Conference Schedule


Saturday, February 8, 2020: ITALT workshop day

08:00 – 08:30 Light breakfast
08:30 – 10:20 Tutorial: Deep Learning Essentials
Ruslan Salakhutdinov
10:20 – 10:40 Break
10:40 – 11:30 Invited talk: In Defense of Uniform Convergence: Generalization via derandomization with an application to interpolating predictors
Daniel M. Roy
11:30 – 11:50 Break
11:50 – 12:40 Invited talk: Margins, perceptrons, and deep networks
Matus Telgarsky
12:40 – 14:00 Lunch (on your own)
14:00 – 15:50 Tutorial: Incentivizing and Coordinating Exploration
Alex Slivkins and Bobby Kleinberg
15:50 – 16:10 Break
16:10 – 17:00 Invited talk: PAC-Bayes, Rademacher and Descriptional Complexities: Three Sides of the Same Coin
Peter Grünwald

Sunday, February 9, 2020

08:15 – 08:45 Light breakfast
08:45 – 09:00 Opening remarks
09:00 – 11:00 Tutorial: A survey on random projections
Jelani Nelson
11:00 – 11:20 Break
11:20 – 12:20 Bandits I
11:20 Top-k Combinatorial Bandits with Full-Bandit Feedback
Idan Rejwan and Yishay Mansour
11:40 Thompson Sampling for Adversarial Bit Prediction
Yuval Lewi, Haim Kaplan and Yishay Mansour
12:00 Bandit Algorithms Based on Thompson Sampling for Bounded Reward Distributions
Charles Riou and Junya Honda
12:20 – 14:00 Lunch (on your own)
14:00 – 15:00 Plenary talk: The Unreasonable Effectiveness of Gradient Descent
John Lafferty
15:00 – 15:20 Break
15:20 – 16:40 Unsupervised and interactive learning
15:20 Toward Universal Testing of Dynamic Network Models
Abram Magner and Wojciech Szpankowski
15:40 On the Analysis of EM for truncated mixtures of two Gaussians
Sai Ganesh Nagarajan and Ioannis Panageas
16:00 Algebraic and Analytic Approaches for Parameter Learning in Mixture Models
Akshay Krishnamurthy, Arya Mazumdar, Andrew McGregor and Soumyabrata Pal
16:20 Interactive Learning of a Dynamic Structure
Ehsan Emamjomeh-Zadeh, David Kempe, Mohammad Mahdian and Robert Schapire
16:40 – 17:00 Break
17:00 – 18:20 Dynamical systems, RL, control
17:00 Planning in Hierarchical Reinforcement Learning: Guarantees for Using Local Policies
Tom Zahavy, Avinatan Hassidim, Haim Kaplan and Yishay Mansour
17:20 Robust guarantees for learning an autoregressive filter
Holden Lee and Cyril Zhang
17:40 Mixing Time Estimation in Ergodic Markov Chains from a Single Trajectory with Contraction Methods
Geoffrey Wolfer
18:00 The Nonstochastic Control Problem
Elad Hazan, Sham Kakade and Karan Singh
18:30 – 20:30 Poster session and reception
20:30 – 21:00 Business meeting

