Atish Agarwala

Publication Activity (10 Years)

Years Active: 2020-2024
Publications (10 Years): 17

Top Topics

High Dimensional

Delta Bar Delta

Software Effort

Top Venues

Trans. Mach. Learn. Res.

Publications

Daniel Beaglehole, Ioannis Mitliagkas, Atish Agarwala
Gradient descent induces alignment between weights and the empirical NTK for deep non-linear networks. CoRR (2024)
Atish Agarwala, Jeffrey Pennington
High dimensional analysis reveals conservative sharpening and a stochastic edge of stability. CoRR (2024)
Vincent Roulet, Atish Agarwala, Jean-Bastien Grill, Grzegorz Swirszcz, Mathieu Blondel, Fabian Pedregosa
Stepping on the Edge: Curvature Aware Learning Rate Tuners. CoRR (2024)
Yann N. Dauphin, Atish Agarwala, Hossein Mobahi
Neglected Hessian component explains mysteries in Sharpness regularization. CoRR (2024)
Noah Marshall, Ke Liang Xiao, Atish Agarwala, Elliot Paquette
A Clipped Trip: the Dynamics of SGD with Gradient Clipping in High-Dimensions. CoRR (2024)
Atish Agarwala, Fabian Pedregosa, Jeffrey Pennington
Second-order regression models exhibit progressive sharpening to the edge of stability. ICML (2023)
Atish Agarwala, Yann N. Dauphin
SAM operates far from home: eigenvalue regularization as a dynamical phenomenon. CoRR (2023)
Vincent Roulet, Atish Agarwala, Fabian Pedregosa
On the Interplay Between Stepsize Tuning and Progressive Sharpening. CoRR (2023)
Atish Agarwala, Yann N. Dauphin
SAM operates far from home: eigenvalue regularization as a dynamical phenomenon. ICML (2023)
Atish Agarwala, Samuel Stern Schoenholz, Jeffrey Pennington, Yann N. Dauphin
Temperature check: theory and practice for training models with softmax-cross-entropy losses. Trans. Mach. Learn. Res. 2023 (2023)
Atish Agarwala, Samuel S. Schoenholz
Deep equilibrium networks are sensitive to initialization statistics. CoRR (2022)
Atish Agarwala, Fabian Pedregosa, Jeffrey Pennington
Second-order regression models exhibit progressive sharpening to the edge of stability. CoRR (2022)
Atish Agarwala, Samuel S. Schoenholz
Deep equilibrium networks are sensitive to initialization statistics. ICML (2022)
Atish Agarwala, Abhimanyu Das, Brendan Juba, Rina Panigrahy, Vatsal Sharan, Xin Wang, Qiuyi Zhang
One Network Fits All? Modular versus Monolithic Task Formulations in Neural Networks. CoRR (2021)
Atish Agarwala, Abhimanyu Das, Brendan Juba, Rina Panigrahy, Vatsal Sharan, Xin Wang, Qiuyi Zhang
One Network Fits All? Modular versus Monolithic Task Formulations in Neural Networks. ICLR (2021)
Atish Agarwala, Jeffrey Pennington, Yann N. Dauphin, Samuel S. Schoenholz
Temperature check: theory and practice for training models with softmax-cross-entropy losses. CoRR (2020)
Atish Agarwala, Abhimanyu Das, Rina Panigrahy, Qiuyi Zhang
Learning the gravitational force law and other analytic functions. CoRR (2020)