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Poster
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Tue 14:00
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EF-BV: A Unified Theory of Error Feedback and Variance Reduction Mechanisms for Biased and Unbiased Compression in Distributed Optimization
Laurent Condat · Kai Yi · Peter Richtarik
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Poster
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Tue 14:00
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Trajectory of Mini-Batch Momentum: Batch Size Saturation and Convergence in High Dimensions
Kiwon Lee · Andrew Cheng · Elliot Paquette · Courtney Paquette
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Workshop
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Fri 9:45
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Alternating minimization for generalized rank one matrix sensing: Sharp predictions from a random initialization
Kabir Chandrasekher
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Poster
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On the role of overparameterization in off-policy Temporal Difference learning with linear function approximation
Valentin Thomas
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Workshop
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Alternating minimization for generalized rank one matrix sensing: Sharp predictions from a random initialization
MENGQI LOU · Kabir Chandrasekher · Ashwin Pananjady
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Poster
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Thu 9:00
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High-dimensional Asymptotics of Feature Learning: How One Gradient Step Improves the Representation
Jimmy Ba · Murat Erdogdu · Taiji Suzuki · Zhichao Wang · Denny Wu · Greg Yang
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Workshop
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TTERGM: Social Theory-Driven network simula
Yifan Huang · Clayton Barham · Eric Page · Pamela K Douglas
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Poster
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"Lossless" Compression of Deep Neural Networks: A High-dimensional Neural Tangent Kernel Approach
lingyu gu · Yongqi Du · yuan zhang · Di Xie · Shiliang Pu · Robert Qiu · Zhenyu Liao
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Poster
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Tue 9:00
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Near-Optimal Randomized Exploration for Tabular Markov Decision Processes
Zhihan Xiong · Ruoqi Shen · Qiwen Cui · Maryam Fazel · Simon Du
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Poster
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Tue 14:00
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Re-Analyze Gauss: Bounds for Private Matrix Approximation via Dyson Brownian Motion
Oren Mangoubi · Nisheeth Vishnoi
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Poster
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Wed 14:00
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Split-kl and PAC-Bayes-split-kl Inequalities for Ternary Random Variables
Yi-Shan Wu · Yevgeny Seldin
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Poster
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Wed 14:00
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On the Convergence Theory for Hessian-Free Bilevel Algorithms
Daouda Sow · Kaiyi Ji · Yingbin Liang
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