1 code implementation • 5 Feb 2024 • Gustaf Ahdritz, Tian Qin, Nikhil Vyas, Boaz Barak, Benjamin L. Edelman
We study the feasibility of identifying epistemic uncertainty (reflecting a lack of knowledge), as opposed to aleatoric uncertainty (reflecting entropy in the underlying distribution), in the outputs of large language models (LLMs) over free-form text.
no code implementations • 24 Jul 2023 • Davis Brown, Nikhil Vyas, Yamini Bansal
Our findings give evidence that while Linear Mode Connectivity improves with increased network width, this improvement is not due to an increase in basis correlation.
no code implementations • 14 Jun 2023 • Nikhil Vyas, Depen Morwani, Rosie Zhao, Gal Kaplun, Sham Kakade, Boaz Barak
The success of SGD in deep learning has been ascribed by prior works to the implicit bias induced by high learning rate or small batch size ("SGD noise").
no code implementations • NeurIPS 2023 • Nikhil Vyas, Alexander Atanasov, Blake Bordelon, Depen Morwani, Sabarish Sainathan, Cengiz Pehlevan
We call this the bias of narrower width.
no code implementations • 21 Feb 2023 • Nikhil Vyas, Sham Kakade, Boaz Barak
There is a growing concern that learned conditional generative models may output samples that are substantially similar to some copyrighted data $C$ that was in their training set.
no code implementations • 20 Jun 2022 • Nikhil Vyas, Yamini Bansal, Preetum Nakkiran
The ``Neural Tangent Kernel'' (NTK) (Jacot et al 2018), and its empirical variants have been proposed as a proxy to capture certain behaviors of real neural networks.
no code implementations • 12 Dec 2018 • Mitali Bafna, Jack Murtagh, Nikhil Vyas
We give a new algorithm for approximating the Discrete Fourier transform of an approximately sparse signal that has been corrupted by worst-case $L_0$ noise, namely a bounded number of coordinates of the signal have been corrupted arbitrarily.
no code implementations • NeurIPS 2018 • Mitali Bafna, Jack Murtagh, Nikhil Vyas
We give a new algorithm for approximating the Discrete Fourier transform of an approximately sparse signal that is robust to worst-case $L_0$ corruptions, namely that some coordinates of the signal can be corrupt arbitrarily.