BibSLEIGH — Proceedings of the 25th International Conference on Machine Learning

William W. Cohen, Andrew McCallum, Sam T. Roweis
Proceedings of the 25th International Conference on Machine Learning
ICML, 2008.

KER

@proceedings{ICML-2008,
	address       = "Helsinki, Finland",
	editor        = "William W. Cohen and Andrew McCallum and Sam T. Roweis",
	isbn          = "978-1-60558-205-4",
	publisher     = "{ACM}",
	series        = "{ACM International Conference Proceeding Series}",
	title         = "{Proceedings of the 25th International Conference on Machine Learning}",
	volume        = 307,
	year          = 2008,
}

Contents (157 items)

ICML-2008-AdamsS #modelling #parametricity #process: Gaussian process product models for nonparametric nonstationarity (RPA, OS), pp. 1–8.
ICML-2008-AllauzenMT #kernel #predict #sequence: Sequence kernels for predicting protein essentiality (CA, MM, AT), pp. 9–16.
ICML-2008-AnWSWCD #analysis #image #kernel #multi #process: Hierarchical kernel stick-breaking process for multi-task image analysis (QA, CW, IS, EW, LC, DBD), pp. 17–24.
ICML-2008-Bach #graph #kernel: Graph kernels between point clouds (FRB), pp. 25–32.
ICML-2008-Bach08a #consistency #estimation #named: Bolasso: model consistent Lasso estimation through the bootstrap (FRB), pp. 33–40.
ICML-2008-BarrettN #learning #multi #policy: Learning all optimal policies with multiple criteria (LB, SN), pp. 41–47.
ICML-2008-BergeronZBB #multi #ranking: Multiple instance ranking (CB, JZ, CMB, KPB), pp. 48–55.
ICML-2008-BickelBLS #learning #multi: Multi-task learning for HIV therapy screening (SB, JB, TL, TS), pp. 56–63.
ICML-2008-BiggsGV #matrix: Nonnegative matrix factorization via rank-one downdate (MB, AG, SAV), pp. 64–71.
ICML-2008-BowlingJBS #evaluation #game studies: Strategy evaluation in extensive games with importance sampling (MHB, MJ, NB, DS), pp. 72–79.
ICML-2008-BryanS #learning: Actively learning level-sets of composite functions (BB, JGS), pp. 80–87.
ICML-2008-CaronD #parametricity: Sparse Bayesian nonparametric regression (FC, AD), pp. 88–95.
ICML-2008-CaruanaKY #empirical #evaluation #learning: An empirical evaluation of supervised learning in high dimensions (RC, NK, AY), pp. 96–103.
ICML-2008-CatanzaroSK #classification #performance: Fast support vector machine training and classification on graphics processors (BCC, NS, KK), pp. 104–111.
ICML-2008-Cayton #nearest neighbour #performance #retrieval: Fast nearest neighbor retrieval for bregman divergences (LC), pp. 112–119.
ICML-2008-CevikalpTP #classification: Nearest hyperdisk methods for high-dimensional classification (HC, BT, RP), pp. 120–127.
ICML-2008-ChenM #learning: Learning to sportscast: a test of grounded language acquisition (DLC, RJM), pp. 128–135.
ICML-2008-ChenY #kernel: Training SVM with indefinite kernels (JC, JY), pp. 136–143.
ICML-2008-CoatesAN #learning #multi: Learning for control from multiple demonstrations (AC, PA, AYN), pp. 144–151.
ICML-2008-ColemanSW #clustering #consistency: Spectral clustering with inconsistent advice (TC, JS, AW), pp. 152–159.
ICML-2008-CollobertW #architecture #learning #multi #natural language #network: A unified architecture for natural language processing: deep neural networks with multitask learning (RC, JW), pp. 160–167.
ICML-2008-Corrada-EmmanuelS #estimation #fault #geometry #low level #precise: Autonomous geometric precision error estimation in low-level computer vision tasks (ACE, HJS), pp. 168–175.
ICML-2008-CortesMPR #algorithm: Stability of transductive regression algorithms (CC, MM, DP, AR), pp. 176–183.
