18 citations · 27 across the 15 of their papers we have counts for
5 papers · 1 filter
How I learned to stop worrying and love StopGrads: Stationarity, Convergence, and a case study on Flow Map Learning
Max W. Shen, Mark Goldstein, Zichu Wang +2
Stopgrads are widely used in training machine learning models, but stopgrads can alter the gradient, stationary points and convergence guarantees of the original objective, which c…
Flow Map Learning via Nongradient Vector Flow
Mark Goldstein, Anshuk Uppal, Raghav Singhal +2
Diffusion and flow-based models benefit from simple regression losses, but inference incurs significant overhead because sampling requires integration. Consistency models address t…
What LLMs explain is not what they believe: Evaluating explanation sufficiency under models' own input beliefs
Nhi Nguyen, Shauli Ravfogel, Rajesh Ranganath
Large language models (LLMs) are increasingly deployed in high-stakes domains, where free-text explanations such as chain-of-thought and post-hoc rationales are used to justify mod…
Estimating Tail Risks in Language Model Output Distributions
Rico Angell, Raghav Singhal, Zachary Horvitz +4
Language models are increasingly capable and are being rapidly deployed on a population-level scale. As a result, the safety of these models is increasingly high-stakes. Fortunatel…
To Use or not to Use Muon: How Simplicity Bias in Optimizers Matters
Sara Dragutinović, Yedi Zhang, Rajesh Ranganath
While Adam has long been the ubiquitous default optimizer for deep neural networks, Muon has recently seen rapid adoption due to its superior training speed. Although much of the l…