| 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 | 275 | 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 | 126 | 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 | 57 | 2021 |
| How benign is benign overfitting? A Sanyal, PK Dokania, V Kanade, PHS Torr International Conference on Learning Representations, (Spotlight Paper) ICLR …, 2021 | 52 | 2021 |
| 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 | 39 | 2019 |
| Optimizing non-decomposable measures with deep networks A Sanyal, P Kumar, P Kar, S Chawla, F Sebastiani Machine Learning 107, 1597-1620, 2018 | 35 | 2018 |
| Robustness via Deep Low-Rank Representations A Sanyal, V Kanade, PHS Torr, PK Dokania arXiv preprint arXiv:1804.07090, 2018 | 34* | 2018 |
| 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 | 18* | 2022 |
| How unfair is private learning ? A Sanyal, Y Hu, F Yang Conference on Uncertainty in Artificial Intelligence (Oral Paper) UAI, 2022 | 17 | 2022 |
| 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 | 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 | 11 | 2017 |
| 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 | 6* | 2022 |
| A law of adversarial risk, interpolation, and label noise D Paleka, A Sanyal International Conference on Learning Representations (ICLR) 2023, 2023 | 5 | 2023 |
| Sample-efficient private data release for Lipschitz functions under sparsity assumptions K Donhauser, J Lokna, A Sanyal, M Boedihardjo, R Hönig, F Yang arXiv preprint arXiv:2302.09680, 2023 | 2 | 2023 |
| PILLAR: How to make semi-private learning more effective F Pinto, Y Hu, F Yang, A Sanyal arXiv preprint arXiv:2306.03962, 2023 | 1 | 2023 |
| 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 | 1* | 2023 |
| A Hybrid Deep Architecture for Face Recognition in Real-life Scenario A Sanyal, U Bhattacharya, SK Parui Computer Vision, Graphics, and Image Processing: ICVGIP 2016 Satellite …, 2017 | 1 | 2017 |
| Certifying Ensembles: A General Certification Theory with S-Lipschitzness A Petrov, F Eiras, A Sanyal, PHS Torr, A Bibi arXiv preprint arXiv:2304.13019, 2023 | | 2023 |
| Do you pay for Privacy in Online learning? A Sanyal, G Ramponi Conference on Learning Theory, 5633-5637, 2022 | | 2022 |
| Certified defences hurt generalisation P De Bartolomeis, J Clarysse, F Yang, A Sanyal I Can't Believe It's Not Better Workshop: Understanding Deep Learning …, 2022 | | 2022 |
Philip TorrProfessor, University of OxfordVerified email at eng.ox.ac.uk