ICML-2008-CrammerTP #clustering: A rate-distortion one-class model and its applications to clustering (KC, PPT, FCNP), pp. 184–191.
ICML-2008-CunninghamSS #estimation #performance #process: Fast Gaussian process methods for point process intensity estimation (JPC, KVS, MS), pp. 192–199.
ICML-2008-DaiYXY #clustering #self: Self-taught clustering (WD, QY, GRX, YY), pp. 200–207.
ICML-2008-DasguptaH #learning: Hierarchical sampling for active learning (SD, DH), pp. 208–215.
ICML-2008-DekelS #learning: Learning to classify with missing and corrupted features (OD, OS), pp. 216–223.
ICML-2008-DembczynskiKS: Maximum likelihood rule ensembles (KD, WK, RS), pp. 224–231.
ICML-2008-DickHS #infinity #learning #semistructured data: Learning from incomplete data with infinite imputations (UD, PH, TS), pp. 232–239.
ICML-2008-DiukCL #learning #object-oriented #performance #representation: An object-oriented representation for efficient reinforcement learning (CD, AC, MLL), pp. 240–247.
ICML-2008-DonmezC #learning #optimisation #rank #reduction: Optimizing estimated loss reduction for active sampling in rank learning (PD, JGC), pp. 248–255.
ICML-2008-DoshiPR #learning #using: Reinforcement learning with limited reinforcement: using Bayes risk for active learning in POMDPs (FD, JP, NR), pp. 256–263.
ICML-2008-DredzeCP #classification #linear: Confidence-weighted linear classification (MD, KC, FP), pp. 264–271.
ICML-2008-DuchiSSC #learning #performance: Efficient projections onto the l1-ball for learning in high dimensions (JCD, SSS, YS, TC), pp. 272–279.
ICML-2008-DugasG: Pointwise exact bootstrap distributions of cost curves (CD, DG), pp. 280–287.
ICML-2008-DundarWLSR #case study #classification #detection: Polyhedral classifier for target detection: a case study: colorectal cancer (MD, MW, SL, MS, VCR), pp. 288–295.
ICML-2008-EpshteynVD #learning: Active reinforcement learning (AE, AV, GD), pp. 296–303.
ICML-2008-FinleyJ: Training structural SVMs when exact inference is intractable (TF, TJ), pp. 304–311.
ICML-2008-FoxSJW #persistent: An HDP-HMM for systems with state persistence (EBF, EBS, MIJ, ASW), pp. 312–319.
ICML-2008-FrancS #algorithm: Optimized cutting plane algorithm for support vector machines (VF, SS), pp. 320–327.
ICML-2008-FrancLM #fault: Stopping conditions for exact computation of leave-one-out error in support vector machines (VF, PL, KRM), pp. 328–335.
ICML-2008-FrankMP #learning: Reinforcement learning in the presence of rare events (JF, SM, DP), pp. 336–343.
ICML-2008-GomesWP #bound #memory management #modelling #topic: Memory bounded inference in topic models (RG, MW, PP), pp. 344–351.
ICML-2008-GonenA #kernel #learning #locality #multi: Localized multiple kernel learning (MG, EA), pp. 352–359.
ICML-2008-GordonGM #game studies #learning: No-regret learning in convex games (GJG, AG, CM), pp. 360–367.
ICML-2008-HaffariWWMJ: Boosting with incomplete information (GH, YW, SW, GM, FJ), pp. 368–375.
ICML-2008-HamL #analysis #learning: Grassmann discriminant analysis: a unifying view on subspace-based learning (JH, DDL), pp. 376–383.
ICML-2008-HeigoldDSN #evaluation #recognition #speech: Modified MMI/MPE: a direct evaluation of the margin in speech recognition (GH, TD, RS, HN), pp. 384–391.
ICML-2008-HellerWG #modelling #statistics: Statistical models for partial membership (KAH, SW, ZG), pp. 392–399.
ICML-2008-HoiJ #kernel #learning: Active kernel learning (SCHH, RJ), pp. 400–407.
ICML-2008-HsiehCLKS #coordination #linear #scalability: A dual coordinate descent method for large-scale linear SVM (CJH, KWC, CJL, SSK, SS), pp. 408–415.
