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Poster
Tue 7:00 Data Amplification: Instance-Optimal Property Estimation
Yi Hao · Alon Orlitsky
Poster
Tue 7:00 Customizing ML Predictions for Online Algorithms
Keerti Anand · Rong Ge · Debmalya Panigrahi
Poster
Tue 9:00 Provable guarantees for decision tree induction: the agnostic setting
Guy Blanc · Jane Lange · Li-Yang Tan
Poster
Tue 9:00 Learning and Sampling of Atomic Interventions from Observations
Arnab Bhattacharyya · Sutanu Gayen · Saravanan Kandasamy · Ashwin Maran · Vinodchandran N. Variyam
Poster
Tue 11:00 Consistent Structured Prediction with Max-Min Margin Markov Networks
Alex Nowak · Francis Bach · Alessandro Rudi
Poster
Tue 14:00 Decentralised Learning with Random Features and Distributed Gradient Descent
Dominic Richards · Patrick Rebeschini · Lorenzo Rosasco
Poster
Tue 18:00 Efficiently Learning Adversarially Robust Halfspaces with Noise
Omar Montasser · Surbhi Goel · Ilias Diakonikolas · Nati Srebro
Poster
Wed 5:00 Improved Communication Cost in Distributed PageRank Computation – A Theoretical Study
Siqiang Luo
Poster
Wed 5:00 On Unbalanced Optimal Transport: An Analysis of Sinkhorn Algorithm
Khiem Pham · Khang Le · Nhat Ho · Tung Pham · Hung Bui
Poster
Wed 8:00 Approximation Capabilities of Neural ODEs and Invertible Residual Networks
Han Zhang · Xi Gao · Jacob Unterman · Tomasz Arodz
Poster
Wed 8:00 Sample Amplification: Increasing Dataset Size even when Learning is Impossible
Brian Axelrod · Shivam Garg · Vatsal Sharan · Gregory Valiant
Poster
Wed 8:00 Superpolynomial Lower Bounds for Learning One-Layer Neural Networks using Gradient Descent
Surbhi Goel · Aravind Gollakota · Zhihan Jin · Sushrut Karmalkar · Adam Klivans