Author of the publication

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming.

S. Dathathri, K. Dvijotham, A. Kurakin, A. Raghunathan, J. Uesato, R. Bunel, S. Shankar, J. Steinhardt, I. Goodfellow, P. Liang, and P. Kohli. NeurIPS, (2020)

Please choose a person to relate this publication to

To differ between persons with the same name, the academic degree and the title of an important publication will be displayed. You can also use the button next to the name to display some publications already assigned to the person.

Aditi Ramdorai

Aditi Shrikumar

Aditi Deshpande

Raghunathan Ramakrishnan

Aditi Singh

Other publications of authors with the same name

A Reinforcement Learning Approach to Online Learning of Decision Trees.A. Garlapati, A. Raghunathan, V. Nagarajan, and B. Ravindran. CoRR, (2015)Reliable Test-Time Adaptation via Agreement-on-the-Line.E. Kim, M. Sun, A. Raghunathan, and Z. Kolter. CoRR, (2023)Adversarially robust machine learning with guarantees.A. Raghunathan. Stanford University, USA, (2021)Calibrated ensembles can mitigate accuracy tradeoffs under distribution shift.A. Kumar, T. Ma, P. Liang, and A. Raghunathan. UAI, volume 180 of Proceedings of Machine Learning Research, page 1041-1051. PMLR, (2022)The Pitfalls of Simplicity Bias in Neural Networks.H. Shah, K. Tamuly, A. Raghunathan, P. Jain, and P. Netrapalli. NeurIPS, (2020)Accuracy on the Line: on the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization.J. Miller, R. Taori, A. Raghunathan, S. Sagawa, P. Koh, V. Shankar, P. Liang, Y. Carmon, and L. Schmidt. ICML, volume 139 of Proceedings of Machine Learning Research, page 7721-7735. PMLR, (2021)Just Train Twice: Improving Group Robustness without Training Group Information.E. Liu, B. Haghgoo, A. Chen, A. Raghunathan, P. Koh, S. Sagawa, P. Liang, and C. Finn. ICML, volume 139 of Proceedings of Machine Learning Research, page 6781-6792. PMLR, (2021)Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices.E. Liu, A. Raghunathan, P. Liang, and C. Finn. ICML, volume 139 of Proceedings of Machine Learning Research, page 6925-6935. PMLR, (2021)AutoFT: Robust Fine-Tuning by Optimizing Hyperparameters on OOD Data.C. Choi, Y. Lee, A. Chen, A. Zhou, A. Raghunathan, and C. Finn. CoRR, (2024)T-MARS: Improving Visual Representations by Circumventing Text Feature Learning.P. Maini, S. Goyal, Z. Lipton, J. Kolter, and A. Raghunathan. CoRR, (2023)

BibSonomy

Disambiguation of "Raghunathan, Aditi"

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming.

Please choose a person to relate this publication to

Aditi Ramdorai

Aditi Shrikumar

Aditi Deshpande

Raghunathan Ramakrishnan

Aditi Singh

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Raghunathan, Aditi"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming.

Please choose a person to relate this publication to

Aditi Ramdorai

Aditi Shrikumar

Aditi Deshpande

Raghunathan Ramakrishnan

Aditi Singh

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming.