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ORCID28th COLT 2015: Paris, France
- Peter Grünwald, Elad Hazan, Satyen Kale:
Proceedings of The 28th Conference on Learning Theory, COLT 2015, Paris, France, July 3-6, 2015. JMLR Workshop and Conference Proceedings 40, JMLR.org 2015
Preface
- Peter Grünwald, Elad Hazan:
Conference on Learning Theory 2015: Preface. 1-3
Regular Papers
- Arpit Agarwal, Shivani Agarwal:
On Consistent Surrogate Risk Minimization and Property Elicitation. 4-22 - Noga Alon, Nicolò Cesa-Bianchi, Ofer Dekel, Tomer Koren:
Online Learning with Feedback Graphs: Beyond Bandits. 23-35 - Animashree Anandkumar, Rong Ge, Majid Janzamin:
Learning Overcomplete Latent Variable Models through Tensor Methods. 36-112 - Sanjeev Arora, Rong Ge, Tengyu Ma, Ankur Moitra:
Simple, Efficient, and Neural Algorithms for Sparse Coding. 113-149 - Pranjal Awasthi, Moses Charikar, Kevin A. Lai, Andrej Risteski:
Label optimal regret bounds for online local learning. 150-166 - Pranjal Awasthi, Maria-Florina Balcan, Nika Haghtalab, Ruth Urner:
Efficient Learning of Linear Separators under Bounded Noise. 167-190 - Maria-Florina Balcan, Avrim Blum, Santosh S. Vempala:
Efficient Representations for Lifelong Learning and Autoencoding. 191-210 - Akshay Balsubramani, Yoav Freund:
Optimally Combining Classifiers Using Unlabeled Data. 211-225 - Peter L. Bartlett, Wouter M. Koolen, Alan Malek, Eiji Takimoto, Manfred K. Warmuth:
Minimax Fixed-Design Linear Regression. 226-239 - Alexandre Belloni, Tengyuan Liang, Hariharan Narayanan, Alexander Rakhlin:
Escaping the Local Minima via Simulated Annealing: Optimization of Approximately Convex Functions. 240-265 - Sébastien Bubeck, Ofer Dekel, Tomer Koren, Yuval Peres:
Bandit Convex Optimization: \(\sqrt{T}\) Regret in One Dimension. 266-278 - Sébastien Bubeck, Ronen Eldan:
The entropic barrier: a simple and optimal universal self-concordant barrier. 279 - Yang Cai
, Constantinos Daskalakis, Christos H. Papadimitriou:
Optimum Statistical Estimation with Strategic Data Sources. 280-296 - Nicolò Cesa-Bianchi, Yishay Mansour, Ohad Shamir:
On the Complexity of Learning with Kernels. 297-325 - Hubie Chen, Matthew Valeriote:
Learnability of Solutions to Conjunctive Queries: The Full Dichotomy. 326-337 - Yuxin Chen, S. Hamed Hassani, Amin Karbasi, Andreas Krause:
Sequential Information Maximization: When is Greedy Near-optimal? 338-363 - Dehua Cheng, Yu Cheng, Yan Liu, Richard Peng, Shang-Hua Teng:
Efficient Sampling for Gaussian Graphical Models via Spectral Sparsification. 364-390 - Peter Chin, Anup Rao, Van Vu:
Stochastic Block Model and Community Detection in Sparse Graphs: A spectral algorithm with optimal rate of recovery. 391-423 - Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri, Manfred K. Warmuth:
On-Line Learning Algorithms for Path Experts with Non-Additive Losses. 424-447 - Rachel Cummings, Stratis Ioannidis, Katrina Ligett:
Truthful Linear Regression. 448-483 - Amit Daniely:
A PTAS for Agnostically Learning Halfspaces. 484-502 - Gautam Dasarathy, Robert D. Nowak, Xiaojin Zhu:
