Abstract is missing.
- Hashing with GraphsWei Liu, Jun Wang 0006, Sanjiv Kumar, Shih-Fu Chang. 1-8
- Efficient Sparse Modeling with Automatic Feature GroupingWenliang Zhong, James T. Kwok. 9-16
- MultiLabel Classification on Tree- and DAG-Structured HierarchiesWei Bi, James T. Kwok. 17-24
- A Graphbased Framework for Multi-Task Multi-View LearningJingrui He, Rick Lawrence. 25-32
- GoDec: Randomized Lowrank & Sparse Matrix Decomposition in Noisy CaseTianyi Zhou, Dacheng Tao. 33-40
- Unimodal BanditsJia Yuan Yu, Shie Mannor. 41-48
- Learning Output Kernels with Block Coordinate DescentFrancesco Dinuzzo, Cheng Soon Ong, Peter V. Gehler, Gianluigi Pillonetto. 49-56
- Vector-valued Manifold RegularizationHa Quang Minh, Vikas Sindhwani. 57-64
- On Information-Maximization Clustering: Tuning Parameter Selection and Analytic SolutionMasashi Sugiyama, Makoto Yamada, Manabu Kimura, Hirotaka Hachiya. 65-72
- On tracking portfolios with certainty equivalents on a generalization of Markowitz model: the Fool, the Wise and the AdaptiveRichard Nock, Brice Magdalou, Eric Briys, Frank Nielsen. 73-80
- Multiple Instance Learning with Manifold BagsBoris Babenko, Nakul Verma, Piotr Dollár, Serge Belongie. 81-88
- Eigenvalue Sensitive Feature SelectionYi Jiang, Jiangtao Ren. 89-96
- Large Scale Text Classification using Semisupervised Multinomial Naive BayesJiang Su, Jelber Sayyad Shirab, Stan Matwin. 97-104
- Enhanced Gradient and Adaptive Learning Rate for Training Restricted Boltzmann MachinesKyungHyun Cho, Tapani Raiko, Alexander Ilin. 105-112
- Dynamic Tree Block Coordinate AscentDaniel Tarlow, Dhruv Batra, Pushmeet Kohli, Vladimir Kolmogorov. 113-120
- Implementing regularization implicitly via approximate eigenvector computationMichael W. Mahoney, Lorenzo Orecchia. 121-128
- Parsing Natural Scenes and Natural Language with Recursive Neural NetworksRichard Socher, Cliff Chiung-Yu Lin, Andrew Y. Ng, Christopher D. Manning. 129-136
- Conjugate Markov Decision ProcessesPhilip S. Thomas, Andrew G. Barto. 137-144
- Learning Mallows Models with Pairwise PreferencesTyler Lu, Craig Boutilier. 145-152
- Surrogate losses and regret bounds for cost-sensitive classification with example-dependent costsClayton Scott. 153-160
- Efficient Rule Ensemble Learning using Hierarchical KernelsPratik Jawanpuria, Jagarlapudi Saketha Nath, Ganesh Ramakrishnan. 161-168
- An Augmented Lagrangian Approach to Constrained MAP InferenceAndré L. Martins, Mário A. T. Figueiredo, Pedro M. Q. Aguiar, Noah A. Smith, Eric P. Xing. 169-176
- Mean-Variance Optimization in Markov Decision ProcessesShie Mannor, John N. Tsitsiklis. 177-184
- Time Series Clustering: Complex is Simpler!Lei Li, B. Aditya Prakash. 185-192
- Max-margin Learning for Lower Linear Envelope Potentials in Binary Markov Random FieldsStephen Gould. 193-200
- Inference of Inversion Transduction GrammarsAlexander Clark. 201-208
- BCDNPKL: Scalable Non-Parametric Kernel Learning Using Block Coordinate DescentEnliang Hu, Bo Wang, Songcan Chen. 209-216
- Learning Discriminative Fisher KernelsLaurens van der Maaten. 217-224
- Pruning nearest neighbor cluster treesSamory Kpotufe, Ulrike von Luxburg. 225-232
- Online AUC MaximizationPeilin Zhao, Steven C. H. Hoi, Rong Jin, Tianbao Yang. 233-240
- Beat the Mean BanditYisong Yue, Thorsten Joachims. 241-248
- Ultra-Fast Optimization Algorithm for Sparse Multi Kernel LearningFrancesco Orabona, Jie Luo. 249-256
