BibSLEIGH — Proceedings of the 24th International Conference on Machine Learning

Zoubin Ghahramani
Proceedings of the 24th International Conference on Machine Learning
ICML, 2007.

KER

@proceedings{ICML-2007,
	address       = "Corvallis, Oregon, USA",
	editor        = "Zoubin Ghahramani",
	isbn          = "978-1-59593-793-3",
	publisher     = "{ACM}",
	series        = "{ACM International Conference Proceeding Series}",
	title         = "{Proceedings of the 24th International Conference on Machine Learning}",
	volume        = 227,
	year          = 2007,
}

Contents (150 items)

ICML-2007-AimeurBG #algorithm #clustering #quantum: Quantum clustering algorithms (EA, GB, SG), pp. 1–8.
ICML-2007-AgarwalC #graph #learning #random #rank: Learning random walks to rank nodes in graphs (AA, SC), pp. 9–16.
ICML-2007-AmitFSU #classification #multi: Uncovering shared structures in multiclass classification (YA, MF, NS, SU), pp. 17–24.
ICML-2007-AndoZ #generative #learning: Two-view feature generation model for semi-supervised learning (RKA, TZ), pp. 25–32.
ICML-2007-AndrewG #modelling #scalability: Scalable training of L1-regularized log-linear models (GA, JG), pp. 33–40.
ICML-2007-AsharafMS #multi: Multiclass core vector machine (SA, MNM, SKS), pp. 41–48.
ICML-2007-Azran #algorithm #learning #markov #multi #random: The rendezvous algorithm: multiclass semi-supervised learning with Markov random walks (AA), pp. 49–56.
ICML-2007-BabariaNKSBM #crawling #scalability: Focused crawling with scalable ordinal regression solvers (RB, JSN, SK, KRS, CB, MNM), pp. 57–64.
ICML-2007-Bar-HillelW #distance #learning #similarity: Learning distance function by coding similarity (ABH, DW), pp. 65–72.
ICML-2007-BhattacharyaBC #classification #kernel: Structural alignment based kernels for protein structure classification (SB, CB, NRC), pp. 73–80.
ICML-2007-BickelBS #learning: Discriminative learning for differing training and test distributions (SB, MB, TS), pp. 81–88.
ICML-2007-BordesBGW #multi: Solving multiclass support vector machines with LaRank (AB, LB, PG, JW), pp. 89–96.
ICML-2007-BryanMSS: Efficiently computing minimax expected-size confidence regions (BB, HBM, CMS, JGS), pp. 97–104.
ICML-2007-BunescuM #learning #multi: Multiple instance learning for sparse positive bags (RCB, RJM), pp. 105–112.
ICML-2007-BusseOB #analysis #clustering #rank: Cluster analysis of heterogeneous rank data (LMB, PO, JMB), pp. 113–120.
ICML-2007-CaoSSYC #feature model #kernel: Feature selection in a kernel space (BC, DS, JTS, QY, ZC), pp. 121–128.
ICML-2007-CaoQLTL #approach #learning #rank: Learning to rank: from pairwise approach to listwise approach (ZC, TQ, TYL, MFT, HL), pp. 129–136.
ICML-2007-CazzantiG #analysis #similarity: Local similarity discriminant analysis (LC, MRG), pp. 137–144.
ICML-2007-ChanVL: Direct convex relaxations of sparse SVM (ABC, NV, GRGL), pp. 145–153.
ICML-2007-ChenJ #classification #feature model #set: Minimum reference set based feature selection for small sample classifications (XwC, JCJ), pp. 153–160.
ICML-2007-ChengV #image #learning: Learning to compress images and videos (LC, SVNV), pp. 161–168.
ICML-2007-CortesMR #algorithm #ranking: Magnitude-preserving ranking algorithms (CC, MM, AR), pp. 169–176.
