COLT 2010
The 23rd Annual Conference on Learning Theory (COLT 2010) was held in Haifa, Israel on June 27-29, 2010.
Archive of conference website
Full proceedings
- Best Arm Identification in Multi-Armed Bandits
Jean-Yves Audibert, Sébastien Bubeck, Rémi Munos
- The Online Loop-free Stochastic Shortest-Path Problem
Andras Gyorgy, Gergely Neu, Csaba Szepesvari
- Sequence prediction in realizable and non-realizable cases
Daniil Ryabko
- Efficient classification for metric data
Lee-Ad Gottlieb, Aryeh (Leonid) Kontorovich, Robi Krauthgamer
- Composite Objective Mirror Descent
John Duchi, Shai Shalev-Shwartz, Yoram Singer, Ambuj Tewari
- Active Learning on Trees and Graphs
Nicolo' Cesa-Bianchi, Claudio Gentile, Fabio Vitale, Giovanni Zappella
- Robust Hierarchical Clustering
Maria-Florina Balcan, Pramod Gupta
- Evolution with Drifting Targets
Varun Kanade, Leslie G. Valiant, Jennifer Wortman Vaughan
- Nonparametric Bandits with Covariates
Philippe Rigollet, Assaf Zeevi
- Forest Density Estimation
Anupam Gupta, John Lafferty, Han Liu, Larry Wasserman, Min Xu
- Characterization of Linkage-based Clustering
Margareta Ackerman, Shai Ben-David, David Loker
- Learning rotations with little regret
Elad Hazan, Satyen Kale, Manfred Warmuth
- Adaptive Bound Optimization for Online Convex Optimization
H. Brendan McMahan, Matthew Streeter
- Convex Games in Banach Spaces
Karthik Sridharan, Ambuj Tewari
- Robustness and Generalization
Huan Xu, Shie Mannor
- An Asymptotically Optimal Bandit Algorithm for Bounded Support Models
Junya Honda, Akimichi Takemura
- Toward Learning Gaussian Mixtures with Arbitrary Separation
Mikhail Belkin, Kaushik Sinha
- Open Loop Optimistic Planning
Sébastien Bubeck, Rémi Munos
- Sparse Recovery in Convex Hulls of Infinite Dictionaries
Vladimir Koltchinskii, Stas Minsker
- Learning to create is as hard as learning to appreciate
David Xiao
- Learning Kernel-Based Halfspaces with the Zero-One Loss
Ohad Shamir, Shai Shalev-Shwartz, Karthik Sridharan
- Deterministic Sparse Fourier Approximation via Fooling Arithmetic Progressions
Adi Akavia
- Principal Component Analysis with Contaminated Data: The High Dimensional Case
Huan Xu, Constantine Caramanis, Shie Mannor
- Robust Selective Sampling from Single and Multiple Teachers
Ofer Dekel, Claudio Gentile, Karthik Sridharan
- Learning with Global Cost in Stochastic Environments
Eyal Even-Dar, Shie Mannor, Yishay Mansour
- Mansour's Conjecture is True for Random DNF Formulas
Adam Klivans, Homin Lee, Andrew Wan
- Online Learning of Noisy Data with Kernels
Nicolo Cesa-Bianchi, Shai Shalev-Shwartz, Ohad Shamir
- Inferring Descriptive Generalisations of Formal Languages
Dominik Freydenberger, Daniel Reidenbach
- Strongly Non-U-Shaped Learning Results by General Techniques
Timo Kötzing, John Case
- Hedging Structured Concepts
Wouter M. Koolen, Manfred K. Warmuth, Jyrki Kivinen
- Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
John Duchi, Elad Hazan, Yoram Singer
- Ranking with kernels in Fourier space
Risi Kondor, Marconi Barbosa
- Regret Minimization with Concept Drift
Koby Crammer, Eyal Even-Dar, Yishay Mansour, Jennifer Wortman Vaughan
- Regret Minimization for Online Buffering Problems Using the Weighted Majority Algorithm
Sascha Geulen, Berthold Voecking, Melanie Winkler
- Following the Flattened Leader
Wojciech Kotlowski, Peter Grunwald, Steven de Rooij
- Causal Markov condition for submodular information measures
Bastian Steudel, Dominik Janzing, Bernhard Schoelkopf
- Optimal Algorithms for Online Convex Optimization with Multi-Point Bandit Feedback
Alekh Agarwal, Ofer Dekel, Lin Xiao
- Theoretical Justification of Popular Link Prediction Heuristics
Purnamrita Sarkar, Deepayan Chakrabarti, Andrew Moore
- Quantum Predictive Learning and Communication Complexity with Single Input
Dmitry Gavinsky
- Adaptive Submodularity: A New Approach to Active Learning and Stochastic Optimization
Daniel Golovin, Andreas Krause
- Improved Guarantees for Agnostic Learning of Disjunctions
Pranjal Awasthi, Avrim Blum, Or Sheffet