTR #modelling #process

Gaussian Process Change Point Models (YS, RDT, CER), pp. 927–934.

ICML-2010-SakumaA #online #predict #privacy

Online Prediction with Privacy (JS, HA), pp. 935–942.

ICML-2010-Salakhutdinov #adaptation #learning #using

Learning Deep Boltzmann Machines using Adaptive MCMC (RS), pp. 943–950.

ICML-2010-SawadeLBS #estimation

Active Risk Estimation (CS, NL, SB, TS), pp. 951–958.

ICML-2010-Scherrer #difference #fixpoint #perspective

Should one compute the Temporal Difference fix point or minimize the Bellman Residual? The unified oblique projection view (BS), pp. 959–966.

ICML-2010-Seeger #scalability

Gaussian Covariance and Scalable Variational Inference (MWS), pp. 967–974.

ICML-2010-ShoebG #detection #machine learning

Application of Machine Learning To Epileptic Seizure Detection (AHS, JVG), pp. 975–982.

ICML-2010-SlivkinsRG #documentation #learning #ranking #scalability

Learning optimally diverse rankings over large document collections (AS, FR, SG), pp. 983–990.

ICML-2010-SongSGS #markov #modelling

Hilbert Space Embeddings of Hidden Markov Models (LS, BB, SMS, GJG, AJS), pp. 991–998.

ICML-2010-SonnenburgF #framework #linear #named

COFFIN: A Computational Framework for Linear SVMs (SS, VF), pp. 999–1006.

ICML-2010-SorgSL #bound

Internal Rewards Mitigate Agent Boundedness (JS, SPS, RLL), pp. 1007–1014.

ICML-2010-SrinivasKKS #design #optimisation #process

Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design (NS, AK, SK, MWS), pp. 1015–1022.

ICML-2010-SyedR #dataset #identification

Unsupervised Risk Stratification in Clinical Datasets: Identifying Patients at Risk of Rare Outcomes (ZS, IR), pp. 1023–1030.

ICML-2010-SzitaS #bound #complexity #learning #modelling

Model-based reinforcement learning with nearly tight exploration complexity bounds (IS, CS), pp. 1031–1038.

ICML-2010-SzlamB

Total Variation, Cheeger Cuts (AS, XB), pp. 1039–1046.

ICML-2010-TanWT #dataset #feature model #learning

Learning Sparse SVM for Feature Selection on Very High Dimensional Datasets (MT, LW, IWT), pp. 1047–1054.

ICML-2010-TangE #network #recognition #robust #visual notation

Deep networks for robust visual recognition (YT, CE), pp. 1055–1062.

ICML-2010-ThiaoTA #approach #problem #programming

A DC Programming Approach for Sparse Eigenvalue Problem (MT, PDT, LTHA), pp. 1063–1070.

ICML-2010-ThieryS #policy #problem #trade-off

Least-Squares Policy Iteration: Bias-Variance Trade-off in Control Problems (CT, BS), pp. 1071–1078.

ICML-2010-TingHJ #analysis #convergence #graph

An Analysis of the Convergence of Graph Laplacians (DT, LH, MIJ), pp. 1079–1086.

ICML-2010-TomiokaSSK #algorithm #learning #matrix #performance #rank

A Fast Augmented Lagrangian Algorithm for Learning Low-Rank Matrices (RT, TS, MS, HK), pp. 1087–1094.

ICML-2010-TuL #classification #multi

One-sided Support Vector Regression for Multiclass Cost-sensitive Classification (HHT, HTL), pp. 1095–1102.

ICML-2010-VickreyLK

Non-Local Contrastive Objectives (DV, CCYL, DK), pp. 1103–1110.

ICML-2010-VogtPFR #clustering #distance #invariant #process

The Translation-invariant Wishart-Dirichlet Process for Clustering Distance Data (JEV, SP, TJF, VR), pp. 1111–1118.

ICML-2010-WalshSLD #learning

Generalizing Apprenticeship Learning across Hypothesis Classes (TJW, KS, MLL, CD), pp. 1119–1126.

ICML-2010-WangKC #learning

Sequential Projection Learning for Hashing with Compact Codes (JW, SK, SFC), pp. 1127–1134.

ICML-2010-WangZ #analysis

A New Analysis of Co-Training (WW, ZHZ), pp. 1135–1142.

ICML-2010-WangCV #multi

Multi-Class Pegasos on a Budget (ZW, KC, SV), pp. 1143–1150.

ICML-2010-WilliamsonWHB #modelling #process #topic

The IBP Compound Dirichlet Process and its Application to Focused Topic Modeling (SW, CW, KAH, DMB), pp. 1151–1158.

ICML-2010-WuYWD #feature model #online #streaming

Online Streaming Feature Selection (XW, KY, HW, WD), pp. 1159–1166.

ICML-2010-WunderLB #multi

Classes of Multiagent Q-learning Dynamics with epsilon-greedy Exploration (MW, MLL, MB), pp. 1167–1174.

ICML-2010-XuJYKL #kernel #learning #multi #performance

Simple and Efficient Multiple Kernel Learning by Group Lasso (ZX, RJ, HY, IK, MRL), pp. 1175–1182.

ICML-2010-YanQ #process

Sparse Gaussian Process Regression via L1 Penalization (FY, Y(Q), pp. 1183–1190.

ICML-2010-YangXKL #learning #online

Online Learning for Group Lasso (HY, ZX, IK, MRL), pp. 1191–1198.

ICML-2010-YangJJ #learning

Learning from Noisy Side Information by Generalized Maximum Entropy Model (TY, RJ, AKJ), pp. 1199–1206.

ICML-2010-Yu #convergence #difference

Convergence of Least Squares Temporal Difference Methods Under General Conditions (HY), pp. 1207–1214.

ICML-2010-YuZ #coordination #using

Improved Local Coordinate Coding using Local Tangents (KY, TZ), pp. 1215–1222.

ICML-2010-ZhangS #reduction

Projection Penalties: Dimension Reduction without Loss (YZ, JGS), pp. 1223–1230.

ICML-2010-ZhaoH #framework #learning #named #online

OTL: A Framework of Online Transfer Learning (PZ, SCHH), pp. 1231–1238.

ICML-2010-ZhuX #random #topic

Conditional Topic Random Fields (JZ, EPX), pp. 1239–1246.

ICML-2010-ZhuGJRHK #learning #modelling

Cognitive Models of Test-Item Effects in Human Category Learning (XZ, BRG, KSJ, TTR, JH, CK), pp. 1247–1254.

ICML-2010-ZiebartBD #interactive #modelling #principle

Modeling Interaction via the Principle of Maximum Causal Entropy (BDZ, JAB, AKD), pp. 1255–1262.