S2: An Efficient Graph Based Active Learning Algorithm with Application to Nonparametric Classification. 503-522 - Yash Deshpande, Andrea Montanari:
Improved Sum-of-Squares Lower Bounds for Hidden Clique and Hidden Submatrix Problems. 523-562 - Miroslav Dudík, Katja Hofmann, Robert E. Schapire, Aleksandrs Slivkins, Masrour Zoghi:
Contextual Dueling Bandits. 563-587 - Justin Eldridge, Mikhail Belkin, Yusu Wang:
Beyond Hartigan Consistency: Merge Distortion Metric for Hierarchical Clustering. 588-606 - Moein Falahatgar, Ashkan Jafarpour, Alon Orlitsky, Venkatadheeraj Pichapati, Ananda Theertha Suresh:
Faster Algorithms for Testing under Conditional Sampling. 607-636 - Uriel Feige, Yishay Mansour, Robert E. Schapire:
Learning and inference in the presence of corrupted inputs. 637-657 - Nicolas Flammarion, Francis R. Bach:
From Averaging to Acceleration, There is Only a Step-size. 658-695 - Dean P. Foster, Howard J. Karloff, Justin Thaler:
Variable Selection is Hard. 696-709 - Rafael M. Frongillo, Ian A. Kash:
Vector-Valued Property Elicitation. 710-727 - Roy Frostig, Rong Ge, Sham M. Kakade, Aaron Sidford:
Competing with the Empirical Risk Minimizer in a Single Pass. 728-763 - Pierre Gaillard, Sébastien Gerchinovitz:
A Chaining Algorithm for Online Nonparametric Regression. 764-796 - Rong Ge, Furong Huang, Chi Jin, Yang Yuan:
Escaping From Saddle Points - Online Stochastic Gradient for Tensor Decomposition. 797-842 - Nicolas Goix, Anne Sabourin, Stéphan Clémençon:
Learning the dependence structure of rare events: a non-asymptotic study. 843-860 - Aditya Gopalan, Shie Mannor:
Thompson Sampling for Learning Parameterized Markov Decision Processes. 861-898 - Bruce E. Hajek, Yihong Wu, Jiaming Xu:
Computational Lower Bounds for Community Detection on Random Graphs. 899-928 - Zaïd Harchaoui, Anatoli B. Juditsky, Arkadi Nemirovski, Dmitry Ostrovsky:
Adaptive Recovery of Signals by Convex Optimization. 929-955 - Samuel B. Hopkins, Jonathan Shi, David Steurer:
Tensor principal component analysis via sum-of-square proofs. 956-1006 - Prateek Jain, Praneeth Netrapalli:
Fast Exact Matrix Completion with Finite Samples. 1007-1034 - Parameswaran Kamalaruban, Robert C. Williamson, Xinhua Zhang:
Exp-Concavity of Proper Composite Losses. 1035-1065 - Sudeep Kamath, Alon Orlitsky, Dheeraj Pichapati, Ananda Theertha Suresh:
On Learning Distributions from their Samples. 1066-1100 - Varun Kanade, Elchanan Mossel:
MCMC Learning. 1101-1128 - Zohar Shay Karnin, Edo Liberty:
Online with Spectral Bounds. 1129-1140 - Junpei Komiyama, Junya Honda, Hisashi Kashima, Hiroshi Nakagawa:
Regret Lower Bound and Optimal Algorithm in Dueling Bandit Problem. 1141-1154 - Wouter M. Koolen, Tim van Erven:
Second-order Quantile Methods for Experts and Combinatorial Games. 1155-1175 - Samory Kpotufe, Ruth Urner, Shai Ben-David:
Hierarchical Label Queries with Data-Dependent Partitions. 1176-1189 - Rasmus Kyng, Anup Rao, Sushant Sachdeva, Daniel A. Spielman:
Algorithms for Lipschitz Learning on Graphs. 1190-1223 - Jean Lafond:
Low Rank Matrix Completion with Exponential Family Noise. 1224-1243 - Jan Leike, Marcus Hutter:
