PROGRAM
Days: Friday, July 6th Saturday, July 7th Sunday, July 8th Monday, July 9th
Friday, July 6th
View this program: with abstractssession overviewtalk overview
08:00-08:55 Session 1: Registration
Registration
09:00-10:00 Session 3
Chair:
| 09:00 | An Optimal Learning Algorithm for Online Unconstrained Submodular Maximization (abstract) |
| 09:10 | Black-Box Reductions for Parameter-free Online Learning in Banach Spaces (abstract) |
| 09:20 | The Many Faces of Exponential Weights in Online Learning (abstract) |
| 09:30 | Smoothed Online Convex Optimization in High Dimensions via Online Balanced Descent (abstract) |
| 09:40 | Online learning over a finite action set with limited switching (abstract) |
| 09:50 | Online Learning: Sufficient Statistics and the Burkholder Method (abstract) |
10:00-10:30Coffee Break
10:30-12:00 Session 4
Chair:
| 10:30 | Learning Single Index Models in Gaussian Space (abstract) |
| 10:40 | L1 Regression using Lewis Weights Preconditioning and Stochastic Gradient Descent (abstract) |
| 10:50 | Optimal Errors and Phase Transitions in High-Dimensional Generalized Linear Models (abstract) |
| 11:00 | Marginal Singularity, and the Benefits of Labels in Covariate-Shift (abstract) |
| 11:10 | Learning Mixtures of Linear Regressions with Nearly Optimal Complexity (abstract) |
| 11:20 | Efficient Algorithms for Outlier-Robust Regression (abstract) |
| 11:30 | Restricted Eigenvalue from Stable Rank with Applications to Sparse Linear Regression (abstract) |
| 11:40 | Logistic Regression: The Importance of Being Improper (abstract) |
| 11:50 | Reducibility and Computational Lower Bounds for Problems with Planted Sparse Structure (abstract) |
12:00-14:00Lunch Break
14:00-15:00 Session 5: Invited talk: Stephane Mallat
| 14:00 | Unsupervised Learning from Max Entropy to Deep Generative Networks (abstract) |
15:20-16:20 Session 6
Chair:
| 15:20 | An explicit analysis of the entropic penalty in linear programming (abstract) |
| 15:30 | Lower Bounds for Higher-Order Convex Optimization (abstract) |
| 15:40 | Efficient Convex Optimization with Membership Oracles (abstract) |
| 15:50 | Faster Rates for Convex-Concave Games (abstract) |
| 16:00 | Smoothed analysis for low-rank solutions to semidefinite programs in quadratic penalty form (abstract) |
| 16:10 | Convex Optimization with Unbounded Nonconvex Oracles Using Simulated Annealing (abstract) |
16:30-17:30 Session 7
Chair:
| 16:30 | Local moment matching: A unified methodology for symmetric functional estimation and distribution estimation under Wasserstein distance (abstract) |
| 16:40 | Fast and Sample Near-Optimal Algorithms for Learning Multidimensional Histograms (abstract) |
| 16:50 | Learning Patterns for Detection with Multiscale Scan Statistics (abstract) |
| 17:00 | Detecting Correlations with Little Memory and Communication (abstract) |
| 17:10 | Actively Avoiding Nonsense in Generative Models (abstract) |
| 17:20 | Near-Optimal Sample Complexity Bounds for Maximum Likelihood Estimation of Multivariate Log-concave Densities (abstract) |
17:30-19:30 Session 8
Poster Session
Saturday, July 7th
View this program: with abstractssession overviewtalk overview
09:00-10:00 Session 9
Chair:
| 09:00 | The Externalities of Exploration and How Data Diversity Helps Exploitation (abstract) |
| 09:10 | Learning Without Mixing: Towards A Sharp Analysis of Linear System Identification (abstract) |
| 09:20 | Testing Symmetric Markov Chains From a Single Trajectory (abstract) |
