| Calibrating Deep Neural Networks using Focal Loss J Mukhoti, V Kulharia, A Sanyal, S Golodetz, PHS Torr, PK Dokania Advances in Neural Information Processing Systems (NeurIPS), December 2020, 2020 | 554 | 2020 |
| TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service A Sanyal, MJ Kusner, A Gascón, V Kanade International Conference on Machine Learning 80, 4497--4506, 2018 | 177 | 2018 |
| Progressive skeletonization: Trimming more fat from a network at initialization P de Jorge, A Sanyal, HS Behl, PHS Torr, G Rogez, PK Dokania International Conference on Learning Representations, ICLR 2021, 2021 | 117 | 2021 |
| How benign is benign overfitting? A Sanyal, PK Dokania, V Kanade, PHS Torr International Conference on Learning Representations, (Spotlight Paper) ICLR …, 2021 | 72 | 2021 |
| Towards Adversarial Evaluations for Inexact Machine Unlearning S Goel, A Prabhu, A Sanyal, SN Lim, P Torr, P Kumaraguru arXiv preprint arXiv:2201.06640, 2022 | 67* | 2022 |
| Make Some Noise: Reliable and Efficient Single-Step Adversarial Training P de Jorge, A Bibi, R Volpi, A Sanyal, PHS Torr, G Rogez, PK Dokania Advances in Neural Information Processing Systems (NeurlPS), 2022 | 63* | 2022 |
| Stable Rank Normalization for Improved Generalization in Neural Networks and GANs A Sanyal, PHS Torr, PK Dokania International Conference on Learning Representations, (Spotlight Paper) ICLR …, 2019 | 55 | 2019 |
| Optimizing non-decomposable measures with deep networks A Sanyal, P Kumar, P Kar, S Chawla, F Sebastiani Machine Learning 107, 1597-1620, 2018 | 40 | 2018 |
| How robust is unsupervised representation learning to distribution shift? Y Shi, I Daunhawer, JE Vogt, P Torr, A Sanyal International Conference on Learning Representations (ICLR), 2023 | 38* | 2023 |
| How unfair is private learning ? A Sanyal, Y Hu, F Yang Conference on Uncertainty in Artificial Intelligence (Oral Paper) UAI, 2022 | 36 | 2022 |
| Robustness via Deep Low-Rank Representations A Sanyal, V Kanade, PHS Torr, PK Dokania arXiv preprint arXiv:1804.07090, 2018 | 35* | 2018 |
| Corrective machine unlearning S Goel, A Prabhu, P Torr, P Kumaraguru, A Sanyal Transactions on Machine Learning Research, 2024 | 28 | 2024 |
| PILLAR: How to make semi-private learning more effective F Pinto, Y Hu, F Yang, A Sanyal Conference on Secure and Trustworthy Machine Learning (SatML) 2024, 2023 | 15 | 2023 |
| Multiscale sequence modeling with a learned dictionary B van Merriënboer, A Sanyal, H Larochelle, Y Bengio ICML 2017 Workshop on Machine Learning in Speech and Language Processing, 2017 | 12 | 2017 |
| Open problems in machine unlearning for ai safety F Barez, T Fu, A Prabhu, S Casper, A Sanyal, A Bibi, A O'Gara, R Kirk, ... arXiv preprint arXiv:2501.04952, 2025 | 11 | 2025 |
| A law of adversarial risk, interpolation, and label noise D Paleka, A Sanyal International Conference on Learning Representations (ICLR) 2023, 2023 | 11 | 2023 |
| What makes and breaks safety fine-tuning? a mechanistic study S Jain, ES Lubana, K Oksuz, T Joy, P Torr, A Sanyal, P Dokania Advances in Neural Information Processing Systems 37, 93406-93478, 2024 | 10 | 2024 |
| Catastrophic overfitting can be induced with discriminative non-robust features G Ortiz-Jimenez, P de Jorge, A Sanyal, A Bibi, PK Dokania, P Frossard, ... arXiv preprint arXiv:2206.08242, 2022 | 7* | 2022 |
| Certified private data release for sparse Lipschitz functions K Donhauser, J Lokna, A Sanyal, M Boedihardjo, R Hönig, F Yang International Conference on Artificial Intelligence and Statistics, 1396-1404, 2024 | 4* | 2024 |
| Can semi-supervised learning use all the data effectively? A lower bound perspective A Tifrea, G Yüce, A Sanyal, F Yang Advances in Neural Information Processing Systems 36, 21960-21982, 2023 | 4 | 2023 |
Philip TorrProfessor, University of OxfordVerified email at eng.ox.ac.uk
