On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models. - Sven Gowal; Krishnamurthy Dvijotham; Robert Stanforth; Rudy Bunel; Chongli Qin; Jonathan Uesato; Relja Arandjelovic; Timothy A. Mann; Pushmeet Kohli (CoRR, 2018)

Scalable Verified Training for Provably Robust Image Classification. - Sven Gowal; Krishnamurthy Dvijotham; Robert Stanforth; Rudy Bunel; Chongli Qin; Jonathan Uesato; Relja Arandjelovic; Timothy Arthur Mann; Pushmeet Kohli (ICCV, 2019)

Verification of Non-Linear Specifications for Neural Networks. - Chongli Qin; Krishnamurthy (Dj) Dvijotham; Brendan O'Donoghue; Rudy Bunel; Robert Stanforth; Sven Gowal; Jonathan Uesato; Grzegorz Swirszcz; Pushmeet Kohli (ICLR (Poster), 2019)

Adversarial Robustness through Local Linearization. - Chongli Qin; James Martens; Sven Gowal; Dilip Krishnan; Krishnamurthy Dvijotham; Alhussein Fawzi; Soham De; Robert Stanforth; Pushmeet Kohli (NeurIPS, 2019)

Efficient Neural Network Verification with Exactness Characterization. - Krishnamurthy (Dj) Dvijotham; Robert Stanforth; Sven Gowal; Chongli Qin; Soham De; Pushmeet Kohli (UAI, 2019)

An Alternative Surrogate Loss for PGD-based Adversarial Testing. - Sven Gowal; Jonathan Uesato; Chongli Qin; Po-Sen Huang; Timothy A. Mann; Pushmeet Kohli (CoRR, 2019)

Achieving Robustness in the Wild via Adversarial Mixing With Disentangled Representations. - Sven Gowal; Chongli Qin; Po-Sen Huang; A. Taylan Cemgil; Krishnamurthy Dvijotham; Timothy A. Mann; Pushmeet Kohli (CVPR, 2020)

A Framework for robustness Certification of Smoothed Classifiers using F-Divergences. - Krishnamurthy (Dj) Dvijotham; Jamie Hayes; Borja Balle; J. Zico Kolter; Chongli Qin; András György 001; Kai Xiao; Sven Gowal; Pushmeet Kohli (ICLR, 2020)

Training Generative Adversarial Networks by Solving Ordinary Differential Equations. - Chongli Qin; Yan Wu 010; Jost Tobias Springenberg; Andy Brock; Jeff Donahue; Timothy P. Lillicrap; Pushmeet Kohli (NeurIPS, 2020)

Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples. - Sven Gowal; Chongli Qin; Jonathan Uesato; Timothy A. Mann; Pushmeet Kohli (CoRR, 2020)

Improved protein structure prediction using potentials from deep learning. - Andrew W. Senior; Richard Evans 001; John Jumper; James Kirkpatrick; Laurent Sifre; Tim Green; Chongli Qin; Augustin Zídek; Alexander W. R. Nelson; Alex Bridgland; Hugo Penedones; Stig Petersen; et al. (Nat., 2020)

Feature Likelihood Score: Evaluating the Generalization of Generative Models Using Samples. - Marco Jiralerspong; Avishek Joey Bose; Ian Gemp; Chongli Qin; Yoram Bachrach; Gauthier Gidel (NeurIPS, 2023)

On a continuous time model of gradient descent dynamics and instability in deep learning. - Mihaela Rosca; Yan Wu 010; Chongli Qin; Benoit Dherin (Trans. Mach. Learn. Res., 2023)

Supervised Fine Tuning on Curated Data is Reinforcement Learning (and can be improved). - Chongli Qin; Jost Tobias Springenberg (CoRR, 2025)