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Poster
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Tue 9:00
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Outlier-Robust Sparse Estimation via Non-Convex Optimization
Yu Cheng · Ilias Diakonikolas · Rong Ge · Shivam Gupta · Daniel Kane · Mahdi Soltanolkotabi
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Poster
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Tue 9:00
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Stability and Generalization for Markov Chain Stochastic Gradient Methods
Puyu Wang · Yunwen Lei · Yiming Ying · Ding-Xuan Zhou
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Poster
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Thu 9:00
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Statistically Meaningful Approximation: a Case Study on Approximating Turing Machines with Transformers
Colin Wei · Yining Chen · Tengyu Ma
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Poster
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Wed 14:00
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On the generalization of learning algorithms that do not converge
Nisha Chandramoorthy · Andreas Loukas · Khashayar Gatmiry · Stefanie Jegelka
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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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Distributed Learning of Finite Gaussian Mixtures
Qiong Zhang · Jiahua Chen
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Poster
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Thu 14:00
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Statistical Learning and Inverse Problems: A Stochastic Gradient Approach
Yuri Fonseca · Yuri Saporito
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Poster
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Thu 14:00
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Effects of Data Geometry in Early Deep Learning
Saket Tiwari · George Konidaris
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Poster
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Tue 9:00
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Learning (Very) Simple Generative Models Is Hard
Sitan Chen · Jerry Li · Yuanzhi Li
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Poster
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Tue 14:00
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Privacy Induces Robustness: Information-Computation Gaps and Sparse Mean Estimation
Kristian Georgiev · Samuel Hopkins
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Poster
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Tue 9:00
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Continual learning: a feature extraction formalization, an efficient algorithm, and fundamental obstructions
Binghui Peng · Andrej Risteski
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Poster
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Thu 9:00
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An Analytical Theory of Curriculum Learning in Teacher-Student Networks
Luca Saglietti · Stefano Mannelli · Andrew Saxe
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