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