A Hybrid Deep Architecture for Face Recognition in Real-Life Scenario. - Amartya Sanyal; Ujjwal Bhattacharya; Swapan K. Parui (ICVGIP Workshops, 2016)

Agent based simulation of the evolution of society as an alternate maximzation problem. - Amartya Sanyal; Sanjana Garg; Asim Unmesh; Harish Karnick (BESC, 2017)

Multiscale sequence modeling with a learned dictionary. - Bart van Merriënboer; Amartya Sanyal; Hugo Larochelle; Yoshua Bengio (CoRR, 2017)

Agent based simulation of the evolution of society as an alternate maximization problem. - Amartya Sanyal; Sanjana Garg; Asim Unmesh (CoRR, 2017)

TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service. - Amartya Sanyal; Matt J. Kusner; Adrià Gascón; Varun Kanade (ICML, 2018)

Low Rank Structure of Learned Representations. - Amartya Sanyal; Varun Kanade; Philip H. S. Torr (CoRR, 2018)

Optimizing non-decomposable measures with deep networks. - Amartya Sanyal; Pawan Kumar; Purushottam Kar; Sanjay Chawla; Fabrizio Sebastiani 001 (Mach. Learn., 2018)

Stable Rank Normalization for Improved Generalization in Neural Networks and GANs. - Amartya Sanyal; Philip H. S. Torr; Puneet K. Dokania (ICLR, 2020)

Calibrating Deep Neural Networks using Focal Loss. - Jishnu Mukhoti; Viveka Kulharia; Amartya Sanyal; Stuart Golodetz; Philip H. S. Torr; Puneet K. Dokania (NeurIPS, 2020)

Hierarchical discovery of large-scale and focal copy number alterations in low-coverage cancer genomes. - Ahmed Ibrahim S. Khalil; Costerwell Khyriem; Anupam Chattopadhyay; Amartya Sanyal (BMC Bioinform., 2020)

Identification and utilization of copy number information for correcting Hi-C contact map of cancer cell lines. - Ahmed Ibrahim S. Khalil; Siti Rawaidah Binte Mohammad Muzaki; Anupam Chattopadhyay; Amartya Sanyal (BMC Bioinform., 2020)

Progressive Skeletonization: Trimming more fat from a network at initialization. - Pau de Jorge; Amartya Sanyal; Harkirat S. Behl; Philip H. S. Torr; Grégory Rogez; Puneet K. Dokania (ICLR, 2021)

How Benign is Benign Overfitting ? - Amartya Sanyal; Puneet K. Dokania; Varun Kanade; Philip H. S. Torr (ICLR, 2021)

Identifying and Exploiting Structures for Reliable Deep Learning. - Amartya Sanyal (CoRR, 2021)

Identifying and exploiting structures for reliable deep learning - Amartya Sanyal (2021)

Make Some Noise: Reliable and Efficient Single-Step Adversarial Training. - Pau de Jorge Aranda; Adel Bibi; Riccardo Volpi; Amartya Sanyal; Philip H. S. Torr; Grégory Rogez; Puneet K. Dokania (NeurIPS, 2022)

How unfair is private learning? - Amartya Sanyal; Yaxi Hu; Fanny Yang (UAI, 2022)

Catastrophic overfitting is a bug but also a feature. - Guillermo Ortiz-Jiménez; Pau de Jorge; Amartya Sanyal; Adel Bibi; Puneet K. Dokania; Pascal Frossard; Grégory Rogez; Philip H. S. Torr (CoRR, 2022)

How robust are pre-trained models to distribution shift? - Yuge Shi; Imant Daunhawer; Julia E. Vogt; Philip H. S. Torr; Amartya Sanyal (CoRR, 2022)

Do you pay for Privacy in Online learning? - Amartya Sanyal; Giorgia Ramponi (CoRR, 2022)

A law of adversarial risk, interpolation, and label noise. - Daniel Paleka; Amartya Sanyal (ICLR, 2023)

How robust is unsupervised representation learning to distribution shift? - Yuge Shi; Imant Daunhawer; Julia E. Vogt; Philip H. S. Torr; Amartya Sanyal (ICLR, 2023)

Certifying Ensembles: A General Certification Theory with S-Lipschitzness. - Aleksandar Petrov; Francisco Eiras; Amartya Sanyal; Philip H. S. Torr; Adel Bibi (ICML, 2023)