ICML-2008-HuynhM #learning #logic #markov #network #parametricity: Discriminative structure and parameter learning for Markov logic networks (TNH, RJM), pp. 416–423.
ICML-2008-HyvarinenSH #modelling: Causal modelling combining instantaneous and lagged effects: an identifiable model based on non-Gaussianity (AH, SS, POH), pp. 424–431.
ICML-2008-KakadeST #algorithm #multi #online #performance #predict: Efficient bandit algorithms for online multiclass prediction (SMK, SSS, AT), pp. 440–447.
ICML-2008-KarlenWEC #scalability: Large scale manifold transduction (MK, JW, AE, RC), pp. 448–455.
ICML-2008-KerstingD #parametricity #policy #relational: Non-parametric policy gradients: a unified treatment of propositional and relational domains (KK, KD), pp. 456–463.
ICML-2008-KirshnerP #dependence #using: ICA and ISA using Schweizer-Wolff measure of dependence (SK, BP), pp. 464–471.
ICML-2008-KlementievRS #modelling #rank: Unsupervised rank aggregation with distance-based models (AK, DR, KS), pp. 472–479.
ICML-2008-KohliSRKT #multi #on the: On partial optimality in multi-label MRFs (PK, AS, CR, VK, PHST), pp. 480–487.
ICML-2008-KolterCNGD #learning #programming: Space-indexed dynamic programming: learning to follow trajectories (JZK, AC, AYN, YG, CD), pp. 488–495.
ICML-2008-KondorB #graph: The skew spectrum of graphs (RK, KMB), pp. 496–503.
ICML-2008-KuzelkaZ #estimation #first-order #performance: Fast estimation of first-order clause coverage through randomization and maximum likelihood (OK, FZ), pp. 504–511.
ICML-2008-LanLQML #learning #rank: Query-level stability and generalization in learning to rank (YL, TYL, TQ, ZM, HL), pp. 512–519.
ICML-2008-Landwehr #modelling #process: Modeling interleaved hidden processes (NL), pp. 520–527.
ICML-2008-LangfordSW: Exploration scavenging (JL, ALS, JW), pp. 528–535.
ICML-2008-LarochelleB #classification #strict #using: Classification using discriminative restricted Boltzmann machines (HL, YB), pp. 536–543.
ICML-2008-LazaricRB #learning: Transfer of samples in batch reinforcement learning (AL, MR, AB), pp. 544–551.
ICML-2008-LebanonZ #modelling: Local likelihood modeling of temporal text streams (GL, YZ), pp. 552–559.
ICML-2008-Li #approximate #comparison #difference #linear #worst-case: A worst-case comparison between temporal difference and residual gradient with linear function approximation (LL), pp. 560–567.
ICML-2008-LiLW #framework #learning #self #what: Knows what it knows: a framework for self-aware learning (LL, MLL, TJW), pp. 568–575.
ICML-2008-LiLT #classification #constraints #programming: Pairwise constraint propagation by semidefinite programming for semi-supervised classification (ZL, JL, XT), pp. 576–583.
ICML-2008-LiangJ #analysis #generative #pseudo: An asymptotic analysis of generative, discriminative, and pseudolikelihood estimators (PL, MIJ), pp. 584–591.
ICML-2008-LiangDK #compilation: Structure compilation: trading structure for features (PL, HDI, DK), pp. 592–599.
ICML-2008-LoeffFR #approximate #learning #named: ManifoldBoost: stagewise function approximation for fully-, semi- and un-supervised learning (NL, DAF, DR), pp. 600–607.
ICML-2008-LongS #classification #random: Random classification noise defeats all convex potential boosters (PML, RAS), pp. 608–615.
ICML-2008-LuPV #analysis #component #multi: Uncorrelated multilinear principal component analysis through successive variance maximization (HL, KNP, ANV), pp. 616–623.
ICML-2008-LuLHE #framework #kernel: A reproducing kernel Hilbert space framework for pairwise time series distances (ZL, TKL, YH, DE), pp. 624–631.
ICML-2008-MakinoT #network #online: On-line discovery of temporal-difference networks (TM, TT), pp. 632–639.
