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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