Bad Universal Priors and Notions of Optimality. 1244-1259 - Tengyuan Liang, Alexander Rakhlin, Karthik Sridharan:
Learning with Square Loss: Localization through Offset Rademacher Complexity. 1260-1285 - Haipeng Luo, Robert E. Schapire:
Achieving All with No Parameters: AdaNormalHedge. 1286-1304 - Mehrdad Mahdavi, Lijun Zhang, Rong Jin:
Lower and Upper Bounds on the Generalization of Stochastic Exponentially Concave Optimization. 1305-1320 - Konstantin Makarychev, Yury Makarychev, Aravindan Vijayaraghavan:
Correlation Clustering with Noisy Partial Information. 1321-1342 - Issei Matsumoto, Kohei Hatano, Eiji Takimoto:
Online Density Estimation of Bradley-Terry Models. 1343-1359 - Gergely Neu:
First-order regret bounds for combinatorial semi-bandits. 1360-1375 - Behnam Neyshabur, Ryota Tomioka, Nathan Srebro:
Norm-Based Capacity Control in Neural Networks. 1376-1401 - Christos H. Papadimitriou, Santosh S. Vempala:
Cortical Learning via Prediction. 1402-1422 - Richard Peng, He Sun, Luca Zanetti:
Partitioning Well-Clustered Graphs: Spectral Clustering Works! 1423-1455 - Vianney Perchet, Philippe Rigollet, Sylvain Chassang, Erik Snowberg:
Batched Bandit Problems. 1456 - Alexander Rakhlin, Karthik Sridharan:
Hierarchies of Relaxations for Online Prediction Problems with Evolving Constraints. 1457-1479 - Patrick Rebeschini, Amin Karbasi:
Fast Mixing for Discrete Point Processes. 1480-1500 - Mark D. Reid, Rafael M. Frongillo, Robert C. Williamson, Nishant A. Mehta:
Generalized Mixability via Entropic Duality. 1501-1522 - Ohad Shamir:
On the Complexity of Bandit Linear Optimization. 1523-1551 - Hans Ulrich Simon:
An Almost Optimal PAC Algorithm. 1552-1563 - Jacob Steinhardt, John C. Duchi:
Minimax rates for memory-bounded sparse linear regression. 1564-1587 - Thomas Steinke, Jonathan R. Ullman:
Interactive Fingerprinting Codes and the Hardness of Preventing False Discovery. 1588-1628 - Matus Telgarsky, Miroslav Dudík:
Convex Risk Minimization and Conditional Probability Estimation. 1629-1682 - Christos Thrampoulidis, Samet Oymak, Babak Hassibi:
Regularized Linear Regression: A Precise Analysis of the Estimation Error. 1683-1709 - Santosh S. Vempala, Ying Xiao:
Max vs Min: Tensor Decomposition and ICA with nearly Linear Sample Complexity. 1710-1723 - Huizhen Yu:
On Convergence of Emphatic Temporal-Difference Learning. 1724-1751
Open Problems
- Arindam Banerjee, Sheng Chen, Vidyashankar Sivakumar:
Open Problem: Restricted Eigenvalue Condition for Heavy Tailed Designs. 1752-1755 - Anna Choromanska, Yann LeCun, Gérard Ben Arous:
Open Problem: The landscape of the loss surfaces of multilayer networks. 1756-1760 - Cristóbal Guzmán:
Open Problem: The Oracle Complexity of Smooth Convex Optimization in Nonstandard Settings. 1761-1763 - Wouter M. Koolen, Manfred K. Warmuth, Dmitry Adamskiy:
Open Problem: Online Sabotaged Shortest Path. 1764-1766 - Jeremy Kun, Lev Reyzin:
Open Problem: Learning Quantum Circuits with Queries. 1767-1769 - Hans Ulrich Simon, Sandra Zilles:
Open Problem: Recursive Teaching Dimension Versus VC Dimension. 1770-1772
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