| 09:30 | Action-Constrained Markov Decision Processes With Kullback-Leibler Cost (abstract) |
| 09:40 | A Finite Time Analysis of Temporal Difference Learning With Linear Function Approximation (abstract) |
| 09:50 | Finite Sample Analysis of Two-Timescale Stochastic Approximation with Applications to Reinforcement Learning (abstract) |
10:00-10:30Coffee Break
10:30-12:00 Session 10
Chair:
| 10:30 | A Faster Approximation Algorithm for the Gibbs Partition Function (abstract) |
| 10:40 | The Mean-Field Approximation: Information Inequalities, Algorithms, and Complexity (abstract) |
| 10:50 | The Vertex Sample Complexity of Free Energy is Polynomial (abstract) |
| 11:00 | Optimal Single Sample Tests for Structured versus Unstructured Network Data (abstract) |
| 11:10 | Fundamental Limits of Weak Recovery with Applications to Phase Retrieval (abstract) |
| 11:20 | Non-Convex Matrix Completion Against a Semi-Random Adversary (abstract) |
| 11:30 | Counting Motifs with Graph Sampling (abstract) |
| 11:40 | Breaking the $1/\sqrt{n}$ Barrier: Faster Rates for Permutation-based Models in Polynomial Time (abstract) |
| 11:50 | Geometric Lower Bounds for Distributed Parameter Estimation under Communication Constraints (abstract) |
12:00-13:40Lunch Break
13:50-14:00 Session 11: Open Problems
Chair:
| 13:50 | The Dependence of Sample Complexity Lower Bounds on Planning Horizon (abstract) |
| 13:55 | Improper Learning of Mixtures of Gaussians (abstract) |
14:00-15:00 Session 12: Invited talk: Susan Murphy
| 14:00 | Mobile Health: Adventures in Sequential Experimentation and Reinforcement Learning! (abstract) |
15:20-16:50 Session 13
Chair:
| 15:20 | An Estimate Sequence for Geodesically Convex Optimization (abstract) |
| 15:30 | Averaged Stochastic Gradient Descent on Riemannian Manifolds (abstract) |
| 15:40 | Accelerating Stochastic Gradient Descent for Least Squares Regression (abstract) |
| 15:50 | Accelerated Gradient Descent Escapes Saddle Points Faster than Gradient Descent (abstract) |
| 16:00 | Exponential convergence of testing error for stochastic gradient methods (abstract) |
| 16:10 | Cutting plane methods can be extended into nonconvex optimization (abstract) |
| 16:20 | Online Variance Reduction for Stochastic Optimization (abstract) |
| 16:30 | Minimax Bounds on Stochastic Batched Convex Optimization (abstract) |
| 16:40 | Iterate Averaging as Regularization for Stochastic Gradient Descent (abstract) |
17:00-18:00 Session 14
Business meeting
18:00-20:00 Session 15
Poster Session
Sunday, July 8th
View this program: with abstractssession overviewtalk overview
09:00-10:00 Session 16
Chair:
| 09:00 | Langevin Monte Carlo and JKO splitting (abstract) |
| 09:10 | Log-concave sampling: Metropolis-Hastings algorithms are fast! (abstract) |
| 09:20 | Sampling as optimization in the space of measures: The Langevin dynamics as a composite optimization problem (abstract) |
| 09:30 | Generalization Bounds of SGLD for Non-convex Learning: Two Theoretical Viewpoints (abstract) |
| 09:40 | Local Optimality and Generalization Guarantees for the Langevin Algorithm via Empirical Metastability (abstract) |
| 09:50 | Underdamped Langevin MCMC: A non-asymptotic analysis (abstract) |
10:00-10:30Coffee Break
10:30-12:00 Session 17
Chair:
| 10:30 | Incentivizing Exploration by Heterogeneous Users (abstract) |
| 10:40 | A General Approach to Multi-Armed Bandits Under Risk Criteria (abstract) |