ICML-2007-dAspremontBG #analysis #component: Full regularization path for sparse principal component analysis (Ad, FRB, LEG), pp. 177–184.
ICML-2007-DaiY #kernel: Kernel selection forl semi-supervised kernel machines (GD, DYY), pp. 185–192.
ICML-2007-DaiYXY #learning: Boosting for transfer learning (WD, QY, GRX, YY), pp. 193–200.
ICML-2007-DavidsonR #clustering #constraints: Intractability and clustering with constraints (ID, SSR), pp. 201–208.
ICML-2007-DavisKJSD #learning #metric: Information-theoretic metric learning (JVD, BK, PJ, SS, ISD), pp. 209–216.
ICML-2007-DavisCRP #approach #predict #process: An integrated approach to feature invention and model construction for drug activity prediction (JD, VSC, SR, DP), pp. 217–224.
ICML-2007-DelageM #markov #nondeterminism #optimisation #performance #process: Percentile optimization in uncertain Markov decision processes with application to efficient exploration (ED, SM), pp. 225–232.
ICML-2007-DietzBS #predict: Unsupervised prediction of citation influences (LD, SB, TS), pp. 233–240.
ICML-2007-DollarRB #algorithm #analysis #learning: Non-isometric manifold learning: analysis and an algorithm (PD, VR, SJB), pp. 241–248.
ICML-2007-DudikBS #estimation: Hierarchical maximum entropy density estimation (MD, DMB, RES), pp. 249–256.
ICML-2007-EspositoR #algorithm #classification #evaluation #named #performance: CarpeDiem: an algorithm for the fast evaluation of SSL classifiers (RE, DPR), pp. 257–264.
ICML-2007-FarahmandSA #adaptation #estimation: Manifold-adaptive dimension estimation (AMF, CS, JYA), pp. 265–272.
ICML-2007-GellyS #online: Combining online and offline knowledge in UCT (SG, DS), pp. 273–280.
ICML-2007-GerberTW #reduction #robust #using: Robust non-linear dimensionality reduction using successive 1-dimensional Laplacian Eigenmaps (SG, TT, RTW), pp. 281–288.
ICML-2007-GeurtsWd #kernel: Gradient boosting for kernelized output spaces (PG, LW, FdB), pp. 289–296.
ICML-2007-GhavamzadehE #algorithm: Bayesian actor-critic algorithms (MG, YE), pp. 297–304.
ICML-2007-GlobersonKCC #algorithm #predict: Exponentiated gradient algorithms for log-linear structured prediction (AG, TK, XC, MC), pp. 305–312.
ICML-2007-GriraH #clustering #heuristic: Best of both: a hybridized centroid-medoid clustering heuristic (NG, MEH), pp. 313–320.
ICML-2007-GuoHFX #approach #modelling #network: Recovering temporally rewiring networks: a model-based approach (FG, SH, WF, EPX), pp. 321–328.
ICML-2007-GuptaDS #clique #performance: Efficient inference with cardinality-based clique potentials (RG, AAD, SS), pp. 329–336.
ICML-2007-HeraultG #classification #probability: Sparse probabilistic classifiers (RH, YG), pp. 337–344.
ICML-2007-HaiderBS #clustering #detection #email #streaming: Supervised clustering of streaming data for email batch detection (PH, UB, TS), pp. 345–352.
ICML-2007-Hanneke #bound #complexity #learning: A bound on the label complexity of agnostic active learning (SH), pp. 353–360.
ICML-2007-HoiJL #constraints #kernel #learning #matrix #parametricity: Learning nonparametric kernel matrices from pairwise constraints (SCHH, RJ, MRL), pp. 361–368.
ICML-2007-Jaeger #learning #network #parametricity #relational: Parameter learning for relational Bayesian networks (MJ), pp. 369–376.
ICML-2007-JiC #optimisation: Bayesian compressive sensing and projection optimization (SJ, LC), pp. 377–384.