Monday, February 10, 2020

08:30 – 09:00 Light breakfast
09:00 – 11:00 Tutorial: Stochastic Calculus in Machine Learning: Optimization, Sampling, Simulation
Maxim Raginsky
11:00 – 11:20 Break
11:20 – 12:20 Statistical learning theory I
11:20 On the Complexity of Proper Distribution-Free Learning of Linear Classifiers
Philip Long and Raphael Long
11:40 Distribution Free Learning with Local Queries
Galit Bary Weisberg, Amit Daniely and Shai Shalev-Shwartz
12:00 On the Expressive Power of Kernel Methods and the Efficiency of Kernel Learning by Association Schemes
Roi Livni and Pravesh K Kothari
12:20 – 14:00 Lunch (on your own)
13:20 – 13:50 AALT meeting
14:00 – 15:00 Plenary talk: A Hard Look at Soft Concepts
Dafna Shahaf
15:00 – 15:20 Break
15:20 – 16:40 Optimization
15:20 Don’t Jump Through Hoops and Remove Those Loops: SVRG and Katyusha are Better Without the Outer Loop
Dmitry Kovalev, Samuel Horvath and Peter Richtárik
15:40 Leverage Score Sampling for Faster Accelerated Regression and ERM
Naman Agarwal, Sham Kakade, Rahul Kidambi, Yin Tat Lee, Praneeth Netrapalli and Aaron Sidford
16:00 A Tight Convergence Analysis for Stochastic Gradient Descent with Delayed Updates
Yossi Arjevani, Ohad Shamir and Nathan Srebro
16:20 Finding Robust Nash equilibria
Vianney Perchet
16:40 – 17:00 Break
17:00 – 18:40 Nonstandard models
17:00 A Non-Trivial Algorithm Enumerating Relevant Features over Finite Fields
Mikito Nanashima
17:20 Approximate Representer Theorems in Non-reflexive Banach Spaces
Kevin Schlegel
17:40 Cautious Limit Learning
Vanja Doskoc and Timo Kötzing
18:00 What relations are reliably embeddable in Euclidean space?
Robi Bhattacharjee and Sanjoy Dasgupta
18:20 On Learning Causal Structures from Non-Experimental Data without Any Faithfulness Assumption
Hanti Lin and Jiji Zhang
18:40 – 19:30 Break
19:00 Banquet

Tuesday, February 11, 2020

08:30 – 09:00 Light breakfast
09:00 – 10:40 Online learning and optimization
09:00 Cooperative Online Learning: Keeping your Neighbors Updated
Nicolò Cesa-Bianchi, Tommaso Cesari and Claire Monteleoni
09:20 Online Non-Convex Learning: Following the Perturbed Leader is Optimal
Arun Suggala and Praneeth Netrapalli
09:40 An adaptive stochastic optimization algorithm for resource allocation
Xavier Fontaine, Shie Mannor and Vianney Perchet
10:00 Exponentiated Gradient Meets Gradient Descent
Udaya Ghai, Elad Hazan and Yoram Singer
10:20 Robust Algorithms for Online k-means Clustering
Aditya Bhaskara and Aravinda Kanchana Ruwanpathirana
10:40 – 11:00 Break
11:00 – 12:20 Bandits II
11:00 First-Order Bayesian Regret Analysis of Thompson Sampling
Mark Sellke and Sébastien Bubeck
11:20 Feedback graph regret bounds for Thompson Sampling and UCB
Thodoris Lykouris, Eva Tardos and Drishti Wali
11:40 Optimal delta correct best-arm selection for general distributions
Shubhada Agrawal, Sandeep Juneja and Peter Glynn
12:00 Solving Bernoulli Rank-One Bandits with Unimodal Thompson Sampling
Cindy Trinh, Emilie Kaufmann, Claire Vernade and Richard Combes
12:20 – 14:00 Lunch (on your own)
14:00 – 15:00 Plenary talk: Winnowing with gradient descent
Manfred Warmuth
15:00 – 15:20 Break
15:20 – 16:20 Statistical learning theory II
15:20 Optimal multiclass overfitting by sequence reconstruction from Hamming queries
Jayadev Acharya and Ananda Theertha Suresh
15:40 Adversarially Robust Learning Could Leverage Computational Hardness
Sanjam Garg, Somesh Jha, Saeed Mahloujifar and Mohammad Mahmoody
16:00 On Learnability with Computable Learners
Sushant Agarwal, Nivasini Ananthakrishnan, Shai Ben-David, Tosca Lechner and Ruth Urner
16:20 – 16:40 Break
16:40 – 17:40 Privacy and stability
16:40 Sampling Without Compromising Accuracy in Adaptive Data Analysis
Benjamin Fish, Lev Reyzin and Benjamin Rubinstein
17:00 Privately Answering Classification Queries in the Agnostic PAC Model
Anupama Nandi and Raef Bassily
17:20 Efficient Private Algorithms for Learning Large-Margin Halfspaces
Huy Nguyen, Jonathan Ullman and Lydia Zakynthinou