Rethinking Scaling Laws for Learning in Strategic Environments

no code implementations • 12 Feb 2024 • Tinashe Handina, Eric Mazumdar

We find that strategic interactions can break the conventional view of scaling laws$\unicode{x2013}$meaning that performance does not necessarily monotonically improve as models get larger and/ or more expressive (even with infinite data).

Model Selection Multi-agent Reinforcement Learning

Chasing Convex Bodies and Functions with Black-Box Advice

no code implementations • 23 Jun 2022 • Nicolas Christianson, Tinashe Handina, Adam Wierman

We consider the problem of convex function chasing with black-box advice, where an online decision-maker aims to minimize the total cost of making and switching between decisions in a normed vector space, aided by black-box advice such as the decisions of a machine-learned algorithm.

Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness?

2 code implementations • ICLR 2022 • Vikash Sehwag, Saeed Mahloujifar, Tinashe Handina, Sihui Dai, Chong Xiang, Mung Chiang, Prateek Mittal

We circumvent this challenge by using additional data from proxy distributions learned by advanced generative models.

Adversarial Robustness Image Classification