ICML-2008-MartinsFASX #kernel: Nonextensive entropic kernels (AFTM, MATF, PMQA, NAS, EPX), pp. 640–647.
ICML-2008-MehtaRTD #automation: Automatic discovery and transfer of MAXQ hierarchies (NM, SR, PT, TGD), pp. 648–655.
ICML-2008-MekaJCD #learning #online #rank: Rank minimization via online learning (RM, PJ, CC, ISD), pp. 656–663.
ICML-2008-MeloMR #analysis #approximate #learning: An analysis of reinforcement learning with function approximation (FSM, SPM, MIR), pp. 664–671.
ICML-2008-MnihSA #empirical: Empirical Bernstein stopping (VM, CS, JYA), pp. 672–679.
ICML-2008-KumarT #estimation: Efficiently solving convex relaxations for MAP estimation (MPK, PHST), pp. 680–687.
ICML-2008-NarayanamurthyR #markov #on the #process #symmetry: On the hardness of finding symmetries in Markov decision processes (SMN, BR), pp. 688–695.
ICML-2008-Nijssen #classification: Bayes optimal classification for decision trees (SN), pp. 696–703.
ICML-2008-NowozinB #approach #learning: A decoupled approach to exemplar-based unsupervised learning (SN, GHB), pp. 704–711.
ICML-2008-OBrienGG #classification #multi #probability: Cost-sensitive multi-class classification from probability estimates (DBO, MRG, RMG), pp. 712–719.
ICML-2008-OrabonaKC #bound #kernel: The projectron: a bounded kernel-based Perceptron (FO, JK, BC), pp. 720–727.
ICML-2008-OuyangG #learning #ranking: Learning dissimilarities by ranking: from SDP to QP (HO, AGG), pp. 728–735.
ICML-2008-PaiementGBE #distance: A distance model for rhythms (JFP, YG, SB, DE), pp. 736–743.
ICML-2008-PalatucciC #classification #on the #scalability: On the chance accuracies of large collections of classifiers (MP, AC), pp. 744–751.
ICML-2008-ParrLTPL #analysis #approximate #feature model #learning #linear #modelling: An analysis of linear models, linear value-function approximation, and feature selection for reinforcement learning (RP, LL, GT, CPW, MLL), pp. 752–759.
ICML-2008-PuolamakiAK #learning #query: Learning to learn implicit queries from gaze patterns (KP, AA, SK), pp. 760–767.
ICML-2008-QiLDC #multi #process: Multi-task compressive sensing with Dirichlet process priors (YQ, DL, DBD, LC), pp. 768–775.
ICML-2008-QuadriantoSCL: Estimating labels from label proportions (NQ, AJS, TSC, QVL), pp. 776–783.
ICML-2008-RadlinskiKJ #learning #multi #ranking: Learning diverse rankings with multi-armed bandits (FR, RK, TJ), pp. 784–791.
ICML-2008-RanzatoS #documentation #learning #network: Semi-supervised learning of compact document representations with deep networks (MR, MS), pp. 792–799.
ICML-2008-RavikumarAW #convergence #linear #message passing #source code: Message-passing for graph-structured linear programs: proximal projections, convergence and rounding schemes (PDR, AA, MJW), pp. 800–807.
ICML-2008-RaykarKBDR #automation #feature model #induction #learning #multi: Bayesian multiple instance learning: automatic feature selection and inductive transfer (VCR, BK, JB, MD, RBR), pp. 808–815.
ICML-2008-ReisingerSM #kernel #learning #online: Online kernel selection for Bayesian reinforcement learning (JR, PS, RM), pp. 816–823.
ICML-2008-RenDC #process: The dynamic hierarchical Dirichlet process (LR, DBD, LC), pp. 824–831.
ICML-2008-RishGCPG #linear #modelling #reduction: Closed-form supervised dimensionality reduction with generalized linear models (IR, GG, GAC, FP, GJG), pp. 832–839.
ICML-2008-Rosset #kernel: Bi-level path following for cross validated solution of kernel quantile regression (SR), pp. 840–847.
ICML-2008-RothF #algorithm #linear #modelling #performance: The Group-Lasso for generalized linear models: uniqueness of solutions and efficient algorithms (VR, BF), pp. 848–855.