Can semi-supervised learning use all the data effectively? A lower bound perspective. - Alexandru Tifrea; Gizem Yüce; Amartya Sanyal; Fanny Yang (NeurIPS, 2023)

Sample-efficient private data release for Lipschitz functions under sparsity assumptions. - Konstantin Donhauser; Johan Lokna; Amartya Sanyal; March Boedihardjo; Robert Hönig; Fanny Yang (CoRR, 2023)

How robust accuracy suffers from certified training with convex relaxations. - Piersilvio De Bartolomeis; Jacob Clarysse; Amartya Sanyal; Fanny Yang (CoRR, 2023)

Catastrophic overfitting can be induced with discriminative non-robust features. - Guillermo Ortiz-Jiménez; Pau de Jorge; Amartya Sanyal; Adel Bibi; Puneet K. Dokania; Pascal Frossard; Grégory Rogez; Philip Torr 001 (Trans. Mach. Learn. Res., 2023)

Certified private data release for sparse Lipschitz functions. - Konstantin Donhauser; Johan Lokna; Amartya Sanyal; March Boedihardjo; Robert Hönig; Fanny Yang (AISTATS, 2024)

On the Growth of Mistakes in Differentially Private Online Learning: A Lower Bound Perspective. - Daniil Dmitriev; Kristóf Szabó; Amartya Sanyal (COLT, 2024)

The Role of Learning Algorithms in Collective Action. - Omri Ben-Dov; Jake Fawkes; Samira Samadi; Amartya Sanyal (ICML, 2024)

Provable Privacy with Non-Private Pre-Processing. - Yaxi Hu; Amartya Sanyal; Bernhard Schölkopf (ICML, 2024)

Robust Mixture Learning when Outliers Overwhelm Small Groups. - Daniil Dmitriev; Rares-Darius Buhai; Stefan Tiegel; Alexander Wolters; Gleb Novikov; Amartya Sanyal; David Steurer; Fanny Yang (NeurIPS, 2024)

What Makes and Breaks Safety Fine-tuning? A Mechanistic Study. - Samyak Jain; Ekdeep Singh Lubana; Kemal Oksuz; Tom Joy; Philip Torr 001; Amartya Sanyal; Puneet K. Dokania (NeurIPS, 2024)

PILLAR: How to make semi-private learning more effective. - Francesco Pinto; Yaxi Hu; Fanny Yang; Amartya Sanyal (SaTML, 2024)

Delta-Influence: Unlearning Poisons via Influence Functions. - Wenjie Li; Jiawei Li; Christian Schröder de Witt; Ameya Prabhu; Amartya Sanyal (CoRR, 2024)

Corrective Machine Unlearning. - Shashwat Goel; Ameya Prabhu; Philip Torr 001; Ponnurangam Kumaraguru; Amartya Sanyal (Trans. Mach. Learn. Res., 2024)

Accuracy on the wrong line: On the pitfalls of noisy data for out-of-distribution generalisation. - Amartya Sanyal; Yaxi Hu; Yaodong Yu; Yian Ma; Yixin Wang; Bernhard Schölkopf (AISTATS, 2025)

Protecting against simultaneous data poisoning attacks. - Neel Alex; Shoaib Ahmed Siddiqui; Amartya Sanyal; David Krueger 001 (ICLR, 2025)

Differentially Private Steering for Large Language Model Alignment. - Anmol Goel; Yaxi Hu; Iryna Gurevych; Amartya Sanyal (ICLR, 2025)

Provable unlearning in topic modeling and downstream tasks. - Stanley Wei; Sadhika Malladi; Sanjeev Arora; Amartya Sanyal (ICLR, 2025)

Open Problems in Machine Unlearning for AI Safety. - Fazl Barez; Tingchen Fu; Ameya Prabhu; Stephen Casper; Amartya Sanyal; Adel Bibi; Aidan O'Gara; Robert Kirk; Ben Bucknall; Timothy Fist; Luke Ong; Philip Torr 001; et al. (CoRR, 2025)

Online Learning and Unlearning. - Yaxi Hu; Bernhard Schölkopf; Amartya Sanyal (CoRR, 2025)

Fairness for the People, by the People: Minority Collective Action. - Omri Ben-Dov; Samira Samadi; Amartya Sanyal; Alexandru Tifrea (CoRR, 2025)