Amartya Sanyal

I am an Assistant Professor in Machine Learning in the Department of Computer Science in University of Copenhagen. I am also an Affiliated Assistant Professor in the Department of Mathematics.

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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

June 10 Differential Privacy for Correlated Data @ Workshop on Applied Algorithm for Machine Learning
July 15 Impact of label noise on OOD robustness @ ICML Workshop on NextGen AI Safety

Recent News

October, 2024

Our paper titled Corrective Machine Unlearning is accepted at TMLR .

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.

September, 2024

I am serving as the General Chair of 3rd IEEE Conference on Secure and Trustworthy Machine Learning.

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.

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.

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

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!

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.







Publications

TMLR, 2024

Corrective Machine Unlearning


Shashwat Goel, Ameya Prabhu, Philip Torr, Ponnurangam Kumaraguru, Amartya Sanyal
Transactions on Machine Learning Research, 2024
Arxiv / Proceedings / code /

NeurIPS, 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
Advances in Neural Information Processing Systems, 2024
Arxiv /

NeurIPS, 2024

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 /

ICML Workshop, 2024
Oral Paper

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 /

TPDP, COLT, 2024

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 /

ICML, 2024

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 /

TPDP, ICML, 2024

Provable Privacy with Non-Private Pre-Processing


Yaxi Hu, Amartya Sanyal , Bernhard Schölkopf
International Conference on Machine Learning, 2024
Arxiv / Proceedings /

TPDP, AISTATS, 2024

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 /

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
Arxiv / Proceedings /

TPDP, SaTML, 2023

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 /

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 / code /

ICML, 2023

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 /

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

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

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 /

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

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 /

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

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 /

NeurIPS, 2020

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 /

ICLR, 2020
Spotlight Paper

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 /

Preprint, 2019

Robustness via Deep Low-Rank Representations


Amartya Sanyal , Varun Kanade, Philip H.S. Torr, Puneet Dokania
Preprint, 2019
Arxiv /

ICML, 2018

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 /

ICML Workshop, 2017

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 /








Design and source code from Jon Barron's website