collaborators

15 papers

cs.CR2026

Improved Pseudorandom Codes from Permuted Puzzles

Miranda Christ, Noah Golowich, Sam Gunn +2

Watermarks are an essential tool for identifying AI-generated content. Recently, Christ and Gunn (CRYPTO '24) introduced pseudorandom error-correcting codes (PRCs), which are equiv…

cs.LG2026

Learning with Simulators: No Regret in a Computationally Bounded World

Sasha Voitovych, Abhishek Shetty, Noah Golowich +1

Understanding the minimal assumptions necessary for generalization is the fundamental question in learning theory. Unfortunately, most results rely heavily on independence (or some…

cs.DS2026

The Power of Test-Time Training for Approximate Sampling

Noah Golowich, Ankur Moitra, Dhruv Rohatgi

Efficiently sampling from a complex probability distribution is a fundamental problem which has become increasingly pertinent in recent years with the rise of generative AI, as sop…

cs.GT2026

On the Complexity of Correlated Equilibria Beyond Normal-Form Games

Ioannis Anagnostides, Constantinos Daskalakis, Gabriele Farina +3

Correlated equilibria are a fundamental solution concept in game theory. However, despite decades of research, the complexity beyond games of polynomial type -- such as extensive-f…

cs.LG2026

Reject, Resample, Repeat: Understanding Parallel Reasoning in Language Model Inference

Noah Golowich, Fan Chen, Dhruv Rohatgi +4

Inference-time methods that aggregate and prune multiple samples have emerged as a powerful paradigm for steering large language models, yet we lack any principled understanding of…

cs.LG2026

Subliminal Effects in Your Data: A General Mechanism via Log-Linearity

Ishaq Aden-Ali, Noah Golowich, Allen Liu +3

Training modern large language models (LLMs) has become a veritable smorgasbord of algorithms and datasets designed to elicit particular behaviors, making it critical to develop te…