- Estimating the Bayes Point Using Linear Knapsack ProblemsBrian Potetz. 257-264
- On optimization methods for deep learningQuoc V. Le, Jiquan Ngiam, Adam Coates, Ahbik Lahiri, Bobby Prochnow, Andrew Y. Ng. 265-272
- Multiclass Classification with Bandit Feedback using Adaptive RegularizationKoby Crammer, Claudio Gentile. 273-280
- On the Necessity of Irrelevant VariablesDavid P. Helmbold, Philip M. Long. 281-288
- ABC-EP: Expectation Propagation for Likelihoodfree Bayesian ComputationSimon Barthelmé, Nicolas Chopin. 289-296
- A PAC-Bayes Sample-compression Approach to Kernel MethodsPascal Germain, Alexandre Lacoste, François Laviolette, Mario Marchand, Sara Shanian. 297-304
- Integrating Partial Model Knowledge in Model Free RL AlgorithmsAviv Tamar, Dotan Di Castro, Ron Meir. 305-312
- Fast Newton-type Methods for Total Variation RegularizationÁlvaro Barbero Jiménez, Suvrit Sra. 313-320
- Parallel Coordinate Descent for L1-Regularized Loss MinimizationJoseph K. Bradley, Aapo Kyrola, Danny Bickson, Carlos Guestrin. 321-328
- Large-Scale Convex Minimization with a Low-Rank ConstraintShai Shalev-Shwartz, Alon Gonen, Ohad Shamir. 329-336
- Approximate Dynamic Programming for Storage ProblemsLauren Hannah, David B. Dunson. 337-344
- Online Submodular Minimization for Combinatorial StructuresStefanie Jegelka, Jeff Bilmes. 345-352
- Minimal Loss Hashing for Compact Binary CodesMohammad Norouzi 0002, David J. Fleet. 353-360
- The Hierarchical Beta Process for Convolutional Factor Analysis and Deep LearningBo Chen, Gungor Polatkan, Guillermo Sapiro, David B. Dunson, Lawrence Carin. 361-368
- Simultaneous Learning and Covering with Adversarial NoiseAndrew Guillory, Jeff Bilmes. 369-376
- Topic Modeling with Nonparametric Markov TreeHaojun Chen, David B. Dunson, Lawrence Carin. 377-384
- Relational Active Learning for Joint Collective Classification ModelsAnkit Kuwadekar, Jennifer Neville. 385-392
- A Co-training Approach for Multi-view Spectral ClusteringAbhishek Kumar, Hal Daumé III. 393-400
- Learning from Multiple OutlooksMaayan Harel, Shie Mannor. 401-408
- Adaptive Kernel Approximation for Large-Scale Non-Linear SVM PredictionMichele Cossalter, Rong Yan, Lu Zheng. 409-416
- Risk-Based Generalizations of f-divergencesDario García-García, Ulrike von Luxburg, Raúl Santos-Rodríguez. 417-424
- Learning Multi-View Neighborhood Preserving ProjectionsNovi Quadrianto, Christoph H. Lampert. 425-432
- Better Algorithms for Selective SamplingFrancesco Orabona, Nicolò Cesa-Bianchi. 433-440
- Minimax Learning Rates for Bipartite Ranking and Plug-in RulesSylvain Robbiano, Stéphan Clémençon. 441-448
- Task Space Retrieval Using Inverse Feedback ControlNikolay Jetchev, Marc Toussaint. 449-456
- Bayesian CCA via Group SparsitySeppo Virtanen, Arto Klami, Samuel Kaski. 457-464
- PILCO: A Model-Based and Data-Efficient Approach to Policy SearchMarc Peter Deisenroth, Carl Edward Rasmussen. 465-472
- Suboptimal Solution Path Algorithm for Support Vector MachineMasayuki Karasuyama, Ichiro Takeuchi. 473-480
- Incremental Basis Construction from Temporal Difference ErrorYi Sun, Faustino J. Gomez, Mark B. Ring, Jürgen Schmidhuber. 481-488
- Predicting Legislative Roll Calls from TextSean Gerrish, David M. Blei. 489-496
- On Bayesian PCA: Automatic Dimensionality Selection and Analytic SolutionShinichi Nakajima, Masashi Sugiyama, S. Derin Babacan. 497-504
- Learning Linear Functions with Quadratic and Linear Multiplicative UpdatesTom Bylander. 505-512
- Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning ApproachXavier Glorot, Antoine Bordes, Yoshua Bengio. 513-520
- Learning with Whom to Share in Multi-task Feature LearningZhuoliang Kang, Kristen Grauman, Fei Sha. 521-528
- Boosting on a Budget: Sampling for Feature-Efficient PredictionLev Reyzin. 529-536
- Speeding-Up Hoeffding-Based Regression Trees With OptionsElena Ikonomovska, João Gama, Bernard Zenko, Saso Dzeroski. 537-544
- Linear Regression under Fixed-Rank Constraints: A Riemannian ApproachGilles Meyer, Silvere Bonnabel, Rodolphe Sepulchre. 545-552
- Cauchy Graph EmbeddingDijun Luo, Chris H. Q. Ding, Feiping Nie, Heng Huang. 553-560
- Uncovering the Temporal Dynamics of Diffusion NetworksManuel Gomez-Rodriguez, David Balduzzi, Bernhard Schölkopf. 561-568
- Multiclass Boosting with Hinge Loss based on Output CodingTianshi Gao, Daphne Koller. 569-576
- Approximation Bounds for Inference using Cooperative CutsStefanie Jegelka, Jeff Bilmes. 577-584
- Brier Curves: a New Cost-Based Visualisation of Classifier PerformanceJosé Hernández-Orallo, Peter A. Flach, Cèsar Ferri Ramirez. 585-592
- Semi-supervised Penalized Output Kernel Regression for Link PredictionCéline Brouard, Florence d'Alché-Buc, Marie Szafranski. 593-600
- A New Bayesian Rating System for Team CompetitionsSergey I. Nikolenko, Alexander Sirotkin. 601-608
- OptiML: An Implicitly Parallel Domain-Specific Language for Machine LearningArvind K. Sujeeth, HyoukJoong Lee, Kevin J. Brown, Tiark Rompf, Hassan Chafi, Michael Wu, Anand R. Atreya, Martin Odersky, Kunle Olukotun. 609-616
- Infinite SVM: a Dirichlet Process Mixture of Large-margin Kernel MachinesJun Zhu, Ning Chen, Eric P. Xing. 617-624
- On the Integration of Topic Modeling and Dictionary LearningLingbo Li, Mingyuan Zhou, Guillermo Sapiro, Lawrence Carin. 625-632
- Piecewise Bounds for Estimating Bernoulli-Logistic Latent Gaussian ModelsBenjamin M. Marlin, Mohammad Emtiyaz Khan, Kevin P. Murphy. 633-640
- Access to Unlabeled Data can Speed up Prediction TimeRuth Urner, Shai Shalev-Shwartz, Shai Ben-David. 641-648
- From PAC-Bayes Bounds to Quadratic Programs for Majority VotesJean-Francis Roy, François Laviolette, Mario Marchand. 649-656
- A Coherent Interpretation of AUC as a Measure of Aggregated Classification PerformancePeter A. Flach, José Hernández-Orallo, Cèsar Ferri Ramirez. 657-664
- Support Vector Machines as Probabilistic ModelsVojtech Franc, Alexander Zien, Bernhard Schölkopf. 665-672
- Adaptively Learning the Crowd KernelOmer Tamuz, Ce Liu, Serge Belongie, Ohad Shamir, Adam Kalai. 673-680
- Bayesian Learning via Stochastic Gradient Langevin DynamicsMax Welling, Yee Whye Teh. 681-688
- Multimodal Deep LearningJiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee, Andrew Y. Ng. 689-696
- On the Robustness of Kernel Density M-EstimatorsJooSeuk Kim, Clayton D. Scott. 697-704
- Beam Search based MAP Estimates for the Indian Buffet ProcessPiyush Rai, Hal Daumé III. 705-712
- Optimal Distributed Online PredictionOfer Dekel, Ran Gilad-Bachrach, Ohad Shamir, Lin Xiao. 713-720
- Message Passing Algorithms for the Dirichlet Diffusion TreeDavid A. Knowles, Jurgen Van Gael, Zoubin Ghahramani. 721-728
- Convex Max-Product over Compact Sets for Protein FoldingJian Peng 0001, Tamir Hazan, David A. McAllester, Raquel Urtasun. 729-736
- Structure Learning in Ergodic Factored MDPs without Knowledge of the Transition Function's In-DegreeDoran Chakraborty, Peter Stone. 737-744
