Till then, I am a postdoctoral fellow at the Empirical Inference
group in Max Planck
Institute for Intelligent Systems, Tubingen where I
work closely with
Prof. Bernhard Schölkopf . Prior to this, I was a
postdoctoral fellow at ETH Zurich AI Center
where I worked closely with Prof. Fanny Yang .
I completed my DPhil (PhD) at the
Department of
Computer Science, University of Oxford, funded by
the Turing
Doctoral Studentship. I was also a member of the
Torr Vision
Group. My DPhil advisors were Varun
Kanade and
Philip H.S. Torr. I am also a member of the ELLIS Society.
Prior to that, I completed my undergraduate (B.Tech in
Computer Science) at the Indian Institute
of Technology, Kanpur. On various occassions, I
have spent time at
Facebook AI Research (FAIR),
Twitter Cortex , Laboratory for
Computational and Statistical Learning, Montreal Institute of
Learning Algorithms , and Amazon ML.
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If you are interested in a PhD position with me, see here for more details
Research Interests
I am interested in understanding both theoretical and
empirical aspects of Trustworthy Machine Learning
including privacy, robustness, fairness. In
particular, I am focusing on the impacts that
inadequate data and computation may have on the
trustworthiness of ML algorithms, especially under
adversarial settings, and how one can solve these
issues with reasonable approximations and relaxations
including semi-supervised and self-supervised
learning.
Current Students
I am very lucky to be currently working with the following
students.
Anmol Goel (ELLIS PhD
Student; Co-advised with Prof.
Iryna Gurevych)
Omri
Ben-Dov (PhD Student; Co-advised with Dr.
Samira Samadi)
Yaxi
Hu (PhD Student; Advised by Prof.
Bernhard Schölkopf and Prof. Fanny Yang)
Upcoming/Recent Talks
University of Helsinki
September 21
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Harnessing Low Dimensionality and public unlabelled data in Semi-Private Machine Learning Algorithms
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MIT Algorithmic Fairness Seminar
September 29
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Fairness and Privacy in Machine Learning: Challenges in Long-Tailed Data and Innovations in Semi-Private Algorithms
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Meta AI NYC
October 03
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Harnessing Low Dimensionality and public unlabelled data in Semi-Private Machine Learning Algorithms
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University of Michigan Data Science Seminar
October 04
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Harnessing Low Dimensionality and public unlabelled data in Semi-Private Machine Learning Algorithms
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Recent News
September, 2023 |
Our paper on understanding the capabilities of semi-supervised
learning was accepted as a spotlight paper in NeurIPS 2023. Paper to
appear soon.
|
August, 2023 |
Two papers on i) leveraging small amounts of
unlabelled public data for differentially private learning and
ii) certified private
data release were accepted in TPDP 2023
|
July, 2023 |
Our paper titled
Catastrophic overfitting can be induced with discriminative non-robust
features is accepted at TMLR
.
|
April, 2023 |
Our paper titled
Certifying Ensembles: A General Certification Theory with
S-Lipschitzness is accepted at ICML 2023.
|
April, 2023 |
Co-organising the Workshop on Pitfalls
of limited data and computation for Trustworthy ML at ICLR 2023 in Kigali, Rwanda.
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January, 2023 |
Selected to speak at the Rising Stars in AI
Symposium, 2023 in KAUST.
|
January, 2023 |
Two papers on i) interpolating label noise provably hurts adversarial
robustness and ii)
robustness of unsupervised representation learning
to distribution shift were accepted in ICLR 2023
|
September, 2022 |
Our paper titled Make Some
Noise: Reliable and Efficient Single-Step Adversarial Training
is accepted at NeurIPS 2022.
|
April, 2022 |
Our paper
How unfair is private learning? on the interaction of Privacy,
Accuracy, and Fairness received an Oral in UAI 2022.
