Amartya Sanyal

According to our database, Amartya Sanyal authored at least 31 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Provable Privacy with Non-Private Pre-Processing.

Yaxi Hu, Amartya Sanyal, Bernhard Schölkopf

CoRR, 2024

On the Growth of Mistakes in Differentially Private Online Learning: A Lower Bound Perspective.

Daniil Dmitriev, Kristóf Szabó, Amartya Sanyal

CoRR, 2024

Corrective Machine Unlearning.

Shashwat Goel, Ameya Prabhu, Philip H. S. Torr, Ponnurangam Kumaraguru, Amartya Sanyal

CoRR, 2024

2023

How robust accuracy suffers from certified training with convex relaxations.

Piersilvio De Bartolomeis, Jacob Clarysse, Amartya Sanyal, Fanny Yang

CoRR, 2023

PILLAR: How to make semi-private learning more effective.

Francesco Pinto, Yaxi Hu, Fanny Yang, Amartya Sanyal

CoRR, 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

Can semi-supervised learning use all the data effectively? A lower bound perspective.

Alexandru Tifrea, Gizem Yüce, Amartya Sanyal, Fanny Yang

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Certifying Ensembles: A General Certification Theory with S-Lipschitzness.

Aleksandar Petrov, Francisco Eiras, Amartya Sanyal, Philip H. S. Torr, Adel Bibi

Proceedings of the International Conference on Machine Learning, 2023

How robust is unsupervised representation learning to distribution shift?

Yuge Shi, Imant Daunhawer, Julia E. Vogt, Philip H. S. Torr, Amartya Sanyal

Proceedings of the Eleventh International Conference on Learning Representations, 2023

A law of adversarial risk, interpolation, and label noise.

Daniel Paleka, Amartya Sanyal

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Do you pay for Privacy in Online learning?

Amartya Sanyal, Giorgia Ramponi

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

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

Make Some Noise: Reliable and Efficient Single-Step Adversarial Training.

Pau de Jorge, Adel Bibi, Riccardo Volpi, Amartya Sanyal, Philip H. S. Torr, Grégory Rogez, Puneet K. Dokania

CoRR, 2022

How unfair is private learning?

Amartya Sanyal, Yaxi Hu, Fanny Yang

Proceedings of the Uncertainty in Artificial Intelligence, 2022

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

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Identifying and exploiting structures for reliable deep learning

Amartya Sanyal

PhD thesis, 2021

Identifying and Exploiting Structures for Reliable Deep Learning.

Amartya Sanyal

CoRR, 2021

How Benign is Benign Overfitting ?

Amartya Sanyal, Puneet K. Dokania, Varun Kanade, Philip H. S. Torr

Proceedings of the 9th International Conference on Learning Representations, 2021

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

Proceedings of the 9th International Conference on Learning Representations, 2021

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

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

Calibrating Deep Neural Networks using Focal Loss.

Jishnu Mukhoti, Viveka Kulharia, Amartya Sanyal, Stuart Golodetz, Philip H. S. Torr, Puneet K. Dokania

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Stable Rank Normalization for Improved Generalization in Neural Networks and GANs.

Amartya Sanyal, Philip H. S. Torr, Puneet K. Dokania

Proceedings of the 8th International Conference on Learning Representations, 2020

2018

Optimizing non-decomposable measures with deep networks.

Amartya Sanyal, Pawan Kumar, Purushottam Kar, Sanjay Chawla, Fabrizio Sebastiani

Mach. Learn., 2018

Low Rank Structure of Learned Representations.

Amartya Sanyal, Varun Kanade, Philip H. S. Torr

CoRR, 2018

TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service.

Amartya Sanyal, Matt J. Kusner, Adrià Gascón, Varun Kanade

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

Agent based simulation of the evolution of society as an alternate maximization problem.

Amartya Sanyal, Sanjana Garg, Asim Unmesh

CoRR, 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 maximzation problem.

Amartya Sanyal, Sanjana Garg, Asim Unmesh, Harish Karnick

Proceedings of the 2017 International Conference on Behavioral, 2017

2016

A Hybrid Deep Architecture for Face Recognition in Real-Life Scenario.

Amartya Sanyal, Ujjwal Bhattacharya, Swapan K. Parui

Proceedings of the Computer Vision, Graphics, and Image Processing, 2016