- Clusterpath: an Algorithm for Clustering using Convex Fusion PenaltiesToby Hocking, Jean-Philippe Vert, Francis Bach, Armand Joulin. 745-752
- Tree preserving embeddingAlbert Shieh, Tatsunori Hashimoto, Edo Airoldi. 753-760
- Clustering by Left-Stochastic Matrix FactorizationRaman Arora, Maya R. Gupta, Amol Kapila, Maryam Fazel. 761-768
- The Infinite Regionalized Policy RepresentationMiao Liu, Xuejun Liao, Lawrence Carin. 769-776
- SampleRank: Training Factor Graphs with Atomic GradientsMichael L. Wick, Khashayar Rohanimanesh, Kedar Bellare, Aron Culotta, Andrew McCallum. 777-784
- Tree-Structured Infinite Sparse Factor ModelXianXing Zhang, David B. Dunson, Lawrence Carin. 785-792
- Preserving Personalized Pagerank in SubgraphsAndrea Vattani, Deepayan Chakrabarti, Maxim Gurevich. 793-800
- Hierarchical Classification via Orthogonal TransferLin Xiao, Dengyong Zhou, Mingrui Wu. 801-808
- A Three-Way Model for Collective Learning on Multi-Relational DataMaximilian Nickel, Volker Tresp, Hans-Peter Kriegel. 809-816
- Variational Inference for Policy Search in changing situationsGerhard Neumann. 817-824
- Learning Scoring Functions with Order-Preserving Losses and Standardized SupervisionDavid Buffoni, Clément Calauzènes, Patrick Gallinari, Nicolas Usunier. 825-832
- Contractive Auto-Encoders: Explicit Invariance During Feature ExtractionSalah Rifai, Pascal Vincent, Xavier Muller, Xavier Glorot, Yoshua Bengio. 833-840
- Variational Heteroscedastic Gaussian Process RegressionMiguel Lázaro-Gredilla, Michalis K. Titsias. 841-848
- Bounding the Partition Function using Holder's InequalityQiang Liu, Alexander T. Ihler. 849-856
- Dynamic Egocentric Models for Citation NetworksDuy Quang Vu, Arthur U. Asuncion, David R. Hunter, Padhraic Smyth. 857-864
- The Constrained Weight Space SVM: Learning with Ranked FeaturesKevin Small, Byron C. Wallace, Carla E. Brodley, Thomas A. Trikalinos. 865-872
- Robust Matrix Completion and Corrupted ColumnsYudong Chen, Huan Xu, Constantine Caramanis, Sujay Sanghavi. 873-880
- Online Discovery of Feature DependenciesAlborz Geramifard, Finale Doshi, Josh Redding, Nicholas Roy, Jonathan P. How. 881-888
- Variational Inference for Stick-Breaking Beta Process PriorsJohn William Paisley, Lawrence Carin, David M. Blei. 889-896
- Apprenticeship Learning About Multiple IntentionsMonica Babes, Vukosi N. Marivate, Kaushik Subramanian, Michael L. Littman. 897-904
- Minimum Probability Flow LearningJascha Sohl-Dickstein, Peter Battaglino, Michael R. DeWeese. 905-912
- Infinite Dynamic Bayesian NetworksFinale Doshi, David Wingate, Joshua B. Tenenbaum, Nicholas Roy. 913-920
- The Importance of Encoding Versus Training with Sparse Coding and Vector QuantizationAdam Coates, Andrew Y. Ng. 921-928
- Fast Global Alignment KernelsMarco Cuturi. 929-936
- Learning attentional policies for tracking and recognition in video with deep networksLoris Bazzani, Nando de Freitas, Hugo Larochelle, Vittorio Murino, Jo-Anne Ting. 937-944
- Large-Scale Learning of Embeddings with Reconstruction SamplingYann Dauphin, Xavier Glorot, Yoshua Bengio. 945-952
- Automatic Feature Decomposition for Single View Co-trainingMinmin Chen, Kilian Q. Weinberger, Yixin Chen. 953-960
- Mapping kernels for treesKilho Shin, Marco Cuturi, Tetsuji Kuboyama. 961-968
- Stochastic Low-Rank Kernel Learning for RegressionPierre Machart, Thomas Peel, Sandrine Anthoine, Liva Ralaivola, Hervé Glotin. 969-976
