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Workshop
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Amortized Decision-Aware Bayesian Experimental Design
Daolang Huang · Yujia Guo · Luigi Acerbi · Samuel Kaski
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Workshop
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Sat 12:00
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Data-Adaptive Tradeoffs among Multiple Risks in Distribution-Free Prediction
Drew Nguyen · Reese Pathak · Anastasios Angelopoulos · Stephen Bates · Michael Jordan
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Poster
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Thu 16:30
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A Unifying Post-Processing Framework for Multi-Objective Learn-to-Defer Problems
Mohammad-Amin Charusaie · Samira Samadi
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Poster
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Fri 16:30
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Achieving ~O(1/ϵ) Sample Complexity for Constrained Markov Decision Process
Jiashuo Jiang · Yinyu Ye
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Poster
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Fri 16:30
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Learning the Infinitesimal Generator of Stochastic Diffusion Processes
Vladimir Kostic · Hélène Halconruy · Timothée Devergne · Karim Lounici · Massimiliano Pontil
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Workshop
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Adversarial Training Can Provably Improve Robustness: Theoretical Analysis of Feature Learning Process Under Structured Data
Binghui Li · Yuanzhi Li
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Poster
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Thu 11:00
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Warm-up Free Policy Optimization: Improved Regret in Linear Markov Decision Processes
Asaf Cassel · Aviv Rosenberg
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Poster
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Wed 16:30
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Safe Time-Varying Optimization based on Gaussian Processes with Spatio-Temporal Kernel
Jialin Li · Marta Zagorowska · Giulia De Pasquale · Alisa Rupenyan · John Lygeros
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Poster
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Fri 16:30
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Universal Rates of Empirical Risk Minimization
Steve Hanneke · Mingyue Xu
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Workshop
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Adversarial Attacks as Near-Zero Eigenvalues in the Empirical Kernel of Neural Networks
Ouns El Harzli · Bernardo Grau
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Workshop
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Sat 15:45
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LLMs for Causal Inference
Jonathan Choi
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Poster
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Wed 16:30
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Deep Learning Through A Telescoping Lens: A Simple Model Provides Empirical Insights On Grokking, Gradient Boosting & Beyond
Alan Jeffares · Alicia Curth · Mihaela van der Schaar
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