ICML-2007-JohnsM #approximate #graph: Constructing basis functions from directed graphs for value function approximation (JJ, SM), pp. 385–392.
ICML-2007-KerstingPPB #process: Most likely heteroscedastic Gaussian process regression (KK, CP, PP, WB), pp. 393–400.
ICML-2007-KimC #algorithm #geometry #message passing: Neighbor search with global geometry: a minimax message passing algorithm (KHK, SC), pp. 401–408.
ICML-2007-KimP #learning #recursion: A recursive method for discriminative mixture learning (MK, VP), pp. 409–416.
ICML-2007-KirshnerS #infinity: Infinite mixtures of trees (SK, PS), pp. 417–423.
ICML-2007-KlamiK #component: Local dependent components (AK, SK), pp. 425–432.
ICML-2007-KokD #statistics: Statistical predicate invention (SK, PMD), pp. 433–440.
ICML-2007-KramerB #kernel #performance: Kernelizing PLS, degrees of freedom, and efficient model selection (NK, MLB), pp. 441–448.
ICML-2007-KrauseG #approach #learning #process: Nonmyopic active learning of Gaussian processes: an exploration-exploitation approach (AK, CG), pp. 449–456.
ICML-2007-KropotovV #learning #on the: On one method of non-diagonal regularization in sparse Bayesian learning (DK, DV), pp. 457–464.
ICML-2007-KuzminW #kernel #matrix #online: Online kernel PCA with entropic matrix updates (DK, MKW), pp. 465–472.
ICML-2007-LarochelleECBB #architecture #empirical #evaluation #problem: An empirical evaluation of deep architectures on problems with many factors of variation (HL, DE, ACC, JB, YB), pp. 473–480.
ICML-2007-LawrenceM #modelling #process: Hierarchical Gaussian process latent variable models (NDL, AJM), pp. 481–488.
ICML-2007-LeeCVK #learning #multi: Learning a meta-level prior for feature relevance from multiple related tasks (SIL, VC, DV, DK), pp. 489–496.
ICML-2007-LeskovecF #graph #modelling #multi #scalability #using: Scalable modeling of real graphs using Kronecker multiplication (JL, CF), pp. 497–504.
ICML-2007-LiCFX #clustering: Support cluster machine (BL, MC, JF, XX), pp. 505–512.
ICML-2007-LiYW #distance #framework #learning #metric #reduction: A transductive framework of distance metric learning by spectral dimensionality reduction (FL, JY, JW), pp. 513–520.
ICML-2007-DingL #adaptation #analysis #clustering #reduction #using: Adaptive dimension reduction using discriminant analysis and K-means clustering (CHQD, TL), pp. 521–528.
ICML-2007-LiLL #learning #scalability: Large-scale RLSC learning without agony (WL, KHL, KSL), pp. 529–536.
ICML-2007-LiCLW #novel #orthogonal: A novel orthogonal NMF-based belief compression for POMDPs (XL, WKWC, JL, ZW), pp. 537–544.
ICML-2007-LiangJT #modelling: A permutation-augmented sampler for DP mixture models (PL, MIJ, BT), pp. 545–552.
ICML-2007-LiaoLC #classification #semistructured data: Quadratically gated mixture of experts for incomplete data classification (XL, HL, LC), pp. 553–560.
ICML-2007-LinWK #scalability #trust: Trust region Newton methods for large-scale logistic regression (CJL, RCW, SSK), pp. 561–568.
ICML-2007-LongZWY #clustering #relational #symmetry: Relational clustering by symmetric convex coding (BL, Z(Z, XW, PSY), pp. 569–576.
ICML-2007-MaLTK #analysis #correlation #similarity: Discriminant analysis in correlation similarity measure space (YM, SL, ET, MK), pp. 577–584.
ICML-2007-Mahadevan #3d #adaptation #learning #multi #using: Adaptive mesh compression in 3D computer graphics using multiscale manifold learning (SM), pp. 585–592.
