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ORCIDAtish Agarwala
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2020 – today
- 2025
[i14]Sidak Pal Singh, Hossein Mobahi, Atish Agarwala, Yann N. Dauphin:
Avoiding spurious sharpness minimization broadens applicability of SAM. CoRR abs/2502.02407 (2025)- 2024
- [c6]Yann N. Dauphin, Atish Agarwala, Hossein Mobahi:
Neglected Hessian component explains mysteries in sharpness regularization. NeurIPS 2024 - [c5]Vincent Roulet, Atish Agarwala, Jean-Bastien Grill, Grzegorz Swirszcz, Mathieu Blondel, Fabian Pedregosa:
Stepping on the Edge: Curvature Aware Learning Rate Tuners. NeurIPS 2024 - [i13]Yann N. Dauphin, Atish Agarwala, Hossein Mobahi:
Neglected Hessian component explains mysteries in Sharpness regularization. CoRR abs/2401.10809 (2024) - [i12]Daniel Beaglehole, Ioannis Mitliagkas, Atish Agarwala:
Gradient descent induces alignment between weights and the empirical NTK for deep non-linear networks. CoRR abs/2402.05271 (2024) - [i11]Atish Agarwala, Jeffrey Pennington:
High dimensional analysis reveals conservative sharpening and a stochastic edge of stability. CoRR abs/2404.19261 (2024) - [i10]Noah Marshall, Ke Liang Xiao, Atish Agarwala, Elliot Paquette:
A Clipped Trip: the Dynamics of SGD with Gradient Clipping in High-Dimensions. CoRR abs/2406.11733 (2024) - [i9]Vincent Roulet, Atish Agarwala, Jean-Bastien Grill, Grzegorz Swirszcz, Mathieu Blondel, Fabian Pedregosa:
Stepping on the Edge: Curvature Aware Learning Rate Tuners. CoRR abs/2407.06183 (2024) - [i8]Ke Liang Xiao, Noah Marshall, Atish Agarwala, Elliot Paquette:
Exact Risk Curves of signSGD in High-Dimensions: Quantifying Preconditioning and Noise-Compression Effects. CoRR abs/2411.12135 (2024) - 2023
- [j1]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) - [c4]Atish Agarwala, Yann N. Dauphin:
SAM operates far from home: eigenvalue regularization as a dynamical phenomenon. ICML 2023: 152-168 - [c3]Atish Agarwala, Fabian Pedregosa, Jeffrey Pennington:
Second-order regression models exhibit progressive sharpening to the edge of stability. ICML 2023: 169-195 - [i7]Atish Agarwala, Yann N. Dauphin:
SAM operates far from home: eigenvalue regularization as a dynamical phenomenon. CoRR abs/2302.08692 (2023) - [i6]Vincent Roulet, Atish Agarwala, Fabian Pedregosa:
On the Interplay Between Stepsize Tuning and Progressive Sharpening. CoRR abs/2312.00209 (2023) - 2022
- [c2]Atish Agarwala, Samuel S. Schoenholz:
Deep equilibrium networks are sensitive to initialization statistics. ICML 2022: 136-160 - [i5]Atish Agarwala, Samuel S. Schoenholz:
Deep equilibrium networks are sensitive to initialization statistics. CoRR abs/2207.09432 (2022) - [i4]Atish Agarwala, Fabian Pedregosa, Jeffrey Pennington:
Second-order regression models exhibit progressive sharpening to the edge of stability. CoRR abs/2210.04860 (2022) - 2021
- [c1]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 - [i3]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 abs/2103.15261 (2021) - 2020
- [i2]Atish Agarwala, Abhimanyu Das, Rina Panigrahy, Qiuyi Zhang:
Learning the gravitational force law and other analytic functions. CoRR abs/2005.07724 (2020) - [i1]Atish Agarwala, Jeffrey Pennington, Yann N. Dauphin, Samuel S. Schoenholz:
Temperature check: theory and practice for training models with softmax-cross-entropy losses. CoRR abs/2010.07344 (2020)
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