PROGRAM
Days: Thursday, June 23rd Friday, June 24th Saturday, June 25th Sunday, June 26th
Thursday, June 23rd
View this program: with abstractssession overviewtalk overview
14:00-15:10 Session 3: Multi-armed Bandits I
Chair:
14:00 | Simple Bayesian Algorithms for Best Arm Identification ( abstract ) |
14:20 | Optimal Best Arm Identification with Fixed Confidence ( abstract ) |
14:40 | Tight (Lower) Bounds for the Fixed Budget Best Arm Identification Bandit Problem ( abstract ) |
14:50 | Best-of-K Bandits ( abstract ) |
15:00 | Pure Exploration of Multi-armed Bandit Under Matroid Constraints ( abstract ) |
15:10-15:30Coffee Break
15:30-16:20 Session 4: Multi-armed Bandits II
Chair:
15:30 | Maximin Action Identification: A New Bandit Framework for Games ( abstract ) |
15:40 | Instance-dependent Regret Bounds for Dueling Bandits ( abstract ) |
15:50 | An efficient algorithm for contextual bandits with knapsacks, and an extension to concave objectives ( abstract ) |
16:00 | Regret Analysis of the Finite-Horizon Gittins Index Strategy for Multi-Armed Bandits ( abstract ) |
16:10 | An algorithm with nearly optimal pseudo-regret \\for both stochastic and adversarial bandits ( abstract ) |
16:20-16:40Break
16:40-18:00 Session 5: Stochastic Block Models
Chair:
16:40 | Information-theoretic thresholds for community detection in sparse networks ( abstract ) |
17:00 | On the low-rank approach for semidefinite programs arising in synchronization and community detection ( abstract ) |
17:20 | Density Evolution in the Degree-correlated Stochastic Block Model ( abstract ) |
17:30 | Learning Communities in the Presence of Errors ( abstract ) |
17:40 | Semidefinite Programs for Exact Recovery of a Hidden Community ( abstract ) |
17:50 | Spectral thresholds in the bipartite stochastic block model ( abstract ) |
Friday, June 24th
View this program: with abstractssession overviewtalk overview
09:00-09:50 Session 7: Deep Networks
Chair:
09:00 | The Power of Depth for Feedforward Neural Networks ( abstract ) |
09:10 | Benefits of depth in neural networks ( abstract ) |
09:20 | On the Expressive Power of Deep Learning: A Tensor Analysis ( abstract ) |
09:30 | Cortical Computation via Iterative Constructions ( abstract ) |
09:40 | A Guide to Learning Arithmetic Circuits ( abstract ) |
09:50-10:10Coffee Break
10:10-11:10 Session 8: Tensor Methods/Programming Hierarchies
Chair:
10:10 | How to calculate partition functions using convex programming hierarchies: provable bounds for variational methods ( abstract ) |
10:30 | Noisy Tensor Completion via the Sum-of-Squares Hierarchy ( abstract ) |
10:50 | Basis Learning as an Algorithmic Primitive ( abstract ) |
11:10-11:30Break
12:30-14:30Lunch Break
14:30-15:50 Session 10: Bandits and Reinforcement Learning
Chair:
14:30 | Multi-scale exploration of convex functions and bandit convex optimization (Best Paper Award) ( abstract ) |
14:50 | Delay and Cooperation in Nonstochastic Bandits ( abstract ) |
15:10 | Policy Error Bounds for Model-Based Reinforcement Learning with Factored Linear Models ( abstract ) |
15:30 | Reinforcement Learning of POMDPs using Spectral Methods ( abstract ) |
15:50-16:20Coffee Break
16:20-17:30 Session 11: PAC Learning
Chair:
16:20 | Optimal Learning via the Fourier Transform for Sums of Independent Integer Random Variables ( abstract ) |
16:40 | Properly Learning Poisson Binomial Distributions in Almost Polynomial Time ( abstract ) |
16:50 | Learning and Testing Junta Distributions ( abstract ) |
17:00 | Efficient algorithms for learning and 1-bit compressed sensing under asymmetric noise ( abstract ) |
17:10 | Complexity theoretic limitations on learning DNF's ( abstract ) |
17:20 | Sign rank versus VC dimension ( abstract ) |
17:30-17:50Break
Saturday, June 25th
View this program: with abstractssession overviewtalk overview
09:00-10:00 Session 13: Online Learning I
Chair:
09:00 | Provably manipulation-resistant reputation systems (Best Student Paper Award) ( abstract ) |
09:20 | On the capacity of information processing systems ( abstract ) |
09:40 | Online learning in repeated auctions ( abstract ) |
10:00-10:20Coffee Break
10:20-11:10 Session 14: Statistical inference
Chair:
10:20 | Monte Carlo Markov Chain Algorithms for Sampling Strongly Rayleigh distributions and Determinantal Point Processes ( abstract ) |
10:30 | When Can We Rank Well from Comparisons of $O(n\log n)$ Non-Actively Chosen Pairs? ( abstract ) |
10:40 | Asymptotic behavior of $\ell_q$-based Laplacian regularization in semi-supervised learning ( abstract ) |
10:50 | Optimal rates for total variation denoising ( abstract ) |
11:00 | Aggregation of supports along the Lasso path ( abstract ) |
11:10-11:30Break
12:30-14:30Lunch Break
14:30-15:40 Session 16: Supervised learning
Chair:
14:30 | Adaptive Learning with Robust Generalization Guarantees ( abstract ) |
14:40 | Interactive Algorithms: from Pool to Stream ( abstract ) |
14:50 | The Extended Littlestone’s Dimension for Learning with Mistakes and Abstentions ( abstract ) |
15:00 | Learning Combinatorial Functions from Pairwise Comparisons ( abstract ) |
15:10 | Learning Simple Auctions ( abstract ) |
15:20 | Preference-based Teaching ( abstract ) |
15:40-16:10Coffee Break
16:10-17:20 Session 17: Optimization
Chair:
16:10 | Dropping Convexity for Faster Semi-definite Optimization ( abstract ) |
16:30 | Efficient approaches for escaping higher order saddle points in non-convex optimization ( abstract ) |
16:40 | Gradient Descent only Converges to Minimizers ( abstract ) |
16:50 | First-order Methods for Geodesically Convex Optimization ( abstract ) |
17:00 | A Light Touch for Heavily Constrained SGD ( abstract ) |
17:10 | Highly-Smooth Zero-th Order Online Optimization ( abstract ) |
17:20-17:40Break
Sunday, June 26th
View this program: with abstractssession overviewtalk overview
09:00-10:00 Session 19: Online Learning II
Chair:
09:00 | Online Sparse Linear Regression ( abstract ) |
09:20 | Online Learning with Low Rank Experts ( abstract ) |
09:30 | Online Isotonic Regression ( abstract ) |
09:40 | Online Learning and Blackwell Approachability in Quitting Games ( abstract ) |
09:50 | Time Series Prediction and Online Learning ( abstract ) |
10:00-10:20Coffee Break
10:20-11:10 Session 20: Learning with additional constraints/PCA
Chair:
10:20 | Memory, Communication, and Statistical Queries ( abstract ) |
10:40 | Matching Matrix Bernstein with Little Memory: Near-Optimal Finite Sample Guarantees for Oja's Algorithm ( abstract ) |
10:50 | An Improved Gap-Dependency Analysis of the Noisy Power Method ( abstract ) |
11:00 | On the Approximability of Sparse PCA ( abstract ) |
11:10-11:30Break
12:30-14:30Lunch Break