Andrea Pohoreckyj Danyluk, Léon Bottou, Michael L. Littman
Proceedings of the 26th International Conference on Machine Learning
ICML, 2009.

@proceedings{ICML-2009,
	address       = "Montreal, Quebec, Canada",
	editor        = "Andrea Pohoreckyj Danyluk and Léon Bottou and Michael L. Littman",
	isbn          = "978-1-60558-516-1",
	publisher     = "{ACM}",
	series        = "{ACM International Conference Proceeding Series}",
	title         = "{Proceedings of the 26th International Conference on Machine Learning}",
	volume        = 382,
	year          = 2009,
}

Contents (170 items)

66 ×#learning
19 ×#modelling
16 ×#multi
10 ×#performance
10 ×#predict
10 ×#scalability
10 ×#using
9 ×#summary
9 ×#tutorial
8 ×#classification

ICML-2009-AdamsG #learning #named #parametricity

Archipelago: nonparametric Bayesian semi-supervised learning (RPA, ZG), pp. 1–8.

ICML-2009-Cortes #kernel #learning #performance #question

Invited talk: Can learning kernels help performance? (CC), p. 1.

ICML-2009-Freund #game studies #learning #online

Invited talk: Drifting games, boosting and online learning (YF), p. 2.

ICML-2009-AdamsMM #parametricity #process

Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities (RPA, IM, DJCM), pp. 9–16.

ICML-2009-BeygelzimerLZ #machine learning #reduction #summary #tutorial

Tutorial summary: Reductions in machine learning (AB, JL, BZ), p. 12.

ICML-2009-Even-DarM #convergence #summary #tutorial

Tutorial summary: Convergence of natural dynamics to equilibria (EED, VSM), p. 13.

ICML-2009-TrespY #dependence #learning #summary #tutorial

Tutorial summary: Learning with dependencies between several response variables (VT, KY), p. 14.

ICML-2009-WarmuthV #optimisation #overview #perspective #summary #tutorial

Tutorial summary: Survey of boosting from an optimization perspective (MKW, SVNV), p. 15.

ICML-2009-Niv #learning #summary #tutorial

Tutorial summary: The neuroscience of reinforcement learning (YN), p. 16.

ICML-2009-AiolliMS #kernel

Route kernels for trees (FA, GDSM, AS), pp. 17–24.

ICML-2009-BennettBC #information retrieval #machine learning #summary #tutorial

Tutorial summary: Machine learning in IR: recent successes and new opportunities (PNB, MB, KCT), p. 17.

ICML-2009-DasguptaL #learning #summary #tutorial

Tutorial summary: Active learning (SD, JL), p. 18.

ICML-2009-Leskovec #ml #network #scalability #social #summary #tutorial

Tutorial summary: Large social and information networks: opportunities for ML (JL), p. 19.

ICML-2009-Smith #natural language #predict #summary #tutorial

Tutorial summary: Structured prediction for natural language processing (NAS), p. 20.

ICML-2009-AndrzejewskiZC #modelling #topic

Incorporating domain knowledge into topic modeling via Dirichlet Forest priors (DA, XZ, MC), pp. 25–32.

ICML-2009-BaillyDR #analysis #component #grammar inference #problem

Grammatical inference as a principal component analysis problem (RB, FD, LR), pp. 33–40.

ICML-2009-BengioLCW #education #learning

Curriculum learning (YB, JL, RC, JW), pp. 41–48.

ICML-2009-BeygelzimerDL #learning

Importance weighted active learning (AB, SD, JL), pp. 49–56.

ICML-2009-BouchardZ

Split variational inference (GB, OZ), pp. 57–64.

ICML-2009-BoulariasC #policy #predict

Predictive representations for policy gradient in POMDPs (AB, BCd), pp. 65–72.

ICML-2009-BoutilierRV #elicitation #interactive #online #optimisation

Online feature elicitation in interactive optimization (CB, KR, PV), pp. 73–80.

ICML-2009-BuhlerH #clustering #graph

Spectral clustering based on the graph p-Laplacian (TB, MH), pp. 81–88.

ICML-2009-BurlW #learning

Active learning for directed exploration of complex systems (MCB, EW), pp. 89–96.

ICML-2009-BusettoOB

Optimized expected information gain for nonlinear dynamical systems (AGB, CSO, JMB), pp. 97–104.

ICML-2009-CaiWH #consistency #data analysis #probability

Probabilistic dyadic data analysis with local and global consistency (DC, XW, XH), pp. 105–112.