ICML-2007-MannM #learning #robust #scalability: Simple, robust, scalable semi-supervised learning via expectation regularization (GSM, AM), pp. 593–600.
ICML-2007-Marthi #automation #composition: Automatic shaping and decomposition of reward functions (BM), pp. 601–608.
ICML-2007-Masnadi-ShiraziV #symmetry: Asymmetric boosting (HMS, NV), pp. 609–619.
ICML-2007-McNeillV #generative #linear #modelling #probability: Linear and nonlinear generative probabilistic class models for shape contours (GM, SV), pp. 617–624.
ICML-2007-MihalkovaM #bottom-up #learning #logic #markov #network: Bottom-up learning of Markov logic network structure (LM, RJM), pp. 625–632.
ICML-2007-MimnoLM #topic: Mixtures of hierarchical topics with Pachinko allocation (DMM, WL, AM), pp. 633–640.
ICML-2007-MnihH #modelling #statistics #visual notation: Three new graphical models for statistical language modelling (AM, GEH), pp. 641–648.
ICML-2007-MoschittiZ #effectiveness #kernel #learning #performance #relational: Fast and effective kernels for relational learning from texts (AM, FMZ), pp. 649–656.
ICML-2007-MosciRV #reduction: Dimensionality reduction and generalization (SM, LR, AV), pp. 657–664.
ICML-2007-MylonakisSH #estimation #modelling: Unsupervised estimation for noisy-channel models (MM, KS, RH), pp. 665–672.
ICML-2007-NelsonC #clustering #constraints #modelling #probability: Revisiting probabilistic models for clustering with pair-wise constraints (BN, IC), pp. 673–680.
ICML-2007-NguyenG #algorithm #sequence: Comparisons of sequence labeling algorithms and extensions (NN, YG), pp. 681–688.
ICML-2007-NiCD #learning #multi #process: Multi-task learning for sequential data via iHMMs and the nested Dirichlet process (KN, LC, DBD), pp. 689–696.
ICML-2007-NilssonSJ #kernel #reduction #using: Regression on manifolds using kernel dimension reduction (JN, FS, MIJ), pp. 697–704.
ICML-2007-OsentoskiM #learning: Learning state-action basis functions for hierarchical MDPs (SO, SM), pp. 705–712.
ICML-2007-YoganandaMG #linear #performance: A fast linear separability test by projection of positive points on subspaces (APY, MNM, LG), pp. 713–720.
ICML-2007-PandeyCA #multi #problem: Multi-armed bandit problems with dependent arms (SP, DC, DA), pp. 721–728.
ICML-2007-ParkerFT #learning #performance #query #retrieval: Learning for efficient retrieval of structured data with noisy queries (CP, AF, PT), pp. 729–736.
ICML-2007-ParrPLL #approximate #generative: Analyzing feature generation for value-function approximation (RP, CPW, LL, MLL), pp. 737–744.
ICML-2007-PetersS #learning: Reinforcement learning by reward-weighted regression for operational space control (JP, SS), pp. 745–750.
ICML-2007-PhuaF #approximate #learning #linear: Tracking value function dynamics to improve reinforcement learning with piecewise linear function approximation (CWP, RF), pp. 751–758.
ICML-2007-RainaBLPN #learning #self: Self-taught learning: transfer learning from unlabeled data (RR, AB, HL, BP, AYN), pp. 759–766.
ICML-2007-RakhlinAB #online #similarity: Online discovery of similarity mappings (AR, JA, PLB), pp. 767–774.
ICML-2007-RakotomamonjyBCG #kernel #learning #multi #performance: More efficiency in multiple kernel learning (AR, FRB, SC, YG), pp. 775–782.
ICML-2007-RattiganMJ #clustering #graph #network: Graph clustering with network structure indices (MJR, MEM, DJ), pp. 783–790.