ICML-2008-SahbiARK #kernel #recognition #robust #using: Robust matching and recognition using context-dependent kernels (HS, JYA, JR, RK), pp. 856–863.
ICML-2008-SakumaKW #learning #privacy: Privacy-preserving reinforcement learning (JS, SK, RNW), pp. 864–871.
ICML-2008-SalakhutdinovM #analysis #network #on the: On the quantitative analysis of deep belief networks (RS, IM), pp. 872–879.
ICML-2008-SalakhutdinovM08a #markov #matrix #monte carlo #probability #using: Bayesian probabilistic matrix factorization using Markov chain Monte Carlo (RS, AM), pp. 880–887.
ICML-2008-SarawagiG: Accurate max-margin training for structured output spaces (SS, RG), pp. 888–895.
ICML-2008-SarkarMP #graph #incremental #performance #proximity #scalability: Fast incremental proximity search in large graphs (PS, AWM, AP), pp. 896–903.
ICML-2008-Schnall-LevinCB #algorithm #design #framework: Inverting the Viterbi algorithm: an abstract framework for structure design (MSL, LC, BB), pp. 904–911.
ICML-2008-SeegerN #design: Compressed sensing and Bayesian experimental design (MWS, HN), pp. 912–919.
ICML-2008-SeldinT #category theory #classification #clustering #multi: Multi-classification by categorical features via clustering (YS, NT), pp. 920–927.
ICML-2008-Shalev-ShwartzS #dependence #optimisation #set: SVM optimization: inverse dependence on training set size (SSS, NS), pp. 928–935.
ICML-2008-ShiBY #learning #modelling #using: Data spectroscopy: learning mixture models using eigenspaces of convolution operators (TS, MB, BY), pp. 936–943.
ICML-2008-ShinK #kernel: A generalization of Haussler’s convolution kernel: mapping kernel (KS, TK), pp. 944–951.
ICML-2008-ShringarpureX #named #search-based: mStruct: a new admixture model for inference of population structure in light of both genetic admixing and allele mutations (SS, EPX), pp. 952–959.
ICML-2008-SiggB: Expectation-maximization for sparse and non-negative PCA (CDS, JMB), pp. 960–967.
ICML-2008-SilverSM #learning: Sample-based learning and search with permanent and transient memories (DS, RSS, MM), pp. 968–975.
ICML-2008-SindhwaniR #learning #multi: An RKHS for multi-view learning and manifold co-regularization (VS, DSR), pp. 976–983.
ICML-2008-SokolovskaCY #learning #modelling #probability: The asymptotics of semi-supervised learning in discriminative probabilistic models (NS, OC, FY), pp. 984–991.
ICML-2008-SongZSGS #estimation #kernel: Tailoring density estimation via reproducing kernel moment matching (LS, XZ, AJS, AG, BS), pp. 992–999.
ICML-2008-SorokinaCRF #detection #interactive #statistics: Detecting statistical interactions with additive groves of trees (DS, RC, MR, DF), pp. 1000–1007.
ICML-2008-SriperumbudurLL #classification #kernel #metric: Metric embedding for kernel classification rules (BKS, OAL, GRGL), pp. 1008–1015.
ICML-2008-SuZLM #learning #network #parametricity: Discriminative parameter learning for Bayesian networks (JS, HZ, CXL, SM), pp. 1016–1023.
ICML-2008-SunJY #analysis #canonical #correlation: A least squares formulation for canonical correlation analysis (LS, SJ, JY), pp. 1024–1031.
ICML-2008-SyedBS #learning #linear #programming #using: Apprenticeship learning using linear programming (US, MHB, RES), pp. 1032–1039.
ICML-2008-SzafranskiGR #kernel #learning: Composite kernel learning (MS, YG, AR), pp. 1040–1047.
ICML-2008-SzitaL #approach: The many faces of optimism: a unifying approach (IS, AL), pp. 1048–1055.
ICML-2008-TakedaS: nu-support vector machine as conditional value-at-risk minimization (AT, MS), pp. 1056–1063.
ICML-2008-Tieleman #approximate #strict #using: Training restricted Boltzmann machines using approximations to the likelihood gradient (TT), pp. 1064–1071.