ICML-2009-CamposZJ #constraints #learning #network #using

Structure learning of Bayesian networks using constraints (CPdC, ZZ, QJ), pp. 113–120.

ICML-2009-Cesa-BianchiGO #bound #classification #robust

Robust bounds for classification via selective sampling (NCB, CG, FO), pp. 121–128.

ICML-2009-ChaudhuriKLS #analysis #canonical #clustering #correlation #multi

Multi-view clustering via canonical correlation analysis (KC, SMK, KL, KS), pp. 129–136.

ICML-2009-ChenTLY #learning #multi

A convex formulation for learning shared structures from multiple tasks (JC, LT, JL, JY), pp. 137–144.

ICML-2009-ChenGR #kernel #learning

Learning kernels from indefinite similarities (YC, MRG, BR), pp. 145–152.

ICML-2009-ChengSS #markov #matrix #modelling

Matrix updates for perceptron training of continuous density hidden Markov models (CCC, FS, LKS), pp. 153–160.

ICML-2009-ChengHH #learning #ranking

Decision tree and instance-based learning for label ranking (WC, JCH, EH), pp. 161–168.

ICML-2009-ChoS #analysis #learning #modelling

Learning dictionaries of stable autoregressive models for audio scene analysis (YC, LKS), pp. 169–176.

ICML-2009-ChoiCW #markov #modelling #multi

Exploiting sparse Markov and covariance structure in multiresolution models (MJC, VC, ASW), pp. 177–184.

ICML-2009-ClemenconV #estimation #parametricity

Nonparametric estimation of the precision-recall curve (SC, NV), pp. 185–192.

ICML-2009-DaiJXYY #framework #learning #named

EigenTransfer: a unified framework for transfer learning (WD, OJ, GRX, QY, YY), pp. 193–200.

ICML-2009-DaitchKS #graph

Fitting a graph to vector data (SID, JAK, DAS), pp. 201–208.

ICML-2009-Daume #predict #search-based

Unsupervised search-based structured prediction (HDI), pp. 209–216.

ICML-2009-DavisD #higher-order #logic #markov

Deep transfer via second-order Markov logic (JD, PMD), pp. 217–224.

ICML-2009-DeisenrothHH #process

Analytic moment-based Gaussian process filtering (MPD, MFH, UDH), pp. 225–232.

ICML-2009-DekelS #education

Good learners for evil teachers (OD, OS), pp. 233–240.

ICML-2009-DeodharGGCD #clustering #framework #scalability #semistructured data

A scalable framework for discovering coherent co-clusters in noisy data (MD, GG, JG, HC, ISD), pp. 241–248.

ICML-2009-DiukLL #adaptation #feature model #learning #problem

The adaptive k-meteorologists problem and its application to structure learning and feature selection in reinforcement learning (CD, LL, BRL), pp. 249–256.

ICML-2009-DoLF #learning #online

Proximal regularization for online and batch learning (CBD, QVL, CSF), pp. 257–264.

ICML-2009-DoA #markov #modelling #scalability

Large margin training for hidden Markov models with partially observed states (TMTD, TA), pp. 265–272.

ICML-2009-Doshi-VelezG #process

Accelerated sampling for the Indian Buffet Process (FDV, ZG), pp. 273–280.

ICML-2009-DoyleE #modelling #topic

Accounting for burstiness in topic models (GD, CE), pp. 281–288.

ICML-2009-DuanTXC #adaptation #classification #multi

Domain adaptation from multiple sources via auxiliary classifiers (LD, IWT, DX, TSC), pp. 289–296.

ICML-2009-DuchiS

Boosting with structural sparsity (JCD, YS), pp. 297–304.

ICML-2009-FarhangfarGS #image #learning

Learning to segment from a few well-selected training images (AF, RG, CS), pp. 305–312.

ICML-2009-FloresGMP

GAODE and HAODE: two proposals based on AODE to deal with continuous variables (MJF, JAG, AMM, JMP), pp. 313–320.

ICML-2009-FooDN #algorithm #learning #multi

A majorization-minimization algorithm for (multiple) hyperparameter learning (CSF, CBD, AYN), pp. 321–328.

ICML-2009-FuSX #evolution #network

Dynamic mixed membership blockmodel for evolving networks (WF, LS, EPX), pp. 329–336.

