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Workshop
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Fri 9:30
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Let’s Give Domain Experts a Choice by Creating Many Approximately-Optimal Machine Learning Models
Cynthia Rudin
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Panel
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Tue 9:30
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Panel 1C-3: Towards Understanding Grokking:… & Approximation with CNNs…
Ziming Liu · GUOHAO SHEN
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Poster
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Tue 9:00
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AgraSSt: Approximate Graph Stein Statistics for Interpretable Assessment of Implicit Graph Generators
Wenkai Xu · Gesine D Reinert
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Poster
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Wed 9:00
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Beyond IID: data-driven decision-making in heterogeneous environments
Omar Besbes · Will Ma · Omar Mouchtaki
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Poster
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Tue 14:00
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Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation
Arnaud Delaunoy · Joeri Hermans · François Rozet · Antoine Wehenkel · Gilles Louppe
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Poster
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Wed 14:00
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Learning Two-Player Markov Games: Neural Function Approximation and Correlated Equilibrium
Chris Junchi Li · Dongruo Zhou · Quanquan Gu · Michael Jordan
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Poster
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Wed 14:00
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A Single-timescale Analysis for Stochastic Approximation with Multiple Coupled Sequences
Han Shen · Tianyi Chen
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Poster
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Tue 9:00
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Error Analysis of Tensor-Train Cross Approximation
Zhen Qin · Alexander Lidiak · Zhexuan Gong · Gongguo Tang · Michael B Wakin · Zhihui Zhu
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Poster
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Wed 9:00
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On Deep Generative Models for Approximation and Estimation of Distributions on Manifolds
Biraj Dahal · Alexander Havrilla · Minshuo Chen · Tuo Zhao · Wenjing Liao
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Poster
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Tue 9:00
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Learning single-index models with shallow neural networks
Alberto Bietti · Joan Bruna · Clayton Sanford · Min Jae Song
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Poster
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Wed 9:00
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Bounding and Approximating Intersectional Fairness through Marginal Fairness
Mathieu Molina · Patrick Loiseau
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Poster
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Thu 9:00
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On the Approximation of Cooperative Heterogeneous Multi-Agent Reinforcement Learning (MARL) using Mean Field Control (MFC)
Washim Mondal · Mridul Agarwal · Vaneet Aggarwal · Satish Ukkusuri
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