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Poster
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Thu 10:45
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Using Statistics to Automate Stochastic Optimization
Hunter Lang · Lin Xiao · Pengchuan Zhang
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Poster
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Thu 17:00
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Distributionally Robust Optimization and Generalization in Kernel Methods
Matt Staib · Stefanie Jegelka
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Poster
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Wed 17:00
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A Unifying Framework for Spectrum-Preserving Graph Sparsification and Coarsening
Gecia Bravo-Hermsdorff · Lee Gunderson
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Poster
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Wed 17:00
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Diffusion Improves Graph Learning
Johannes Gasteiger · Stefan Weißenberger · Stephan Günnemann
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Poster
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Wed 17:00
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Catastrophic Forgetting Meets Negative Transfer: Batch Spectral Shrinkage for Safe Transfer Learning
Xinyang Chen · Sinan Wang · Bo Fu · Mingsheng Long · Jianmin Wang
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Poster
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Tue 17:30
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Stochastic Proximal Langevin Algorithm: Potential Splitting and Nonasymptotic Rates
Adil Salim · Dmitry Kovalev · Peter Richtarik
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Poster
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Wed 17:00
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Rapid Convergence of the Unadjusted Langevin Algorithm: Isoperimetry Suffices
Santosh Vempala · Andre Wibisono
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Poster
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Tue 17:30
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Optimal Statistical Rates for Decentralised Non-Parametric Regression with Linear Speed-Up
Dominic Richards · Patrick Rebeschini
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Poster
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Tue 10:45
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Quantum Entropy Scoring for Fast Robust Mean Estimation and Improved Outlier Detection
Yihe Dong · Samuel Hopkins · Jerry Li
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Poster
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Tue 17:30
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Necessary and Sufficient Geometries for Gradient Methods
Daniel Levy · John Duchi
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Poster
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Tue 10:45
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A Family of Robust Stochastic Operators for Reinforcement Learning
Yingdong Lu · Mark Squillante · Chai Wah Wu
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Poster
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Thu 17:00
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Graph Structured Prediction Energy Networks
Colin Graber · Alex Schwing
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