Ambrish Rawat

According to our database, Ambrish Rawat authored at least 34 papers between 2017 and 2024.

Collaborative distances:

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

2024

Attention Tracker: Detecting Prompt Injection Attacks in LLMs.

Kuo-Han Hung, Ching-Yun Ko, Ambrish Rawat, I-Hsin Chung, Winston H. Hsu, Pin-Yu Chen

CoRR, 2024

Attack Atlas: A Practitioner's Perspective on Challenges and Pitfalls in Red Teaming GenAI.

Ambrish Rawat, Stefan Schoepf, Giulio Zizzo, Giandomenico Cornacchia, Muhammad Zaid Hameed, Kieran Fraser, Erik Miehling, Beat Buesser, Elizabeth M. Daly, Mark Purcell, Prasanna Sattigeri, Pin-Yu Chen, Kush R. Varshney

CoRR, 2024

Detectors for Safe and Reliable LLMs: Implementations, Uses, and Limitations.

Swapnaja Achintalwar, Adriana Alvarado Garcia, Ateret Anaby-Tavor, Ioana Baldini, Sara E. Berger, Bishwaranjan Bhattacharjee, Djallel Bouneffouf, Subhajit Chaudhury, Pin-Yu Chen, Lamogha Chiazor, Elizabeth M. Daly, Rogério Abreu de Paula, Pierre L. Dognin, Eitan Farchi, Soumya Ghosh, Michael Hind, Raya Horesh, George Kour, Ja Young Lee, Erik Miehling, Keerthiram Murugesan, Manish Nagireddy, Inkit Padhi, David Piorkowski, Ambrish Rawat, Orna Raz, Prasanna Sattigeri, Hendrik Strobelt, Sarathkrishna Swaminathan, Christoph Tillmann, Aashka Trivedi, Kush R. Varshney, Dennis Wei, Shalisha Witherspoon, Marcel Zalmanovici

CoRR, 2024

Domain Adaptation for Time series Transformers using One-step fine-tuning.

Subina Khanal, Seshu Tirupathi, Giulio Zizzo, Ambrish Rawat, Torben Bach Pedersen

CoRR, 2024

2023

FairSISA: Ensemble Post-Processing to Improve Fairness of Unlearning in LLMs.

Swanand Ravindra Kadhe, Anisa Halimi, Ambrish Rawat, Nathalie Baracaldo

CoRR, 2023

Privacy-Preserving Federated Learning over Vertically and Horizontally Partitioned Data for Financial Anomaly Detection.

Swanand Ravindra Kadhe, Heiko Ludwig, Nathalie Baracaldo, Alan King, Yi Zhou, Keith Houck, Ambrish Rawat, Mark Purcell, Naoise Holohan, Mikio Takeuchi, Ryo Kawahara, Nir Drucker, Hayim Shaul, Eyal Kushnir, Omri Soceanu

CoRR, 2023

Pruning Federated Learning Models for Anomaly Detection in Resource-Constrained Environments.

Simone Magnani, Stefano Braghin, Ambrish Rawat, Roberto Doriguzzi Corin, Mark Purcell, Domenico Siracusa

Proceedings of the IEEE International Conference on Big Data, 2023

Matching Pairs: Attributing Fine-Tuned Models to their Pre-Trained Large Language Models.

Myles Foley, Ambrish Rawat, Taesung Lee, Yufang Hou, Gabriele Picco, Giulio Zizzo

Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2023

2022

Federated Unlearning: How to Efficiently Erase a Client in FL?

Anisa Halimi, Swanand Kadhe, Ambrish Rawat, Nathalie Baracaldo

CoRR, 2022

Challenges and Pitfalls of Bayesian Unlearning.

Ambrish Rawat, James Requeima, Wessel P. Bruinsma, Richard E. Turner

CoRR, 2022

Towards an Accountable and Reproducible Federated Learning: A FactSheets Approach.

Nathalie Baracaldo, Ali Anwar, Mark Purcell, Ambrish Rawat, Mathieu Sinn, Bashar Altakrouri, Dian Balta, Mahdi Sellami, Peter Kuhn, Ulrich Schöpp, Matthias Buchinger

CoRR, 2022

The Devil Is in the GAN: Backdoor Attacks and Defenses in Deep Generative Models.

Ambrish Rawat, Killian Levacher, Mathieu Sinn

Proceedings of the Computer Security - ESORICS 2022, 2022

Robust Learning Protocol for Federated Tumor Segmentation Challenge.

Ambrish Rawat, Giulio Zizzo, Swanand Kadhe, Jonathan P. Epperlein, Stefano Braghin

Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2022

Machine Learning Platform for Extreme Scale Computing on Compressed IoT Data.