- Size-constrained Submodular Minimization through Minimum Norm BaseKiyohito Nagano, Yoshinobu Kawahara, Kazuyuki Aihara. 977-984
- Locally Linear Support Vector MachinesLubor Ladicky, Philip H. S. Torr. 985-992
- Functional Regularized Least Squares Classication with Operator-valued KernelsHachem Kadri, Asma Rabaoui, Philippe Preux, Emmanuel Duflos, Alain Rakotomamonjy. 993-1000
- Clustering Partially Observed Graphs via Convex OptimizationAli Jalali, Yudong Chen, Sujay Sanghavi, Huan Xu. 1001-1008
- On the Use of Variational Inference for Learning Discrete Graphical ModelEunho Yang, Pradeep D. Ravikumar. 1009-1016
- Generating Text with Recurrent Neural NetworksIlya Sutskever, James Martens, Geoffrey E. Hinton. 1017-1024
- Probabilistic Matrix AdditionAmrudin Agovic, Arindam Banerjee, Snigdhansu Chatterjee. 1025-1032
- Learning Recurrent Neural Networks with Hessian-Free OptimizationJames Martens, Ilya Sutskever. 1033-1040
- Sparse Additive Generative Models of TextJacob Eisenstein, Amr Ahmed, Eric P. Xing. 1041-1048
- Classification-based Policy Iteration with a CriticVictor Gabillon, Alessandro Lazaric, Mohammad Ghavamzadeh, Bruno Scherrer. 1049-1056
- Submodular meets Spectral: Greedy Algorithms for Subset Selection, Sparse Approximation and Dictionary SelectionAbhimanyu Das, David Kempe. 1057-1064
- A Spectral Algorithm for Latent Tree Graphical ModelsAnkur P. Parikh, Le Song, Eric P. Xing. 1065-1072
- A Unified Probabilistic Model for Global and Local Unsupervised Feature SelectionYue Guan, Jennifer G. Dy, Michael I. Jordan. 1073-1080
- Towards Making Unlabeled Data Never HurtYu-Feng Li, Zhi-Hua Zhou. 1081-1088
- On Random Weights and Unsupervised Feature LearningAndrew M. Saxe, Pang Wei Koh, Zhenghao Chen, Maneesh Bhand, Bipin Suresh, Andrew Y. Ng. 1089-1096
- Doubly Robust Policy Evaluation and LearningMiroslav Dudík, John Langford, Lihong Li. 1097-1104
- Learning Deep Energy ModelsJiquan Ngiam, Zhenghao Chen, Pang Wei Koh, Andrew Y. Ng. 1105-1112
- Bipartite Ranking through Minimization of Univariate LossWojciech Kotlowski, Krzysztof Dembczynski, Eyke Hüllermeier. 1113-1120
- Manifold Identification of Dual Averaging Methods for Regularized Stochastic Online LearningSangkyun Lee, Stephen J. Wright. 1121-1128
- Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensionsAlekh Agarwal, Sahand Negahban, Martin J. Wainwright. 1129-1136
- Bundle Selling by Online Estimation of Valuation FunctionsDaniel Vainsencher, Ofer Dekel, Shie Mannor. 1137-1144
- Unsupervised Models of Images by Spikeand-Slab RBMsAaron C. Courville, James Bergstra, Yoshua Bengio. 1145-1152
- Approximating Correlated Equilibria using Relaxations on the Marginal PolytopeHetunandan Kamisetty, Eric P. Xing, Christopher James Langmead. 1153-1160
- Active Learning from CrowdsYan Yan, Rómer Rosales, Glenn Fung, Jennifer G. Dy. 1161-1168
- Computational Rationalization: The Inverse Equilibrium ProblemKevin Waugh, Brian D. Ziebart, Drew Bagnell. 1169-1176
- Finite-Sample Analysis of Lasso-TDMohammad Ghavamzadeh, Alessandro Lazaric, Rémi Munos, Matthew W. Hoffman. 1177-1184
- Generalized Value Functions for Large Action SetsJason Pazis, Ronald Parr. 1185-1192
- k-DPPs: Fixed-Size Determinantal Point ProcessesAlex Kulesza, Ben Taskar. 1193-1200
- On Autoencoders and Score Matching for Energy Based ModelsKevin Swersky, Marc'Aurelio Ranzato, David Buchman, Benjamin M. Marlin, Nando de Freitas. 1201-1208
- Generalized Boosting Algorithms for Convex OptimizationAlexander Grubb, Drew Bagnell. 1209-1216