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20062026
most citedOn the Optimization of Deep Networks: Implicit Acceleration by Overparameterization

124 citations · 1.6k across the 112 of their papers we have counts for

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Showing 2024Show all

7 papers · 1 filter

cs.LG2024

Provable Length Generalization in Sequence Prediction via Spectral Filtering

Annie Marsden, Evan Dogariu, Naman Agarwal +3

We consider the problem of length generalization in sequence prediction. We define a new metric of performance in this setting -- the Asymmetric-Regret -- which measures regret aga…

cs.LG2024

FutureFill: Fast Generation from Convolutional Sequence Models

Naman Agarwal, Xinyi Chen, Evan Dogariu +6

We address the challenge of efficient auto-regressive generation in sequence prediction models by introducing FutureFill, a general-purpose fast generation method for any sequence…

cs.LG2024

Flash STU: Fast Spectral Transform Units

Y. Isabel Liu, Windsor Nguyen, Yagiz Devre +3

Recent advances in state-space model architectures have shown great promise for efficient sequence modeling, but challenges remain in balancing computational efficiency with model…

cs.LG2024

Online Control in Population Dynamics

Noah Golowich, Elad Hazan, Zhou Lu +2

The study of population dynamics originated with early sociological works but has since extended into many fields, including biology, epidemiology, evolutionary game theory, and ec…

cs.LG2024

Second Order Methods for Bandit Optimization and Control

Arun Suggala, Y. Jennifer Sun, Praneeth Netrapalli +1

Bandit convex optimization (BCO) is a general framework for online decision making under uncertainty. While tight regret bounds for general convex losses have been established, exi…

cs.LG2024

Adaptive Regret for Bandits Made Possible: Two Queries Suffice

Zhou Lu, Qiuyi Zhang, Xinyi Chen +3

Fast changing states or volatile environments pose a significant challenge to online optimization, which needs to perform rapid adaptation under limited observation. In this paper,…