Europe
Pareto Frontiers in Deep Feature Learning: Data, Compute, Width, and Luck Benjamin L. Edelman
This work investigates how these complexities necessarily arise for feature learning in the presence of computational-statistical gaps.
Adaptive Exploration for Data-Efficient General Value Function Evaluations
General V alue Functions (GVFs) (Sutton et al., 2011) represent predictive knowledge in reinforcement learning.
9602d22a8c791f23f8e4d1398e3fb5be-Paper-Conference.pdf
These theoretical findings are supported by experimental results.
Aging with GRACE: Lifelong Model Editing with Discrete Key-Value Adaptors
BERT, and GPT models show GRACE's state-of-the-art performance in making
Doob's Lagrangian: A Sample-Efficient Variational Approach to Transition Path Sampling
To solve this optimization, we propose a simulation-free training objective with a model parameterization that imposes the desired boundary conditions by design.
Online Performative Gradient Descent for Learning Nash Equilibria in Decision-Dependent Games Zihan Zhu Duke University Ethan X. Fang Duke University Zhuoran Yang Yale University
We focus on finding the Nash equilibrium of decision-dependent games in the bandit feedback setting.