ICML-2009-GargK #algorithm #strict

Gradient descent with sparsification: an iterative algorithm for sparse recovery with restricted isometry property (RG, RK), pp. 337–344.

ICML-2009-GarnettOR #predict

Sequential Bayesian prediction in the presence of changepoints (RG, MAO, SJR), pp. 345–352.

ICML-2009-GermainLLM #classification #learning #linear

PAC-Bayesian learning of linear classifiers (PG, AL, FL, MM), pp. 353–360.

ICML-2009-GiesekePK #clustering #performance

Fast evolutionary maximum margin clustering (FG, TP, OK), pp. 361–368.

ICML-2009-GomesK #dynamic analysis #multi

Dynamic analysis of multiagent Q-learning with ε-greedy exploration (ERG, RK), pp. 369–376.

ICML-2009-GuiverS #modelling #ranking

Bayesian inference for Plackett-Luce ranking models (JG, ES), pp. 377–384.

ICML-2009-HaiderS #clustering #detection #email

Bayesian clustering for email campaign detection (PH, TS), pp. 385–392.

ICML-2009-HazanS #algorithm #learning #performance

Efficient learning algorithms for changing environments (EH, CS), pp. 393–400.

ICML-2009-Heidrich-MeisnerI #policy

Hoeffding and Bernstein races for selecting policies in evolutionary direct policy search (VHM, CI), pp. 401–408.

ICML-2009-HelleputteD #feature model #linear #modelling

Partially supervised feature selection with regularized linear models (TH, PD), pp. 409–416.

ICML-2009-HuangZM #learning

Learning with structured sparsity (JH, TZ, DNM), pp. 417–424.

ICML-2009-HuangS #learning #linear #sequence

Learning linear dynamical systems without sequence information (TKH, JGS), pp. 425–432.

ICML-2009-JacobOV #graph

Group lasso with overlap and graph lasso (LJ, GO, JPV), pp. 433–440.

ICML-2009-JebaraWC #graph #learning

Graph construction and b-matching for semi-supervised learning (TJ, JW, SFC), pp. 441–448.

ICML-2009-JetchevT #learning #predict

Trajectory prediction: learning to map situations to robot trajectories (NJ, MT), pp. 449–456.

ICML-2009-JiY

An accelerated gradient method for trace norm minimization (SJ, JY), pp. 457–464.

ICML-2009-JohnsonCC #representation

Orbit-product representation and correction of Gaussian belief propagation (JKJ, VYC, MC), pp. 473–480.

ICML-2009-KamisettyL #approach #assessment #quality

A Bayesian approach to protein model quality assessment (HK, CJL), pp. 481–488.

ICML-2009-KarampatziakisK #learning #predict

Learning prediction suffix trees with Winnow (NK, DK), pp. 489–496.

ICML-2009-KeglB #classification

Boosting products of base classifiers (BK, RBF), pp. 497–504.

ICML-2009-KokD #learning #logic #markov #network

Learning Markov logic network structure via hypergraph lifting (SK, PMD), pp. 505–512.

ICML-2009-KolterN #polynomial

Near-Bayesian exploration in polynomial time (JZK, AYN), pp. 513–520.

ICML-2009-KolterN09a #difference #feature model #learning

Regularization and feature selection in least-squares temporal difference learning (JZK, AYN), pp. 521–528.

ICML-2009-KondorSB

The graphlet spectrum (RK, NS, KMB), pp. 529–536.

ICML-2009-KotlowskiS #constraints #learning

Rule learning with monotonicity constraints (WK, RS), pp. 537–544.

ICML-2009-KowalskiSR #kernel #learning #multi

Multiple indefinite kernel learning with mixed norm regularization (MK, MS, LR), pp. 545–552.

ICML-2009-KumarMT #approximate #composition #on the

On sampling-based approximate spectral decomposition (SK, MM, AT), pp. 553–560.

ICML-2009-KunegisL #graph transformation #learning #predict

Learning spectral graph transformations for link prediction (JK, AL), pp. 561–568.

ICML-2009-KuzelkaZ #relational

Block-wise construction of acyclic relational features with monotone irreducibility and relevancy properties (OK, FZ), pp. 569–576.

ICML-2009-LanLML #algorithm #analysis #ranking

Generalization analysis of listwise learning-to-rank algorithms (YL, TYL, ZM, HL), pp. 577–584.

ICML-2009-LangT #approximate #probability #relational

Approximate inference for planning in stochastic relational worlds (TL, MT), pp. 585–592.

