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Amartya Sanyal
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2020 – today
- 2024
- [c18]Konstantin Donhauser, Johan Lokna, Amartya Sanyal, March Boedihardjo, Robert Hönig, Fanny Yang:
Certified private data release for sparse Lipschitz functions. AISTATS 2024: 1396-1404 - [c17]Daniil Dmitriev, Kristóf Szabó, Amartya Sanyal:
On the Growth of Mistakes in Differentially Private Online Learning: A Lower Bound Perspective. COLT 2024: 1379-1398 - [c16]Omri Ben-Dov, Jake Fawkes, Samira Samadi, Amartya Sanyal:
The Role of Learning Algorithms in Collective Action. ICML 2024 - [c15]Yaxi Hu, Amartya Sanyal, Bernhard Schölkopf:
Provable Privacy with Non-Private Pre-Processing. ICML 2024 - [c14]Francesco Pinto, Yaxi Hu, Fanny Yang, Amartya Sanyal:
PILLAR: How to make semi-private learning more effective. SaTML 2024: 110-139 - [i29]Shashwat Goel, Ameya Prabhu, Philip Torr, Ponnurangam Kumaraguru, Amartya Sanyal:
Corrective Machine Unlearning. CoRR abs/2402.14015 (2024) - [i28]Daniil Dmitriev, Kristóf Szabó, Amartya Sanyal:
On the Growth of Mistakes in Differentially Private Online Learning: A Lower Bound Perspective. CoRR abs/2402.16778 (2024) - [i27]Yaxi Hu, Amartya Sanyal, Bernhard Schölkopf:
Provable Privacy with Non-Private Pre-Processing. CoRR abs/2403.13041 (2024) - [i26]Omri Ben-Dov, Jake Fawkes, Samira Samadi, Amartya Sanyal:
The Role of Learning Algorithms in Collective Action. CoRR abs/2405.06582 (2024) - [i25]Amartya Sanyal, Yaxi Hu, Yaodong Yu, Yian Ma, Yixin Wang, Bernhard Schölkopf:
Accuracy on the wrong line: On the pitfalls of noisy data for out-of-distribution generalisation. CoRR abs/2406.19049 (2024) - [i24]Samyak Jain, Ekdeep Singh Lubana, Kemal Oksuz, Tom Joy, Philip H. S. Torr, Amartya Sanyal, Puneet K. Dokania:
What Makes and Breaks Safety Fine-tuning? A Mechanistic Study. CoRR abs/2407.10264 (2024) - [i23]Daniil Dmitriev, Rares-Darius Buhai, Stefan Tiegel, Alexander Wolters, Gleb Novikov, Amartya Sanyal, David Steurer, Fanny Yang:
Robust Mixture Learning when Outliers Overwhelm Small Groups. CoRR abs/2407.15792 (2024) - [i22]Neel Alex, Shoaib Ahmed Siddiqui, Amartya Sanyal, David Krueger:
Protecting against simultaneous data poisoning attacks. CoRR abs/2408.13221 (2024) - 2023
- [j4]Guillermo Ortiz-Jiménez, Pau de Jorge, Amartya Sanyal, Adel Bibi, Puneet K. Dokania, Pascal Frossard, Grégory Rogez, Philip Torr:
Catastrophic overfitting can be induced with discriminative non-robust features. Trans. Mach. Learn. Res. 2023 (2023) - [c13]Daniel Paleka, Amartya Sanyal:
A law of adversarial risk, interpolation, and label noise. ICLR 2023 - [c12]Yuge Shi, Imant Daunhawer, Julia E. Vogt, Philip H. S. Torr, Amartya Sanyal:
How robust is unsupervised representation learning to distribution shift? ICLR 2023 - [c11]Aleksandar Petrov, Francisco Eiras, Amartya Sanyal, Philip H. S. Torr, Adel Bibi:
Certifying Ensembles: A General Certification Theory with S-Lipschitzness. ICML 2023: 27709-27736 - [c10]Alexandru Tifrea, Gizem Yüce, Amartya Sanyal, Fanny Yang:
Can semi-supervised learning use all the data effectively? A lower bound perspective. NeurIPS 2023 - [i21]Konstantin Donhauser, Johan Lokna, Amartya Sanyal, March Boedihardjo, Robert Hönig, Fanny Yang:
Sample-efficient private data release for Lipschitz functions under sparsity assumptions. CoRR abs/2302.09680 (2023) - [i20]Aleksandar Petrov, Francisco Eiras, Amartya Sanyal, Philip H. S. Torr, Adel Bibi
:
Certifying Ensembles: A General Certification Theory with S-Lipschitzness. CoRR abs/2304.13019 (2023) - [i19]Francesco Pinto, Yaxi Hu, Fanny Yang, Amartya Sanyal:
PILLAR: How to make semi-private learning more effective. CoRR abs/2306.03962 (2023) - [i18]Piersilvio De Bartolomeis, Jacob Clarysse, Amartya Sanyal, Fanny Yang:
How robust accuracy suffers from certified training with convex relaxations. CoRR abs/2306.06995 (2023) - [i17]Alexandru Tifrea, Gizem Yüce, Amartya Sanyal, Fanny Yang:
Can semi-supervised learning use all the data effectively? A lower bound perspective. CoRR abs/2311.18557 (2023) - 2022
- [c9]Pau de Jorge Aranda, Adel Bibi, Riccardo Volpi, Amartya Sanyal, Philip H. S. Torr, Grégory Rogez, Puneet K. Dokania:
