PROGRAM
Days: Thursday, July 2nd Friday, July 3rd Saturday, July 4th Sunday, July 5th Monday, July 6th
Thursday, July 2nd
View this program: with abstractssession overviewtalk overview
Friday, July 3rd
View this program: with abstractssession overviewtalk overview
09:00-09:40 Session 3: Computational Learning
Chair:
| 09:00 | An Almost Optimal PAC Algorithm (Best Paper Award!) (abstract) |
| 09:20 | Cortical Learning via Prediction (abstract) |
09:40-10:40 Session 4: Optimization I
Chair:
| 09:40 | On the Complexity of Learning with Kernels (abstract) |
| 10:00 | Escaping From Saddle Points --- Online Stochastic Gradient for Tensor Decomposition (abstract) |
| 10:20 | Max vs Min: Tensor Decomposition and ICA with nearly Linear Sample Complexity (abstract) |
| 10:25 | Adaptive recovery of signals by convex optimization (abstract) |
| 10:30 | Competing with the Empirical Risk Minimizer in a Single Pass (abstract) |
| 10:35 | Lower and Upper Bounds on the Generalization of Stochastic Exponentially Concave Optimization (abstract) |
10:40-11:10Coffee Break
11:10-12:30 Session 5: On-Line Learning & Bandits I
Chair:
| 11:10 | From Averaging to Acceleration, There is Only a Step-size (abstract) |
| 11:30 | On-Line Learning Algorithms for Path Experts with Non-Additive Losses (abstract) |
| 11:50 | Achieving All with No Parameters: Adaptive NormalHedge (abstract) |
| 11:55 | Second-order Quantile Methods for Experts and Combinatorial Games (abstract) |
| 12:00 | Online Density Estimation of Bradley-Terry Models (abstract) |
| 12:05 | Hierarchies of Relaxations for Online Prediction Problems with Evolving Constraints (abstract) |
| 12:10 | On the Complexity of Bandit Linear Optimization (abstract) |
| 12:15 | Bandit Convex Optimization: sqrt{T} Regret in One Dimension (abstract) |
| 12:20 | Batched Bandit Problems (abstract) |
| 12:25 | Learnability of Solutions to Conjunctive Queries: The Full Dichotomy (abstract) |
12:30-14:30Lunch Break
14:30-15:40 Session 6: Classification
Chair:
| 14:30 | MCMC Learning (abstract) |
| 14:50 | Learning and inference in the presence of corrupted inputs (abstract) |
| 15:10 | A PTAS for Agnostically Learning Halfspaces (abstract) |
| 15:15 | Convex Risk Minimization and Conditional Probability Estimation (abstract) |
| 15:20 | Efficient Learning of Linear Separators under Bounded Noise (abstract) |
| 15:25 | Optimally Combining Classifiers Using Unlabeled Data (abstract) |
| 15:30 | $S^2$: An Efficient Graph Based Active Learning Algorithm with Application to Nonparametric Classification (abstract) |
| 15:35 | Hierarchical label queries with data-dependent partitions (abstract) |
15:40-16:00Coffee Break
18:45-23:00 Session : Cocktail on top of Zamansky Tower
Cocktails and Light Fare will be served on the top floor of a high university building. We booked the room until 11 PM so that there is no rush, but note that this is not really a dinner.