ICML-2009-LangfordSZ #learning #modelling

Learning nonlinear dynamic models (JL, RS, TZ), pp. 593–600.

ICML-2009-LawrenceU #matrix #process

Non-linear matrix factorization with Gaussian processes (NDL, RU), pp. 601–608.

ICML-2009-LeeGRN #learning #network #scalability

Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations (HL, RBG, RR, AYN), pp. 609–616.

ICML-2009-LiYX #collaboration #generative #learning

Transfer learning for collaborative filtering via a rating-matrix generative model (BL, QY, XX), pp. 617–624.

ICML-2009-Li #adaptation #classification #multi #named

ABC-boost: adaptive base class boost for multi-class classification (PL0), pp. 625–632.

ICML-2009-LiKZ #learning #using

Semi-supervised learning using label mean (YFL, JTK, ZHZ), pp. 633–640.

ICML-2009-LiangJK #exponential #learning #metric #product line

Learning from measurements in exponential families (PL, MIJ, DK), pp. 641–648.

ICML-2009-LiuPZ #coordination #multi #semantics

Blockwise coordinate descent procedures for the multi-task lasso, with applications to neural semantic basis discovery (HL, MP, JZ), pp. 649–656.

ICML-2009-LiuY #linear #performance

Efficient Euclidean projections in linear time (JL, JY), pp. 657–664.

ICML-2009-LiuNG #community #modelling #topic

Topic-link LDA: joint models of topic and author community (YL, ANM, WG), pp. 665–672.

ICML-2009-LuJD #geometry #learning #metric

Geometry-aware metric learning (ZL, PJ, ISD), pp. 673–680.

ICML-2009-MaSSV #identification #learning #online #scalability

Identifying suspicious URLs: an application of large-scale online learning (JM, LKS, SS, GMV), pp. 681–688.

ICML-2009-MairalBPS #learning #online #taxonomy

Online dictionary learning for sparse coding (JM, FRB, JP, GS), pp. 689–696.

ICML-2009-Makino #network #predict #representation

Proto-predictive representation of states with simple recurrent temporal-difference networks (TM), pp. 697–704.

ICML-2009-MarlinM #modelling #visual notation

Sparse Gaussian graphical models with unknown block structure (BMM, KPM), pp. 705–712.

ICML-2009-MartinsSX #approximate #natural language #parsing

Polyhedral outer approximations with application to natural language parsing (AFTM, NAS, EPX), pp. 713–720.

ICML-2009-McFeeL #kernel #multi #partial order

Partial order embedding with multiple kernels (BM, GRGL), pp. 721–728.

ICML-2009-MesmayRVP #graph #library #optimisation #performance

Bandit-based optimization on graphs with application to library performance tuning (FdM, AR, YV, MP), pp. 729–736.

ICML-2009-MobahiCW #learning #video

Deep learning from temporal coherence in video (HM, RC, JW), pp. 737–744.

ICML-2009-MooijJPS #dependence #modelling

Regression by dependence minimization and its application to causal inference in additive noise models (JMM, DJ, JP, BS), pp. 745–752.

ICML-2009-NeumannMP #learning

Learning complex motions by sequencing simpler motion templates (GN, WM, JP), pp. 753–760.

ICML-2009-NickischS #linear #modelling #scalability

Convex variational Bayesian inference for large scale generalized linear models (HN, MWS), pp. 761–768.

ICML-2009-NowozinJ #clustering #graph #learning #linear #programming

Solution stability in linear programming relaxations: graph partitioning and unsupervised learning (SN, SJ), pp. 769–776.

ICML-2009-PaisleyC #analysis #parametricity #process

Nonparametric factor analysis with beta process priors (JWP, LC), pp. 777–784.

ICML-2009-PanT #modelling

Unsupervised hierarchical modeling of locomotion styles (WP, LT), pp. 785–792.

ICML-2009-PazisL #learning #policy

Binary action search for learning continuous-action control policies (JP, MGL), pp. 793–800.

ICML-2009-PetersJGS #detection

Detecting the direction of causal time series (JP, DJ, AG, BS), pp. 801–808.

ICML-2009-PetrikZ #approximate #constraints #linear #source code

Constraint relaxation in approximate linear programs (MP, SZ), pp. 809–816.

ICML-2009-PlathTN #classification #image #multi #random #segmentation #using

Multi-class image segmentation using conditional random fields and global classification (NP, MT, SN), pp. 817–824.

ICML-2009-PoczosASGS #exclamation #learning

Learning when to stop thinking and do something! (BP, YAY, CS, RG, NRS), pp. 825–832.

ICML-2009-PutthividhyaAN #independence #modelling #topic

Independent factor topic models (DP, HTA, SSN), pp. 833–840.

ICML-2009-QiTZCZ #learning #metric #performance

An efficient sparse metric learning in high-dimensional space via l1-penalized log-determinant regularization (GJQ, JT, ZJZ, TSC, HJZ), pp. 841–848.

ICML-2009-QianJZHW #higher-order #random #sequence

Sparse higher order conditional random fields for improved sequence labeling (XQ, XJ, QZ, XH, LW), pp. 849–856.

ICML-2009-QuattoniCCD #infinity #performance

An efficient projection for l1,infinity regularization (AQ, XC, MC, TD), pp. 857–864.

ICML-2009-RadovanovicNI #nearest neighbour

Nearest neighbors in high-dimensional data: the emergence and influence of hubs (MR, AN, MI), pp. 865–872.

ICML-2009-RainaMN #learning #scalability #using

Large-scale deep unsupervised learning using graphics processors (RR, AM, AYN), pp. 873–880.

ICML-2009-RamanFWDR

The Bayesian group-Lasso for analyzing contingency tables (SR, TJF, PJW, ED, VR), pp. 881–888.

ICML-2009-RaykarYZJFVBM #learning #multi #trust

Supervised learning from multiple experts: whom to trust when everyone lies a bit (VCR, SY, LHZ, AKJ, CF, GHV, LB, LM), pp. 889–896.

ICML-2009-ReidW #bound

Surrogate regret bounds for proper losses (MDR, RCW), pp. 897–904.

ICML-2009-RoyLW #consistency #learning #modelling #probability #visual notation

Learning structurally consistent undirected probabilistic graphical models (SR, TL, MWW), pp. 905–912.

ICML-2009-Rueping #ranking

Ranking interesting subgroups (SR), pp. 913–920.

ICML-2009-Schmidt #process #using

Function factorization using warped Gaussian processes (MNS), pp. 921–928.

ICML-2009-Shalev-ShwartzT #probability

Stochastic methods for l1 regularized loss minimization (SSS, AT), pp. 929–936.

ICML-2009-ShawJ

Structure preserving embedding (BS, TJ), pp. 937–944.

ICML-2009-SilverT #monte carlo #simulation

Monte-Carlo simulation balancing (DS, GT), pp. 945–952.

ICML-2009-SindhwaniML #design #nondeterminism

Uncertainty sampling and transductive experimental design for active dual supervision (VS, PM, RDL), pp. 953–960.

ICML-2009-SongHSF

Hilbert space embeddings of conditional distributions with applications to dynamical systems (LS, JH, AJS, KF), pp. 961–968.

ICML-2009-StreichFBB #clustering #multi

Multi-assignment clustering for Boolean data (APS, MF, DAB, JMB), pp. 969–976.

ICML-2009-SunJY #machine learning #problem

A least squares formulation for a class of generalized eigenvalue problems in machine learning (LS, SJ, JY), pp. 977–984.

ICML-2009-Sutskever #analysis

A simpler unified analysis of budget perceptrons (IS), pp. 985–992.

ICML-2009-SuttonMPBSSW #approximate #learning #linear #performance

Fast gradient-descent methods for temporal-difference learning with linear function approximation (RSS, HRM, DP, SB, DS, CS, EW), pp. 993–1000.

ICML-2009-SzitaL #learning #polynomial

Optimistic initialization and greediness lead to polynomial time learning in factored MDPs (IS, AL), pp. 1001–1008.

ICML-2009-SzlamS

Discriminative k-metrics (AS, GS), pp. 1009–1016.

ICML-2009-TaylorP #approximate #kernel #learning

Kernelized value function approximation for reinforcement learning (GT, RP), pp. 1017–1024.

ICML-2009-TaylorH #modelling #strict

Factored conditional restricted Boltzmann Machines for modeling motion style (GWT, GEH), pp. 1025–1032.

ICML-2009-TielemanH #performance #persistent #using

Using fast weights to improve persistent contrastive divergence (TT, GEH), pp. 1033–1040.

ICML-2009-Tillman #distributed #independence #learning

Structure learning with independent non-identically distributed data (RET), pp. 1041–1048.

ICML-2009-Toussaint #approximate #optimisation #using

Robot trajectory optimization using approximate inference (MT), pp. 1049–1056.