|
Publications
NeurIPS, 2023
Spotlight Paper
|
Can semi-supervised learning use all the data effectively? A lower bound perspective
Gizem Yüce* , Alexandru Țifrea , Amartya Sanyal , Fanny Yang
Advances in Neural Information Processing Systems, 2023
Spotlight Paper
|
TPDP, 2023
|
PILLAR: How to make semi-private learning more effective
Francesco Pinto, Yaxi Hu, Fanny Yang, Amartya Sanyal
Workshop on Pitfalls of limited data and computation for Trustworthy ML Theory and Practice of Differential Privacy , 2023
Arxiv /
Proceedings /
poster /
|
TPDP, 2023
|
Sample-efficient private data release for Lipschitz functions under sparsity assumptions
Konstantin Donhauser, Johan Lokna, Amartya Sanyal , March Boedihardjo, Robert Hönig, Fanny Yang,
Theory and Practice of Differential Privacy , 2023
Arxiv /
|
TMLR, 2023
|
Catastrophic overfitting can be induced with discriminative non-robust features
Guillermo Ortiz-Jiménez, Pau de Jorge, Amartya Sanyal , Adel Bibi, Puneet Dokania, Pascal Frossard, Gregory Rogéz, Philip H.S. Torr
TMLR , 2023
Arxiv /
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ICML, 2023
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Certifying Ensembles: A General Certification Theory with S-Lipschitzness
Aleksandar Petrov, Francisco Eiras, Amartya Sanyal , Philip H.S. Torr, Adel Bibi
International Conference on Machine Learning , 2023
Arxiv /
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NeurIPS Workshop, 2023
Oral Paper
|
How robust accuracy suffers from certified training with convex relaxations
Piersilvio De Bartolomeis, Jacob Clarysse, Fanny Yang, Amartya Sanyal
Workshop on Understanding Deep Learning Through Empirical Falsification , 2023
Oral Paper
Arxiv /
Proceedings /
|
Preprint, 2023
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Towards Adversarial Evaluations for Inexact Machine Unlearning
Shashwat Goel, Ameya Prabhu, Amartya Sanyal , Ser-Nam Lim, Philip Torr, Ponnurangam Kumaraguru
Arxiv, 2023
Arxiv /
|
ICLR, 2023
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A law of adversarial risk, interpolation, and label noise
Daniel Paleka*, Amartya Sanyal*
International Conference on Learning Representations (ICLR), 2023
Arxiv /
Proceedings /
poster /
|
ICLR, 2023
|
How robust is unsupervised representation learning to distribution shift?
Yuge Shi, Imant Daunhawer, Julia E. Vogt, Philip H.S. Torr , Amartya Sanyal .
International Conference on Learning Representations (ICLR), 2023
Arxiv /
Proceedings /
|
NeurIPS, 2022
|
Make Some Noise: Reliable and Efficient Single-Step Adversarial Training
Pau De Jorge Aranda, Adel Bibi , Ricardo Volpi, Amartya Sanyal , Philip H.S. Torr, Grégory Rogez , Puneet Dokania
Neural Information Processing Systems (NeurIPS) 2022., 2022
Arxiv /
Proceedings /
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SlowDNN Workshop, 2022
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Semi-private learning via low dimensional structures
Yaxi Hu, Francesco Pinto, Fanny Yang, Amartya Sanyal
Workshop on Seeking Low-Dimensionality in Deep Neural Networks, 2022
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UAI, 2022
Oral Paper
|
How unfair is private learning ?
Amartya Sanyal , Yaxi Hu, Fanny Yang
Conference on Uncertainty in Artificial Intelligence (UAI) , 2022
Oral Paper
Arxiv /
Proceedings /
poster /
slides /
|
COLT, 2022
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Open Problem: Do you pay for Privacy in Online Learning ?
Amartya Sanyal , Giorgia Ramponi
Conference on Learning Theory (COLT) Open Problems, 2022
Arxiv /
Proceedings /
slides /
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ICLR, 2021
Spotlight Paper
|
How benign is benign overfitting?
Amartya Sanyal , Varun Kanade, Puneet Dokania, Philip H.S. Torr
International Conference of Learning Representations (ICLR) , 2021
Spotlight Paper
Arxiv /
Proceedings /
poster /
|
ICLR, 2021
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Progressive Skeletonization: Trimming more fat from a network at initialization
Pau De Jorge Aranda, Amartya Sanyal , Harkirat Behl , Philip H.S. Torr, Grégory Rogez , Puneet Dokania
International Conference of Learning Representations (ICLR), 2021
Arxiv /
Proceedings /
code /
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NeurIPS, 2020
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Calibrating Deep Neural Networks using Focal Loss
Jishnu Mukhoti, Viveka Kulharia, Amartya Sanyal , Stuart Golodetz, Philip H.S. Torr, Puneet Dokania
Advances in Neural Information Processing Systems (NeurIPS), 2020
Arxiv /
Proceedings /
code /
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ICLR, 2020
Spotlight Paper
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Stable Rank Normalization for Improved Generalization in Neural Networks and GANs
Amartya Sanyal , Philip H.S. Torr, Puneet Dokania
International Conference of Learning Representations (ICLR), 2020
Spotlight Paper
Arxiv /
Proceedings /
video /
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Preprint, 2019
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Robustness via Deep Low-Rank Representations
Amartya Sanyal , Varun Kanade, Philip H.S. Torr, Puneet Dokania
Preprint, 2019
Arxiv /
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ICML, 2018
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TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service
Amartya Sanyal , Matt Kusner , Adrià Gascón Varun Kanade
International Conference of Machine Learning (ICML), 2018
Arxiv /
Proceedings /
code /
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ICML Workshop, 2017
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Multiscale sequence modeling with a learned dictionary
Bart van Merriënboer, Amartya Sanyal , Hugo Larochelle, Yoshua Bengio
Machine Learning in Speech and Language Processing, 2017
Arxiv /
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