Seshu Tirupathi, Dhaval Salwala, Giulio Zizzo, Ambrish Rawat, Mark Purcell, Søren Kejser Jensen, Christian Thomsen, Nguyen Ho, Carlos E. Muñiz-Cuza, Jonas Brusokas, Torben Bach Pedersen, Giorgos Alexiou, Giorgos Giannopoulos, Panagiotis Gidarakos, Alexandros Kalimeris, Stavros Maroulis, George Papastefanatos, Ioannis Psarros, Vassilis Stamatopoulos, Manolis Terrovitis

Proceedings of the IEEE International Conference on Big Data, 2022

Bandit Limited Discrepancy Search and Application to Machine Learning Pipeline Optimization.

Akihiro Kishimoto, Djallel Bouneffouf, Radu Marinescu, Parikshit Ram, Ambrish Rawat, Martin Wistuba, Paulito P. Palmes, Adi Botea

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

Security and Robustness in Federated Learning.

Ambrish Rawat, Giulio Zizzo, Muhammad Zaid Hameed, Luis Muñoz-González

Proceedings of the Federated Learning, 2022

2021

Certified Federated Adversarial Training.

Giulio Zizzo, Ambrish Rawat, Mathieu Sinn, Sergio Maffeis, Chris Hankin

CoRR, 2021

Automated Robustness with Adversarial Training as a Post-Processing Step.

Ambrish Rawat, Mathieu Sinn, Beat Buesser

CoRR, 2021

The Devil is in the GAN: Defending Deep Generative Models Against Backdoor Attacks.

Ambrish Rawat, Killian Levacher, Mathieu Sinn

CoRR, 2021

Searching for Machine Learning Pipelines Using a Context-Free Grammar.

Radu Marinescu, Akihiro Kishimoto, Parikshit Ram, Ambrish Rawat, Martin Wistuba, Paulito P. Palmes, Adi Botea

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

FAT: Federated Adversarial Training.

Giulio Zizzo, Ambrish Rawat, Mathieu Sinn, Beat Buesser

CoRR, 2020

IBM Federated Learning: an Enterprise Framework White Paper V0.1.

Heiko Ludwig, Nathalie Baracaldo, Gegi Thomas, Yi Zhou, Ali Anwar, Shashank Rajamoni, Yuya Jeremy Ong, Jayaram Radhakrishnan, Ashish Verma, Mathieu Sinn, Mark Purcell, Ambrish Rawat, Tran Ngoc Minh, Naoise Holohan, Supriyo Chakraborty, Shalisha Witherspoon, Dean Steuer, Laura Wynter, Hifaz Hassan, Sean Laguna, Mikhail Yurochkin, Mayank Agarwal, Ebube Chuba, Annie Abay

CoRR, 2020

Automation of Deep Learning - Theory and Practice.

Martin Wistuba, Ambrish Rawat, Tejaswini Pedapati

Proceedings of the 2020 on International Conference on Multimedia Retrieval, 2020

Survey on Automated End-to-End Data Science?

Djallel Bouneffouf, Charu C. Aggarwal, Thanh Hoang, Udayan Khurana, Horst Samulowitz, Beat Buesser, Sijia Liu, Tejaswini Pedapati, Parikshit Ram, Ambrish Rawat, Martin Wistuba, Alexander G. Gray

Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

2019

How can AI Automate End-to-End Data Science?

Charu C. Aggarwal, Djallel Bouneffouf, Horst Samulowitz, Beat Buesser, Thanh Hoang, Udayan Khurana, Sijia Liu, Tejaswini Pedapati, Parikshit Ram, Ambrish Rawat, Martin Wistuba, Alexander G. Gray

CoRR, 2019

A Survey on Neural Architecture Search.

Martin Wistuba, Ambrish Rawat, Tejaswini Pedapati

CoRR, 2019

Scalable Large Margin Gaussian Process Classification.

Martin Wistuba, Ambrish Rawat

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2019

2018

Adversarial Robustness Toolbox v0.2.2.

Maria-Irina Nicolae, Mathieu Sinn, Tran Ngoc Minh, Ambrish Rawat, Martin Wistuba, Valentina Zantedeschi, Ian M. Molloy, Benjamin Edwards

CoRR, 2018

Scalable Multi-Class Bayesian Support Vector Machines for Structured and Unstructured Data.

Martin Wistuba, Ambrish Rawat

CoRR, 2018

Non-parametric estimation of Jensen-Shannon Divergence in Generative Adversarial Network training.

Mathieu Sinn, Ambrish Rawat

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

Adversarial Phenomenon in the Eyes of Bayesian Deep Learning.

Ambrish Rawat, Martin Wistuba, Maria-Irina Nicolae

CoRR, 2017

Open-World Visual Recognition Using Knowledge Graphs.

Vincent P. A. Lonij, Ambrish Rawat, Maria-Irina Nicolae

CoRR, 2017

Efficient Defenses Against Adversarial Attacks.

Valentina Zantedeschi, Maria-Irina Nicolae, Ambrish Rawat

Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security, 2017

Extending Knowledge Bases Using Images.

Vincent P. A. Lonij, Ambrish Rawat, Maria-Irina Nicolae

Proceedings of the 6th Workshop on Automated Knowledge Base Construction, 2017