From the 1 of 111 linked papers with an AI index.
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Cover meets Robbins while Betting on Bounded Data: Regret and Almost Sure Regret
Shubhada Agrawal, Aaditya Ramdas
Consider betting against a sequence of data in , where one is allowed to make any bet that is fair if the data have a conditional mean . Cover's universal por…
Eventually LIL Regret: Almost Sure Regret for a sub-Gaussian Mixture on Unbounded Data
Shubhada Agrawal, Aaditya Ramdas
We prove that a classic sub-Gaussian mixture proposed by Robbins in a stochastic setting actually satisfies a path-wise (deterministic) regret bound. For every path in a natural ``…
Gradient descent for deep equilibrium single-index models
Sanjit Dandapanthula, Aaditya Ramdas
Deep equilibrium models (DEQs) have recently emerged as a powerful paradigm for training infinitely deep weight-tied neural networks that achieve state of the art performance acros…
Optimal Transportation and Alignment Between Gaussian Measures
Sanjit Dandapanthula, Aleksandr Podkopaev, Shiva Prasad Kasiviswanathan +2
Optimal transport (OT) and Gromov-Wasserstein (GW) alignment provide interpretable geometric frameworks for comparing, transforming, and aggregating heterogeneous datasets -- tasks…
Private Evolution Converges
Tomás González, Giulia Fanti, Aaditya Ramdas
Private Evolution (PE) is a promising training-free method for differentially private (DP) synthetic data generation. While it achieves strong performance in some domains (e.g., im…