| 10:50 | Nonstochastic Bandits with Composite Anonymous Feedback (abstract) |
| 11:00 | More Adaptive Algorithms for Adversarial Bandits (abstract) |
| 11:10 | Best of both worlds: Stochastic & adversarial best-arm identification (abstract) |
| 11:20 | Efficient Contextual Bandits in Non-stationary Worlds (abstract) |
| 11:30 | Adaptivity to Smoothness in X-armed bandits (abstract) |
| 11:40 | Small-loss bounds for online learning with partial information (abstract) |
| 11:50 | Information Directed Sampling and Bandits with Heteroscedastic Noise (abstract) |
12:00-14:00Lunch Break
14:00-15:00 Session 18: Invited talk: Johan Hastad
| 14:00 | Some problems that I like connected to small-depth circuits and hardness of approximation (abstract) |
15:20-16:20 Session 19
Chair:
| 15:20 | Algorithmic Regularization in Over-parameterized Matrix Sensing and Neural Networks with Quadratic Activations (abstract) |
| 15:50 | Global Guarantees for Enforcing Deep Generative Priors by Empirical Risk (abstract) |
| 16:00 | Size-Independent Sample Complexity of Neural Networks (abstract) |
| 16:10 | Optimal approximation of continuous functions by very deep ReLU networks (abstract) |
16:30-17:30 Session 20
Chair:
| 16:30 | Subpolynomial trace reconstruction for random strings and arbitrary deletion probability (abstract) |
| 16:40 | Detection limits in the high-dimensional spiked rectangular model (abstract) |
| 16:50 | Hidden Integrality of SDP Relaxations for Sub-Gaussian Mixture Models (abstract) |
| 17:00 | An Analysis of the t-SNE Algorithm for Data Visualization (abstract) |
| 17:10 | Fitting a putative manifold to noisy data (abstract) |
| 17:20 | Polynomial Time and Sample Complexity for Non-Gaussian Component Analysis: Spectral Methods (abstract) |
17:30-19:30 Session 21
Poster Session
19:30-23:00 Banquet
The banquet will be at artipelag: https://artipelag.se/.
It takes about 30 minutes to go there by bus. The busses will be leaving at 7:30 PM at the beginning of Malvinasväg (or equivalently Osquldasväg) on Drottning Kristinas väg: https://goo.gl/maps/NQBXJJj61YM2.
Monday, July 9th
View this program: with abstractssession overviewtalk overview
09:00-10:00 Session 22
Chair:
| 09:00 | Learning from Unreliable Datasets (abstract) |
| 09:10 | Privacy-preserving Prediction (abstract) |
| 09:20 | Calibrating Noise to Variance in Adaptive Data Analysis (abstract) |
| 09:30 | A Data Prism: Semi-verified learning in the small-alpha regime (abstract) |
| 09:40 | Unleashing Linear Optimizers for Group-Fair Learning and Optimization (abstract) |
| 09:50 | Private Sequential Learning (abstract) |
10:00-10:30Coffee Break
10:30-12:00 Session 23
Chair:
| 10:30 | Hardness of Learning Noisy Halfspaces using Polynomial Thresholds (abstract) |
| 10:40 | Active Tolerant Testing (abstract) |
| 10:50 | Time-Space Tradeoffs for Learning Finite Functions from Random Tests, with Applications to Polynomials (abstract) |
| 11:00 | Empirical bounds for functions with weak interactions (abstract) |
| 11:10 | A Direct Sum for Information Learners (abstract) |
| 11:20 | Exact and Robust Conformal Inference Methods for Predictive Machine Learning With Dependent Data (abstract) |
| 11:30 | Approximation beats concentration? An approximation view on inference with smooth radial kernels (abstract) |
| 11:40 | Approximate Nearest Neighbors in Limited Space (abstract) |
| 11:50 | Efficient Active Learning of Sparse Halfspaces (abstract) |
12:00-14:00Lunch Break
12:00-14:00 Session 24
Poster Session