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M. Pawan Kumar

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Faithful Knowledge Distillation

Jun 08, 2023
Tom A. Lamb, Rudy Brunel, Krishnamurthy DJ Dvijotham, M. Pawan Kumar, Philip H. S. Torr, Francisco Eiras

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Expressive Losses for Verified Robustness via Convex Combinations

May 23, 2023
Alessandro De Palma, Rudy Bunel, Krishnamurthy Dvijotham, M. Pawan Kumar, Robert Stanforth, Alessio Lomuscio

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Provably Correct Physics-Informed Neural Networks

May 17, 2023
Francisco Eiras, Adel Bibi, Rudy Bunel, Krishnamurthy Dj Dvijotham, Philip Torr, M. Pawan Kumar

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Lookback for Learning to Branch

Jun 30, 2022
Prateek Gupta, Elias B. Khalil, Didier Chetélat, Maxime Gasse, Yoshua Bengio, Andrea Lodi, M. Pawan Kumar

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IBP Regularization for Verified Adversarial Robustness via Branch-and-Bound

Jun 29, 2022
Alessandro De Palma, Rudy Bunel, Krishnamurthy Dvijotham, M. Pawan Kumar, Robert Stanforth

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A Stochastic Bundle Method for Interpolating Networks

Jan 29, 2022
Alasdair Paren, Leonard Berrada, Rudra P. K. Poudel, M. Pawan Kumar

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In Defense of the Unitary Scalarization for Deep Multi-Task Learning

Jan 20, 2022
Vitaly Kurin, Alessandro De Palma, Ilya Kostrikov, Shimon Whiteson, M. Pawan Kumar

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Learning to be adversarially robust and differentially private

Jan 06, 2022
Jamie Hayes, Borja Balle, M. Pawan Kumar

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Improving Local Effectiveness for Global robust training

Oct 26, 2021
Jingyue Lu, M. Pawan Kumar

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