Saturday, July 4th
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09:00-10:45 Session 9: Unsupervised Learning
Chair:
| 09:00 | Beyond Hartigan Consistency: Merge Distortion Metric for Hierarchical Clustering (abstract) |
| 09:20 | Simple, Efficient, and Neural Algorithms for Sparse Coding (abstract) |
| 09:40 | Efficient Representations for Lifelong Learning and Autoencoding (abstract) |
| 10:00 | Tensor principal component analysis (abstract) |
| 10:20 | Partitioning Well-Clustered Graphs: Spectral Clustering Works! (abstract) |
| 10:25 | Online PCA with Spectral Bounds (abstract) |
| 10:30 | Correlation Clustering with Noisy Partial Information (abstract) |
| 10:35 | Norm-Based Capacity Control in Neural Networks (abstract) |
| 10:40 | Stochastic Block Model and Community Detection in the Sparse Graphs: A spectral algorithm with optimal rate of recovery (abstract) |
10:45-11:15Coffee Break
11:15-12:15 Session 10: Invited Talk
Chair:
| 11:15 | Laplacian Matrices of Graphs: Algorithms and Applications (abstract) |
Sunday, July 5th
View this program: with abstractssession overviewtalk overview
09:00-10:40 Session 11: Optimization, Online Learning, Loss Functions
Chair:
| 09:00 | The entropic barrier: a simple and optimal universal self-concordant barrier (abstract) |
| 09:20 | Escaping the Local Minima via Simulated Annealing: Optimization of Approximately Convex Functions (abstract) |
| 09:40 | Improved Sum-of-Squares Lower Bounds for Hidden Clique and Hidden Submatrix Problems (abstract) |
| 10:00 | Sequential Information Maximization: When is Greedy Near-optimal? (abstract) |
| 10:05 | Low Rank Matrix Completion with Exponential Family Noise (abstract) |
| 10:10 | Fast Exact Matrix Completion with Finite Samples (abstract) |
| 10:15 | Exp-Concavity of Proper Composite Losses (abstract) |
| 10:20 | Vector-Valued Property Elicitation (abstract) |
| 10:25 | Generalized Mixability via Entropic Duality (abstract) |
| 10:30 | On Consistent Surrogate Risk Minimization and Property Elicitation (abstract) |
| 10:35 | Label optimal regret bounds for online local learning (abstract) |
10:40-11:10Coffee Break
11:10-12:10 Session 12: Invited Talk
Chair:
| 11:10 | Applications of Learning Theory in Algorithmic Game Theory (abstract) |
12:40-14:30Lunch Break
14:30-15:10 Session 14: Estimation, Generative Models
Chair:
| 14:30 | Learning the dependence structure of rare events: a non-asymptotic study (abstract) |
| 14:50 | On Learning Distributions from their Samples (abstract) |
| 14:55 | Optimum Statistical Estimation with Strategic Data Sources (abstract) |
| 15:00 | Learning Overcomplete Latent Variable Models through Tensor Methods (abstract) |
| 15:05 | Efficient Sampling for Gaussian Graphical Models via Spectral Sparsification (abstract) |
15:10-15:40 Session 15: On-Line Learning & Bandits II
Chair:
| 15:10 | Minimax Fixed-Design Linear Regression (abstract) |
| 15:15 | A Chaining Algorithm for Online Nonparametric Regression (abstract) |
| 15:20 | First-order regret bounds for combinatorial semi-bandits (abstract) |
| 15:25 | Online Learning with Feedback Graphs: Beyond Bandits (abstract) |
| 15:30 | Regret Lower Bound and Optimal Algorithm in Dueling Bandit Problem (abstract) |
| 15:35 | Contextual Dueling Bandits (abstract) |
15:40-16:00Coffee Break
Monday, July 6th
View this program: with abstractssession overviewtalk overview
09:00-10:00 Session 18: Invited Talk
Chair:
| 09:00 | Synthetic theory of Ricci curvature - when information theory, optimization, geometry and gradient flows meet (abstract) |
10:00-10:40 Session 19: Probabilistic Models and Reinforcement Learning
Chair:
| 10:00 | Computational Lower Bounds for Community Detection on Random Graphs (abstract) |
| 10:05 | Bad Universal Priors and Notions of Optimality (abstract) |
| 10:10 | Thompson Sampling for Learning Parameterized Markov Decision Processes (abstract) |
| 10:15 | Fast Mixing for Discrete Point Processes (abstract) |
| 10:20 | On Convergence of Emphatic Temporal-Difference Learning (abstract) |
| 10:25 | Faster Algorithms for Testing under Conditional Sampling (abstract) |
| 10:30 | Interactive Fingerprinting Codes and the Hardness of Preventing False Discovery (abstract) |
10:40-11:10Coffee Break
11:10-12:25 Session 20: Regression
Chair:
| 11:10 | Learning with Square Loss: Localization through Offset Rademacher Complexity (abstract) |
| 11:30 | Minimax rates for memory-bounded sparse linear regression (abstract) |
| 11:50 | Algorithms for Lipschitz Learning on Graphs (abstract) |
| 12:10 | Variable Selection is Hard (abstract) |
| 12:15 | Regularized Linear Regression: A Precise Analysis of the Estimation Error (abstract) |
| 12:20 | Truthful Linear Regression (abstract) |