COLT 2013 Videos

Sebastien Bubeck mentioned on his blog that the videos for COLT2013 are out here
Invited Talks

• Learning Representations: A Challenge for Learning Theory Yann LeCun
• Is Intractability a Barrier for Machine Learning? Sanjeev Arora

Online Learning (I)

• Online Learning for Time Series Prediction Elad Hazan
• A Tale of Two Metrics: Simultaneous Bounds on Competitiveness and Regret Siddharth Barman
• Competing With Strategies Karthik Sridharan
• Online Learning with Predictable Sequences Alexander Rakhlin
• Approachability, fast and slow Vianney Perchet
• Horizon-Independent Optimal Prediction with Log-Loss in Exponential Families Fares Hedayati

Online Learning (II)

• Opportunistic Strategies for Generalized No-Regret Problems Andrey Bernstein
• Prediction by random-walk perturbation Gergely Neu
• Online Similarity Prediction of Networked Data from Known and Unknown Graphs Mark Herbster

Computational Learning Theory (I)

• Complexity Theoretic Lower Bounds for Sparse Principal Component Detection Quentin Berthet
• Learning Halfspaces Under Log-Concave Densities: Polynomial Approximations and Moment Matching Raghu Meka

Computational Learning Theory (II)

• Representation, Approximation and Learning of Submodular Functions Using Low-rank Decision Trees Pravesh Kothari
• Algorithms and Hardness for Robust Subspace Recovery Moritz Hardt

Computational Learning Theory (III)

• Efficient Learning of Simplices Luis Rademacher
• Randomized partition trees for exact nearest neighbor search Sanjoy Dasgupta

Unsupervised Learning

• A Tensor Spectral Approach to Learning Mixed Membership Community Models Rong Ge
• Optimal Probability Estimation with Applications to Prediction and Classification Ananda Theertha Suresh
• Blind Signal Separation in the Presence of Gaussian Noise James Voss
• Learning a set of directions Wouter M. Koolen
• Sparse Adaptive Dirichlet-Multinomial-like Processes Tor Lattimore

Dimensionality Reduction and Loss Function

• Subspace Embeddings and ℓp-Regression Using Exponential Random Variables Qin ZhangSurrogate Regret Bounds for the Area Under the ROC Curve via Strongly Proper Losses Shivani Agarwal
• A Theoretical Analysis of NDCG Type Ranking Measures Liwei Wang

Statistical Learning Theory (I)

• Passive Learning with Target Risk Mehrdad Mahdavi
• Classification with Asymmetric Label Noise: Consistency and Maximal Denoising Gilles Blanchard
• Divide and Conquer Kernel Ridge Regression Yuchen Zhang
• Sharp analysis of low-rank kernel matrix approximations Francis R. Bach
• Consistency of Robust Kernel Density Estimators Robert A. Vandermeulen
• Excess risk bounds for multitask learning with trace norm regularization Massimiliano Pontil
• General Oracle Inequalities for Gibbs Posterior with Application to Ranking Cheng Li
• Boosting with the Logistic Loss is Consistent Matus Telgarsky

Statistical Learning Theory (II)

• Honest Compressions and Their Application to Compression Schemes Roi Livni
• Differentially Private Feature Selection via Stability Arguments, and the Robustness of the Lasso Abhradeep Guha Thakurta

Active Learning

• Learning Using Local Membership Queries Pranjal Awasthi
• PLAL: Cluster-based active learning Ruth Urner
• Estimation of Extreme Values and Associated Level Sets of a Regression Function via Selective Sampling Stanislav Minsker
• Active and passive learning of linear separators under log-concave distributions Maria-Florina Balcan

Bandits

• On the Complexity of Bandit and Derivative-Free Stochastic Convex Optimization Ohad Shamir
• The price of bandit information in multiclass online classification Amit Daniely
• Bounded regret in stochastic multi-armed bandits Sébastien Bubeck
• Beating Bandits in Gradually Evolving Worlds Chia-Jung Lee
• Information Complexity in Bandit Subset Selection Emilie Kaufmann
• A near-optimal algorithm for finite partial-monitoring games against adversarial opponents Gábor Bartók
• Adaptive Crowdsourcing Algorithms for the Bandit Survey Problem Aleksandrs Slivkins

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