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Lookback for Learning to Branch



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

* Under review 

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



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

* ICML 2022 Workshop on Formal Verification of Machine Learning 

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A Stochastic Bundle Method for Interpolating Networks



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



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


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Learning to be adversarially robust and differentially private



Jamie Hayes , Borja Balle , M. Pawan Kumar

* Preliminary work appeared at PPML 2021 

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Improving Local Effectiveness for Global robust training



Jingyue Lu , M. Pawan Kumar


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Neural Network Branch-and-Bound for Neural Network Verification



Florian Jaeckle , Jingyue Lu , M. Pawan Kumar

* arXiv admin note: substantial text overlap with arXiv:1912.01329 

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ANCER: Anisotropic Certification via Sample-wise Volume Maximization



Francisco Eiras , Motasem Alfarra , M. Pawan Kumar , Philip H. S. Torr , Puneet K. Dokania , Bernard Ghanem , Adel Bibi

* First two authors and the last one contributed equally to this work 

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