ICML-2008-UenoKMMI #approach #evaluation #policy #statistics: A semiparametric statistical approach to model-free policy evaluation (TU, MK, TM, SiM, SI), pp. 1072–1079.
ICML-2008-UrtasunFGPDL #modelling: Topologically-constrained latent variable models (RU, DJF, AG, JP, TD, NDL), pp. 1080–1087.
ICML-2008-GaelSTG #infinity #markov: Beam sampling for the infinite hidden Markov model (JVG, YS, YWT, ZG), pp. 1088–1095.
ICML-2008-VincentLBM #robust: Extracting and composing robust features with denoising autoencoders (PV, HL, YB, PAM), pp. 1096–1103.
ICML-2008-VovkZ #game studies #predict: Prediction with expert advice for the Brier game (VV, FZ), pp. 1104–1111.
ICML-2008-WalderKS #multi #process: Sparse multiscale gaussian process regression (CW, KIK, BS), pp. 1112–1119.
ICML-2008-WangM #analysis #using: Manifold alignment using Procrustes analysis (CW, SM), pp. 1120–1127.
ICML-2008-WangYQZ #analysis #component #composition #feature model: Dirichlet component analysis: feature extraction for compositional data (HYW, QY, HQ, HZ), pp. 1128–1135.
ICML-2008-WangYZ #adaptation #kernel #learning #multi: Adaptive p-posterior mixture-model kernels for multiple instance learning (HYW, QY, HZ), pp. 1136–1143.
ICML-2008-WangJC #graph: Graph transduction via alternating minimization (JW, TJ, SFC), pp. 1144–1151.
ICML-2008-WangZ #learning #multi #on the: On multi-view active learning and the combination with semi-supervised learning (WW, ZHZ), pp. 1152–1159.
ICML-2008-WeinbergerS #distance #implementation #learning #metric #performance: Fast solvers and efficient implementations for distance metric learning (KQW, LKS), pp. 1160–1167.
ICML-2008-WestonRC #learning: Deep learning via semi-supervised embedding (JW, FR, RC), pp. 1168–1175.
ICML-2008-WingateS #exponential #learning #predict #product line: Efficiently learning linear-linear exponential family predictive representations of state (DW, SPS), pp. 1176–1183.
ICML-2008-WolfeHK #dataset #distributed #scalability: Fully distributed EM for very large datasets (JW, AH, DK), pp. 1184–1191.
ICML-2008-XiaLWZL #algorithm #approach #learning #rank: Listwise approach to learning to rank: theory and algorithm (FX, TYL, JW, WZ, HL), pp. 1192–1199.
ICML-2008-YamanWLd #approximate #modelling: Democratic approximation of lexicographic preference models (FY, TJW, MLL, Md), pp. 1200–1207.
ICML-2008-YaoL #difference #learning: Preconditioned temporal difference learning (HY, ZQL), pp. 1208–1215.
ICML-2008-YuVGS #approach #optimisation: A quasi-Newton approach to non-smooth convex optimization (JY, SVNV, SG, NNS), pp. 1216–1223.
ICML-2008-YueJ #predict #set #using: Predicting diverse subsets using structural SVMs (YY, TJ), pp. 1224–1231.
ICML-2008-ZhangTK #analysis #approximate #fault #rank: Improved Nyström low-rank approximation and error analysis (KZ, IWT, JTK), pp. 1232–1239.
ICML-2008-ZhangDT #algorithm: Estimating local optimums in EM algorithm over Gaussian mixture model (ZZ, BTD, AKHT), pp. 1240–1247.
ICML-2008-ZhaoWZ #clustering #multi #performance: Efficient multiclass maximum margin clustering (BZ, FW, CZ), pp. 1248–1255.
ICML-2008-ZhuXZ #markov #network: Laplace maximum margin Markov networks (JZ, EPX, BZ), pp. 1256–1263.

William W. Cohen, Andrew McCallum, Sam T. RoweisProceedings of the 25th International Conference on Machine LearningICML, 2008.

Contents (157 items)

William W. Cohen, Andrew McCallum, Sam T. Roweis
Proceedings of the 25th International Conference on Machine Learning
ICML, 2008.