Make Some Noise: Reliable and Efficient Single-Step Adversarial Training. NeurIPS 2022 - [c8]Amartya Sanyal, Yaxi Hu, Fanny Yang:
How unfair is private learning? UAI 2022: 1738-1748 - [i16]Pau de Jorge, Adel Bibi, Riccardo Volpi, Amartya Sanyal, Philip H. S. Torr, Grégory Rogez, Puneet K. Dokania:
Make Some Noise: Reliable and Efficient Single-Step Adversarial Training. CoRR abs/2202.01181 (2022) - [i15]Amartya Sanyal, Yaxi Hu, Fanny Yang:
How unfair is private learning ? CoRR abs/2206.03985 (2022) - [i14]Guillermo Ortiz-Jiménez, Pau de Jorge, Amartya Sanyal, Adel Bibi
Catastrophic overfitting is a bug but also a feature. CoRR abs/2206.08242 (2022) - [i13]Yuge Shi, Imant Daunhawer, Julia E. Vogt
How robust are pre-trained models to distribution shift? CoRR abs/2206.08871 (2022) - [i12]Daniel Paleka, Amartya Sanyal:
A law of adversarial risk, interpolation, and label noise. CoRR abs/2207.03933 (2022) - [i11]Amartya Sanyal, Giorgia Ramponi:
Do you pay for Privacy in Online learning? CoRR abs/2210.04817 (2022) - 2021
- [b1]Amartya Sanyal:
Identifying and exploiting structures for reliable deep learning. University of Oxford, UK, 2021 - [c7]Pau de Jorge, Amartya Sanyal, Harkirat S. Behl, Philip H. S. Torr, Grégory Rogez, Puneet K. Dokania:
Progressive Skeletonization: Trimming more fat from a network at initialization. ICLR 2021 - [c6]Amartya Sanyal, Puneet K. Dokania, Varun Kanade, Philip H. S. Torr:
How Benign is Benign Overfitting ? ICLR 2021 - [i10]Amartya Sanyal:
Identifying and Exploiting Structures for Reliable Deep Learning. CoRR abs/2108.07083 (2021) - 2020
- [j3]Ahmed Ibrahim S. Khalil
Hierarchical discovery of large-scale and focal copy number alterations in low-coverage cancer genomes. BMC Bioinform. 21(1): 147 (2020) - [j2]Ahmed Ibrahim S. Khalil
Identification and utilization of copy number information for correcting Hi-C contact map of cancer cell lines. BMC Bioinform. 21(1): 506 (2020) - [c5]Amartya Sanyal, Philip H. S. Torr, Puneet K. Dokania:
Stable Rank Normalization for Improved Generalization in Neural Networks and GANs. ICLR 2020 - [c4]Jishnu Mukhoti, Viveka Kulharia, Amartya Sanyal, Stuart Golodetz, Philip H. S. Torr, Puneet K. Dokania:
Calibrating Deep Neural Networks using Focal Loss. NeurIPS 2020 - [i9]Jishnu Mukhoti, Viveka Kulharia, Amartya Sanyal
Calibrating Deep Neural Networks using Focal Loss. CoRR abs/2002.09437 (2020) - [i8]Pau de Jorge, Amartya Sanyal
Progressive Skeletonization: Trimming more fat from a network at initialization. CoRR abs/2006.09081 (2020) - [i7]Amartya Sanyal
How benign is benign overfitting? CoRR abs/2007.04028 (2020)
2010 – 2019
- 2019
- [i6]Amartya Sanyal
Stable Rank Normalization for Improved Generalization in Neural Networks and GANs. CoRR abs/1906.04659 (2019) - 2018
- [j1]Amartya Sanyal
Optimizing non-decomposable measures with deep networks. Mach. Learn. 107(8-10): 1597-1620 (2018) - [c3]Amartya Sanyal
TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service. ICML 2018: 4497-4506 - [i5]Amartya Sanyal, Pawan Kumar, Purushottam Kar, Sanjay Chawla, Fabrizio Sebastiani:
Optimizing Non-decomposable Measures with Deep Networks. CoRR abs/1802.00086 (2018) - [i4]Amartya Sanyal
Low Rank Structure of Learned Representations. CoRR abs/1804.07090 (2018) - [i3]Amartya Sanyal, Matt J. Kusner, Adrià Gascón, Varun Kanade:
TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service. CoRR abs/1806.03461 (2018) - 2017
- [c2]Amartya Sanyal
Agent based simulation of the evolution of society as an alternate maximzation problem. BESC 2017: 1-7 - [i2]Bart van Merriënboer, Amartya Sanyal, Hugo Larochelle, Yoshua Bengio:
Multiscale sequence modeling with a learned dictionary. CoRR abs/1707.00762 (2017) - [i1]Amartya Sanyal, Sanjana Garg, Asim Unmesh:
Agent based simulation of the evolution of society as an alternate maximization problem. CoRR abs/1707.01546 (2017) - 2016
- [c1]Amartya Sanyal
A Hybrid Deep Architecture for Face Recognition in Real-Life Scenario. ICVGIP Workshops 2016: 120-132
Coauthor Index
aka: Philip H. S. Torr
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