ICML-2007-SalakhutdinovMH #collaboration #strict: Restricted Boltzmann machines for collaborative filtering (RS, AM, GEH), pp. 791–798.
ICML-2007-Shah #bound: Sample compression bounds for decision trees (MS), pp. 799–806.
ICML-2007-Shalev-ShwartzSS #named: Pegasos: Primal Estimated sub-GrAdient SOlver for SVM (SSS, YS, NS), pp. 807–814.
ICML-2007-SongSGB #clustering #dependence: A dependence maximization view of clustering (LS, AJS, AG, KMB), pp. 815–822.
ICML-2007-SongSGBB #dependence #estimation #feature model: Supervised feature selection via dependence estimation (LS, AJS, AG, KMB, JB), pp. 823–830.
ICML-2007-SriperumbudurTL #programming: Sparse eigen methods by D.C. programming (BKS, DAT, GRGL), pp. 831–838.
ICML-2007-SternHG #game studies #learning: Learning to solve game trees (DHS, RH, TG), pp. 839–846.
ICML-2007-SunKR #fault #metric #robust: Robust mixtures in the presence of measurement errors (JS, AK, SR), pp. 847–854.
ICML-2007-SunJSF #algorithm #kernel #learning: A kernel-based causal learning algorithm (XS, DJ, BS, KF), pp. 855–862.
ICML-2007-SuttonM #performance #pseudo #random: Piecewise pseudolikelihood for efficient training of conditional random fields (CAS, AM), pp. 863–870.
ICML-2007-SuttonKS #on the: On the role of tracking in stationary environments (RSS, AK, DS), pp. 871–878.
ICML-2007-TaylorS #learning: Cross-domain transfer for reinforcement learning (MET, PS), pp. 879–886.
ICML-2007-TitovH #incremental #network #predict: Incremental Bayesian networks for structure prediction (IT, JH), pp. 887–894.
ICML-2007-TomiokaA #matrix: Classifying matrices with a spectral regularization (RT, KA), pp. 895–902.
ICML-2007-TsampoukaS #algorithm #approximate: Approximate maximum margin algorithms with rules controlled by the number of mistakes (PT, JST), pp. 903–910.
ICML-2007-TsangKK: Simpler core vector machines with enclosing balls (IWT, AK, JTK), pp. 911–918.
ICML-2007-Tsuda #graph: Entire regularization paths for graph data (KT), pp. 919–926.
ICML-2007-UrtasunD #classification #process: Discriminative Gaussian process latent variable model for classification (RU, TD), pp. 927–934.
ICML-2007-HulseKN #learning: Experimental perspectives on learning from imbalanced data (JVH, TMK, AN), pp. 935–942.
ICML-2007-WachmanK #kernel #learning #order: Learning from interpretations: a rooted kernel for ordered hypergraphs (GW, RK), pp. 943–950.
ICML-2007-WangYL #algorithm #kernel: A kernel path algorithm for support vector machines (GW, DYY, FHL), pp. 951–958.
ICML-2007-WangZQ #learning #metric #towards: Dirichlet aggregation: unsupervised learning towards an optimal metric for proportional data (HYW, HZ, HQ), pp. 959–966.
ICML-2007-WangYHLT: Transductive regression piloted by inter-manifold relations (HW, SY, TSH, JL, XT), pp. 967–974.
ICML-2007-WangFH #modelling #multi #process: Multifactor Gaussian process models for style-content separation (JMW, DJF, AH), pp. 975–982.
ICML-2007-WangZZ #array #classification #hybrid: Hybrid huberized support vector machines for microarray classification (LW, JZ, HZ), pp. 983–990.
ICML-2007-WangYF #difference #learning #on the: On learning with dissimilarity functions (LW, CY, JF), pp. 991–998.
ICML-2007-Warmuth: Winnowing subspaces (MKW), pp. 999–1006.
