Proceedings of the 24th International Conference on Machine Learning
BibSLEIGH corpus
BibSLEIGH tags
BibSLEIGH bundles
BibSLEIGH people
EDIT!
CC-BY
Open Knowledge
XHTML 1.0 W3C Rec
CSS 2.1 W3C CanRec
email twitter

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

KER
DBLP
Scholar
Full names Links ISxN
@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.

Bibliography of Software Language Engineering in Generated Hypertext (BibSLEIGH) is created and maintained by Dr. Vadim Zaytsev.
Hosted as a part of SLEBOK on GitHub.