- Batched Bandit Problems
- Low Rank tustrix Completion with Exponential Family Noise
- Efficient Sampling for Gaussian Graphical Models via Spectral Sparsification
- A PTAS for Agnostically Learning Halfspaces
- Bad Universal Priors and Notions of Optimality
- Computational Lower Bounds for Community Detection on Random Graphs
- Contextual Dueling Bandits
- Online Density Estimation of Bradley-Terry Models
- On the Complexity of Bandit Linear Optimization
- On the Complexity of Learning with Kernels
- Learnability of Solutions to Conjunctive Queries: The Full Dichotomy
- An Almost Optimal PAC Algorithm
- Variable Selection is Hard
- Improved Sum-of-Squares Lower Bounds for Hidden Clique and Hidden Submatrix Problems
- Stochastic Block Model and Community Detection in the Sparse Graphs: A spectral algorithm with optimal rate of recovery
- Learning and inference in the presence of corrupted inputs
- Regret Lower Bound and Optimal Algorithm in Dueling Bandit Problem
- From Averaging to Acceleration, There is Only a Step-size
- Competing with the Empirical Risk Minimizer in a Single Pass
- Fast Exact Matrix Completion with Finite Samples
- On Convergence of Emphatic Temporal-Difference Learning
- Interactive Fingerprinting Codes and the Hardness of Preventing False Discovery
- Fast Mixing for Discrete Point Processes
- The entropic barrier: a simple and optimal universal self-concordant barrier
- Online PCA with Spectral Bounds
- Online Learning with Feedback Graphs: Beyond Bandits
- Thompson Sampling for Learning Parameterized Markov Decision Processes
- MCMC Learning
- Partitioning Well-Clustered Graphs: Spectral Clustering Works!
- Second-order Quantile Methods for Experts and Combinatorial Games
- Bandit Convex Optimization: sqrt{T} Regret in One Dimension
- A Chaining Algorithm for Online Nonparametric Regression
- Exp-Concavity of Proper Composite Losses
- Vector-Valued Property Elicitation
- Achieving All with No Parameters: Adaptive NormalHedge
- Efficient Representations for Lifelong Learning and Autoencoding
- Cortical Learning via Prediction
- Adaptive recovery of signals by convex optimization
- First-order regret bounds for combinatorial semi-bandits
- Lower and Upper Bounds on the Generalization of Stochastic Exponentially Concave Optimization
- Learning the dependence structure of rare events: a non-asymptotic study
- Sequential Information Maximization: When is Greedy Near-optimal?
- Escaping From Saddle Points — Online Stochastic Gradient for Tensor Decomposition
- Algorithms for Lipschitz Learning on Graphs
- Learning Overcomplete Latent Variable Models through Tensor Methods
- Norm-Based Capacity Control in Neural Networks
- On Learning Distributions from their Samples
- Label optimal regret bounds for online local learning
- Minimax Fixed-Design Linear Regression
- Generalized Mixability via Entropic Duality
- Truthful Linear Regression
- Simple, Efficient, and Neural Algorithms for Sparse Coding
- On-Line Learning Algorithms for Path Experts with Non-Additive Losses
- Regularized Linear Regression: A Precise Analysis of the Estimation Error
- Correlation Clustering with Noisy Partial Information
- Max vs Min: Tensor Decomposition and ICA with nearly Linear Sample Complexity
- On Consistent Surrogate Risk Minimization and Property Elicitation
- Escaping the Local Minima via Simulated Annealing: Optimization of Approximately Convex Functions
- Minimax rates for memory-bounded sparse linear regression
- Convex Risk Minimization and Conditional Probability Estimation
- Learning with Square Loss: Localization through Offset Rademacher Complexity
- Faster Algorithms for Testing under Conditional Sampling
- Beyond Hartigan Consistency: Merge Distortion Metric for Hierarchical Clustering
- $S^2$: An Efficient Graph Based Active Learning Algorithm with Application to Nonparametric Classification
- Efficient Learning of Linear Separators under Bounded Noise
- Optimum Statistical Estimation with Strategic Data Sources
- Tensor principal component analysis
- Hierarchical label queries with data-dependent partitions
- Hierarchies of Relaxations for Online Prediction Problems with Evolving Constraints
- Optimally Combining Classifiers Using Unlabeled Data
July 3-6, 2015