Abstract is missing.
- Preface1-2 [doi]
- On the Complexity of Bandit and Derivative-Free Stochastic Convex OptimizationOhad Shamir. 3-24 [doi]
- A Theoretical Analysis of NDCG Type Ranking MeasuresYining Wang, Liwei Wang, Yuanzhi Li, Di He, Tie-Yan Liu. 25-54 [doi]
- Excess risk bounds for multitask learning with trace norm regularizationMassimiliano Pontil, Andreas Maurer. 55-76 [doi]
- Honest Compressions and Their Application to Compression SchemesRoi Livni, Pierre Simon. 77-92 [doi]
- The price of bandit information in multiclass online classificationAmit Daniely, Tom Helbertal. 93-104 [doi]
- Estimation of Extreme Values and Associated Level Sets of a Regression Function via Selective SamplingStanislav Minsker. 105-121 [doi]
- Bounded regret in stochastic multi-armed banditsSébastien Bubeck, Vianney Perchet, Philippe Rigollet. 122-134 [doi]
- Recovering the Optimal Solution by Dual Random ProjectionLijun Zhang, Mehrdad Mahdavi, Rong Jin, Tianbao Yang, Shenghuo Zhu. 135-157 [doi]
- Opportunistic Strategies for Generalized No-Regret ProblemsAndrey Bernstein, Shie Mannor, Nahum Shimkin. 158-171 [doi]
- Online Learning for Time Series PredictionOren Anava, Elad Hazan, Shie Mannor, Ohad Shamir. 172-184 [doi]
- Sharp analysis of low-rank kernel matrix approximationsFrancis Bach. 185-209 [doi]
- Beating Bandits in Gradually Evolving WorldsChao-Kai Chiang, Chia-Jung Lee, Chi-Jen Lu. 210-227 [doi]
- Information Complexity in Bandit Subset SelectionEmilie Kaufmann, Shivaram Kalyanakrishnan. 228-251 [doi]
- Passive Learning with Target RiskMehrdad Mahdavi, Rong Jin. 252-269 [doi]
- Blind Signal Separation in the Presence of Gaussian NoiseMikhail Belkin, Luis Rademacher, James Voss. 270-287 [doi]
- Active and passive learning of linear separators under log-concave distributionsMaria-Florina Balcan, Philip M. Long. 288-316 [doi]
- Randomized partition trees for exact nearest neighbor searchSanjoy Dasgupta, Kaushik Sinha. 317-337 [doi]
- Surrogate Regret Bounds for the Area Under the ROC Curve via Strongly Proper LossesShivani Agarwal. 338-353 [doi]
- Algorithms and Hardness for Robust Subspace RecoveryMoritz Hardt, Ankur Moitra. 354-375 [doi]
- PLAL: Cluster-based active learningRuth Urner, Sharon Wulff, Shai Ben-David. 376-397 [doi]
- Learning Using Local Membership QueriesPranjal Awasthi, Vitaly Feldman, Varun Kanade. 398-431 [doi]
- Sparse Adaptive Dirichlet-Multinomial-like ProcessesMarcus Hutter. 432-459 [doi]
- Prediction by random-walk perturbationLuc Devroye, Gábor Lugosi, Gergely Neu. 460-473 [doi]
- Approachability, fast and slowVianney Perchet, Shie Mannor. 474-488 [doi]
- Classification with Asymmetric Label Noise: Consistency and Maximal DenoisingClayton Scott, Gilles Blanchard, Gregory Handy. 489-511 [doi]
- General Oracle Inequalities for Gibbs Posterior with Application to RankingCheng Li, Wenxin Jiang, Martin A. Tanner. 512-521 [doi]
- Learning Halfspaces Under Log-Concave Densities: Polynomial Approximations and Moment MatchingDaniel M. Kane, Adam Klivans, Raghu Meka. 522-545 [doi]
- Subspace Embeddings and \(\ell_p\)-Regression Using Exponential Random VariablesDavid P. Woodruff, Qin Zhang. 546-567 [doi]
- Consistency of Robust Kernel Density EstimatorsRobert Vandermeulen, Clayton Scott. 568-591 [doi]
- Divide and Conquer Kernel Ridge RegressionYuchen Zhang, John C. Duchi, Martin J. Wainwright. 592-617 [doi]
- Regret Minimization for Branching ExpertsEyal Gofer, Nicolò Cesa-Bianchi, Claudio Gentile, Yishay Mansour. 618-638 [doi]
- Horizon-Independent Optimal Prediction with Log-Loss in Exponential FamiliesPeter L. Bartlett, Peter Grünwald, Peter Harremoës, Fares Hedayati, Wojciech Kotlowski. 639-661 [doi]
- Online Similarity Prediction of Networked Data from Known and Unknown GraphsClaudio Gentile, Mark Herbster, Stephen Pasteris. 662-695 [doi]
- A near-optimal algorithm for finite partial-monitoring games against adversarial opponentsGábor Bartók. 696-710 [doi]
- Representation, Approximation and Learning of Submodular Functions Using Low-rank Decision TreesVitaly Feldman, Pravesh Kothari, Jan Vondrák. 711-740 [doi]
- A Tale of Two Metrics: Simultaneous Bounds on Competitiveness and RegretLachlan L. H. Andrew, Siddharth Barman, Katrina Ligett, Minghong Lin, Adam Meyerson, Alan Roytman, Adam Wierman. 741-763 [doi]
- Optimal Probability Estimation with Applications to Prediction and ClassificationJayadev Acharya, Ashkan Jafarpour, Alon Orlitsky, Ananda Theertha Suresh. 764-796 [doi]
- Polynomial Time Optimal Query Algorithms for Finding Graphs with Arbitrary Real WeightsSung-Soon Choi. 797-818 [doi]
- Differentially Private Feature Selection via Stability Arguments, and the Robustness of the LassoAbhradeep Thakurta, Adam Smith. 819-850 [doi]
- Learning a set of directionsWouter M. Koolen, Jiazhong Nie, Manfred K. Warmuth. 851-866 [doi]
- A Tensor Spectral Approach to Learning Mixed Membership Community ModelsAnimashree Anandkumar, Rong Ge, Daniel Hsu, Sham Kakade. 867-881 [doi]
- Adaptive Crowdsourcing Algorithms for the Bandit Survey ProblemIttai Abraham, Omar Alonso, Vasilis Kandylas, Aleksandrs Slivkins. 882-910 [doi]
- Boosting with the Logistic Loss is ConsistentMatus Telgarsky. 911-965 [doi]
- Competing With StrategiesWei Han, Alexander Rakhlin, Karthik Sridharan. 966-992 [doi]
- Online Learning with Predictable SequencesAlexander Rakhlin, Karthik Sridharan. 993-1019 [doi]
- Efficient Learning of SimplicesJoseph Anderson, Navin Goyal, Luis Rademacher. 1020-1045 [doi]
- Complexity Theoretic Lower Bounds for Sparse Principal Component DetectionQuentin Berthet, Philippe Rigollet. 1046-1066 [doi]
- Open Problem: Adversarial Multiarmed Bandits with Limited AdviceYevgeny Seldin, Koby Crammer, Peter L. Bartlett. 1067-1072 [doi]
- Open Problem: Fast Stochastic Exp-Concave OptimizationTomer Koren. 1073-1075 [doi]
- Open Problem: Lower bounds for Boosting with Hadamard MatricesJiazhong Nie, Manfred K. Warmuth, S. V. N. Vishwanathan, Xinhua Zhang. 1076-1079 [doi]