Prior to this, I was a postdoctoral fellow at the
Empirical Inference group in Max Planck Institute for
Intelligent Systems, Tubingen where I worked closely
with
Prof. Bernhard Schölkopf . Before that, 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.
Openings with me
If you are interested in a PhD or Postdoc position with me, see here for more details
If you are a student at UCPH and interested in doing a
undergraduate or masters project 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.
Carolin Heinzler (Co-advised with Prof. Amir Yehudayoff)
Johanna Düngler (DDSA PhD fellow; Co-advised with Prof. Rasmus Pagh)
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
July 15
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Impact of label noise on OOD robustness @ ICML Workshop on NextGen AI Safety
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November 12
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Differential Privacy for Correlated Data @ Department of Computer Science, IIT Kanpur
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Recent News
October, 2024 |
Alex Tifrea ,
and I are giving a tutorial on Building trustworthy ML: The role
of label quality and availability at AAAI, 2025.
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October, 2024 |
Our paper titled
Corrective Machine Unlearning is accepted at TMLR
.
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September, 2024 |
We will present two papers titled Robust Mixture Learning when Outliers Overwhelm Small Groups and TWhat Makes and Breaks Safety Fine-tuning? A Mechanistic Study in NeurIPS 2024.
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September, 2024 |
I am serving as the General Chair of 3rd IEEE Conference on
Secure and Trustworthy Machine Learning.
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September, 2024 |
We are organising the ENCORE
Workshop on Defining Holistic Private Data Science for Practice in
UCSD along with Clement Canonne, Rachel Cummings, and Bailey Kacsmar.
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July, 2024 |
We will present two papers titled Provable
Privacy with Non-Private Pre-Processing and The Role of
Learning Algorithms in Collective Action in ICML 2024.
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July, 2024 |
Two papers on i) privacy guarantees with
non-private precossing and
ii) lower bounds for
online DP learning were accepted in TPDP 2024
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May, 2024 |
Our paper titled On the Growth of Mistakes in Differentially Private Online Learning: A Lower Bound Perspective is accepted to COLT 2024. See you in Edmonton!
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April, 2024 |
I received a NNF Start Package grant. I am hiring a PhD student and a Postdoc
to start fall, 2025 . Email me if you think you would be good match.
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Publications
TMLR, 2024
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Corrective Machine Unlearning
Shashwat Goel, Ameya Prabhu, Philip Torr, Ponnurangam Kumaraguru, Amartya Sanyal
Transactions on Machine Learning Research, 2024
Arxiv /
Proceedings /
code /
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NeurIPS, 2024
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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
Advances in Neural Information Processing Systems, 2024
Arxiv /
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NeurIPS, 2024
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What Makes and Breaks Safety Fine-tuning? A Mechanistic Study
Samyak Jain, Ekdeep Singh Lubana, Kemal Oksuz, Tom Joy, Philip H.S. Torr, Amartya Sanyal , Puneet K. Dokania
Advances in Neural Information Processing Systems, 2024
Arxiv /
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ICML Workshop, 2024
Oral Paper
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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
ICML Workshop on Next Generation AI Safety, 2024
Oral Paper
Arxiv /
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TPDP, COLT, 2024
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On the Growth of Mistakes in Differentially Private Online Learning: A Lower Bound Perspective
Daniil Dmitriev, Kristóf Szabó, Amartya Sanyal
Conference on Learning Theory, 2024
Arxiv /
Proceedings /
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ICML, 2024
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The Role of Learning Algorithms in Collective Action
Omri Ben Dov* , Jake Fakes* , Samira Samadi, Amartya Sanyal
International Conference on Machine Learning, 2024
Arxiv /
Proceedings /
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TPDP, ICML, 2024
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Provable Privacy with Non-Private Pre-Processing
Yaxi Hu, Amartya Sanyal , Bernhard Schölkopf
International Conference on Machine Learning, 2024
Arxiv /
Proceedings /
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TPDP, AISTATS, 2024
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Certified private data release for sparse Lipschitz functions
Konstantin Donhauser, Johan Lokna, Amartya Sanyal , March Boedihardjo, Robert Hönig, Fanny Yang,
Artificial Intelligence and Statistics Theory and Practice of Differential Privacy , 2024
Arxiv /
Proceedings /
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NeurIPS, 2023
Spotlight Paper
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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
Arxiv /
Proceedings /
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TPDP, SaTML, 2023
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PILLAR: How to make semi-private learning more effective
Francesco Pinto, Yaxi Hu, Fanny Yang, Amartya Sanyal
IEEE Conference on Secure and Trustworthy Machine Learning Theory and Practice of Differential Privacy Workshop on Pitfalls of limited data and computation for Trustworthy ML , 2023
Arxiv /
Proceedings /
code /
poster /
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TMLR, 2023
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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 /
code /
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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
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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 /
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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 /
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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 /
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ICLR, 2023
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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 /
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NeurIPS, 2022
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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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UAI, 2022
Oral Paper
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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 /
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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
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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 /
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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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