ICML-2007-Werner #algorithm #consistency #question #what: What is decreased by the max-sum arc consistency algorithm? (TW), pp. 1007–1014.
ICML-2007-WilsonFRT #approach #learning #multi: Multi-task reinforcement learning: a hierarchical Bayesian approach (AW, AF, SR, PT), pp. 1015–1022.
ICML-2007-WipfN #using: Beamforming using the relevance vector machine (DPW, SSN), pp. 1023–1030.
ICML-2007-WoznicaKH #learning: Learning to combine distances for complex representations (AW, AK, MH), pp. 1031–1038.
ICML-2007-WuYYS #learning: Local learning projections (MW, KY, SY, BS), pp. 1039–1046.
ICML-2007-XuF #learning #linear #on the #ranking: On learning linear ranking functions for beam search (YX, AF), pp. 1047–1054.
ICML-2007-XuanM #dependence #modelling #multi: Modeling changing dependency structure in multivariate time series (XX, KPM), pp. 1055–1062.
ICML-2007-XueDC #flexibility #learning #matrix #multi #process: The matrix stick-breaking process for flexible multi-task learning (YX, DBD, LC), pp. 1063–1070.
ICML-2007-Yairi #locality #reduction: Map building without localization by dimensionality reduction techniques (TY), pp. 1071–1078.
ICML-2007-YamazakiKWSM #fault: Asymptotic Bayesian generalization error when training and test distributions are different (KY, MK, SW, MS, KRM), pp. 1079–1086.
ICML-2007-Ye #analysis #linear: Least squares linear discriminant analysis (JY), pp. 1087–1093.
ICML-2007-YeCJ #kernel #learning #parametricity #programming: Discriminant kernel and regularization parameter learning via semidefinite programming (JY, JC, SJ), pp. 1095–1102.
ICML-2007-YuTY #learning #multi #robust: Robust multi-task learning with t-processes (SY, VT, KY), pp. 1103–1110.
ICML-2007-ZhangY #classification #consistency #constraints #on the: On the value of pairwise constraints in classification and consistency (JZ, RY), pp. 1111–1118.
ICML-2007-ZhangTK #clustering: Maximum margin clustering made practical (KZ, IWT, JTK), pp. 1119–1126.
ICML-2007-ZhangC #analysis #component #independence: Nonlinear independent component analysis with minimal nonlinear distortion (KZ, LC), pp. 1127–1134.
ICML-2007-ZhangXSGL #classification #metric: Optimal dimensionality of metric space for classification (WZ, XX, ZS, YFG, HL), pp. 1135–1142.
ICML-2007-ZhangAV #learning #multi #random: Conditional random fields for multi-agent reinforcement learning (XZ, DA, SVNV), pp. 1143–1150.
ICML-2007-ZhaoL #feature model #learning: Spectral feature selection for supervised and unsupervised learning (ZZ, HL), pp. 1151–1157.
ICML-2007-ZhouB #clustering #learning #multi: Spectral clustering and transductive learning with multiple views (DZ, CJCB), pp. 1159–1166.
ICML-2007-ZhouX #learning #multi #on the: On the relation between multi-instance learning and semi-supervised learning (ZHZ, JMX), pp. 1167–1174.
ICML-2007-ZhuNZW #markov #random #web: Dynamic hierarchical Markov random fields and their application to web data extraction (JZ, ZN, BZ, JRW), pp. 1175–1182.
ICML-2007-ZienBS: Transductive support vector machines for structured variables (AZ, UB, TS), pp. 1183–1190.
ICML-2007-ZienO #kernel #learning #multi: Multiclass multiple kernel learning (AZ, CSO), pp. 1191–1198.

Zoubin GhahramaniProceedings of the 24th International Conference on Machine LearningICML, 2007.

Contents (150 items)

Zoubin Ghahramani
Proceedings of the 24th International Conference on Machine Learning
ICML, 2007.