| 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 | 452 | 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 | 163 | 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 | 102 | 2021 |
| How benign is benign overfitting? A Sanyal, PK Dokania, V Kanade, PHS Torr International Conference on Learning Representations, (Spotlight Paper) ICLR …, 2021 | 63 | 2021 |
| 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 | 55* | 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 | 52 | 2019 |
| Optimizing non-decomposable measures with deep networks A Sanyal, P Kumar, P Kar, S Chawla, F Sebastiani Machine Learning 107, 1597-1620, 2018 | 39 | 2018 |
| 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 | 35* | 2022 |
| Robustness via Deep Low-Rank Representations A Sanyal, V Kanade, PHS Torr, PK Dokania arXiv preprint arXiv:1804.07090, 2018 | 34* | 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 | 32* | 2023 |
| How unfair is private learning ? A Sanyal, Y Hu, F Yang Conference on Uncertainty in Artificial Intelligence (Oral Paper) UAI, 2022 | 24 | 2022 |
| 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 |
| 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 | 11 | 2023 |
| Corrective machine unlearning S Goel, A Prabhu, P Torr, P Kumaraguru, A Sanyal arXiv preprint arXiv:2402.14015, 2024 | 10 | 2024 |
| A law of adversarial risk, interpolation, and label noise D Paleka, A Sanyal International Conference on Learning Representations (ICLR) 2023, 2023 | 8 | 2023 |
| 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 | 5* | 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, ... Transactions on Machine Learning Research, 2023 | 5* | 2023 |
| 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, 2024 | 3 | 2024 |
| What Makes and Breaks Safety Fine-tuning? Mechanistic Study S Jain, ES Lubana, K Oksuz, T Joy, PHS Torr, A Sanyal, PK Dokania arXiv preprint arXiv:2407.10264, 2024 | 2 | 2024 |
| How robust accuracy suffers from certified training with convex relaxations P De Bartolomeis, J Clarysse, A Sanyal, F Yang arXiv preprint arXiv:2306.06995, 2023 | 2 | 2023 |
Philip TorrProfessor, University of OxfordEmail confirmado em eng.ox.ac.uk