ICML-2009-UsunierBG #classification #order #ranking

Ranking with ordered weighted pairwise classification (NU, DB, PG), pp. 1057–1064.

ICML-2009-VarmaB #kernel #learning #multi #performance

More generality in efficient multiple kernel learning (MV, BRB), pp. 1065–1072.

ICML-2009-NguyenEB #clustering #comparison #metric #question

Information theoretic measures for clusterings comparison: is a correction for chance necessary? (XVN, JE, JB), pp. 1073–1080.

ICML-2009-VlassisT #learning

Model-free reinforcement learning as mixture learning (NV, MT), pp. 1081–1088.

ICML-2009-VolkovsZ #learning #named #ranking

BoltzRank: learning to maximize expected ranking gain (MV, RSZ), pp. 1089–1096.

ICML-2009-WagstaffB #evaluation

K-means in space: a radiation sensitivity evaluation (KLW, BJB), pp. 1097–1104.

ICML-2009-WallachMSM #evaluation #modelling #topic

Evaluation methods for topic models (HMW, IM, RS, DMM), pp. 1105–1112.

ICML-2009-WeinbergerDLSA #learning #multi #scalability

Feature hashing for large scale multitask learning (KQW, AD, JL, AJS, JA), pp. 1113–1120.

ICML-2009-Welling

Herding dynamical weights to learn (MW), pp. 1121–1128.

ICML-2009-WoodAGJT #probability #sequence

A stochastic memoizer for sequence data (FW, CA, JG, LJ, YWT), pp. 1129–1136.

ICML-2009-XuWS #learning #predict

Optimal reverse prediction: a unified perspective on supervised, unsupervised and semi-supervised learning (LX, MW, DS), pp. 1137–1144.

ICML-2009-XuJYLK #feature model

Non-monotonic feature selection (ZX, RJ, JY, MRL, IK), pp. 1145–1152.

ICML-2009-YangJY #learning #online

Online learning by ellipsoid method (LY, RJ, JY), pp. 1153–1160.

ICML-2009-YiWSS #probability #using

Stochastic search using the natural gradient (YS, DW, TS, JS), pp. 1161–1168.

ICML-2009-YuJ #learning

Learning structural SVMs with latent variables (CNJY, TJ), pp. 1169–1176.

ICML-2009-YuM #problem

Piecewise-stationary bandit problems with side observations (JYY, SM), pp. 1177–1184.

ICML-2009-YuLZG #collaboration #parametricity #predict #random #scalability #using

Large-scale collaborative prediction using a nonparametric random effects model (KY, JDL, SZ, YG), pp. 1185–1192.

ICML-2009-YuanH #feature model #learning #robust

Robust feature extraction via information theoretic learning (XY, BGH), pp. 1193–1200.

ICML-2009-YueJ #information retrieval #optimisation #problem

Interactively optimizing information retrieval systems as a dueling bandits problem (YY, TJ), pp. 1201–1208.

ICML-2009-YuilleZ #composition #learning

Compositional noisy-logical learning (ALY, SZ), pp. 1209–1216.

ICML-2009-ZangZMI

Discovering options from example trajectories (PZ, PZ, DM, CLIJ), pp. 1217–1224.

ICML-2009-ZhanLLZ #learning #metric #using

Learning instance specific distances using metric propagation (DCZ, ML, YFL, ZHZ), pp. 1225–1232.

ICML-2009-ZhangKP #learning #prototype #scalability

Prototype vector machine for large scale semi-supervised learning (KZ, JTK, BP), pp. 1233–1240.

ICML-2009-ZhangSFD #learning

Learning non-redundant codebooks for classifying complex objects (WZ, AS, XF, TGD), pp. 1241–1248.

ICML-2009-ZhouSL #learning #multi

Multi-instance learning by treating instances as non-I.I.D. samples (ZHZ, YYS, YFL), pp. 1249–1256.

ICML-2009-ZhuAX #classification #modelling #named #topic

MedLDA: maximum margin supervised topic models for regression and classification (JZ, AA, EPX), pp. 1257–1264.

ICML-2009-ZhuX #markov #network #on the

On primal and dual sparsity of Markov networks (JZ, EPX), pp. 1265–1272.

ICML-2009-ZhuangTH #kernel #learning #named #parametricity

SimpleNPKL: simple non-parametric kernel learning (JZ, IWT, SCHH), pp. 1273–1280.