Ambrish Rawat

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 34

Top Topics

Tumor Segmentation

Gaussian Process Classification

Generative Model

Top Venues

BrainLes@MICCAI (2)

Publications

Subina Khanal, Seshu Tirupathi, Giulio Zizzo, Ambrish Rawat, Torben Bach Pedersen
Domain Adaptation for Time series Transformers using One-step fine-tuning. CoRR (2024)
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
Detectors for Safe and Reliable LLMs: Implementations, Uses, and Limitations. CoRR (2024)
Simone Magnani, Stefano Braghin, Ambrish Rawat, Roberto Doriguzzi Corin, Mark Purcell, Domenico Siracusa
Pruning Federated Learning Models for Anomaly Detection in Resource-Constrained Environments. BigData (2023)
Myles Foley, Ambrish Rawat, Taesung Lee, Yufang Hou, Gabriele Picco, Giulio Zizzo
Matching Pairs: Attributing Fine-Tuned Models to their Pre-Trained Large Language Models. ACL (1) (2023)
Myles Foley, Ambrish Rawat, Taesung Lee, Yufang Hou, Gabriele Picco, Giulio Zizzo
Matching Pairs: Attributing Fine-Tuned Models to their Pre-Trained Large Language Models. CoRR (2023)
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
Privacy-Preserving Federated Learning over Vertically and Horizontally Partitioned Data for Financial Anomaly Detection. CoRR (2023)
Swanand Ravindra Kadhe, Anisa Halimi, Ambrish Rawat, Nathalie Baracaldo
FairSISA: Ensemble Post-Processing to Improve Fairness of Unlearning in LLMs. CoRR (2023)
Akihiro Kishimoto, Djallel Bouneffouf, Radu Marinescu, Parikshit Ram, Ambrish Rawat, Martin Wistuba, Paulito P. Palmes, Adi Botea
Bandit Limited Discrepancy Search and Application to Machine Learning Pipeline Optimization. AAAI (2022)
Ambrish Rawat, James Requeima, Wessel P. Bruinsma, Richard E. Turner
Challenges and Pitfalls of Bayesian Unlearning. CoRR (2022)
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
Machine Learning Platform for Extreme Scale Computing on Compressed IoT Data. Big Data (2022)
Ambrish Rawat, Killian Levacher, Mathieu Sinn
The Devil Is in the GAN: Backdoor Attacks and Defenses in Deep Generative Models. ESORICS (3) (2022)
Nathalie Baracaldo, Ali Anwar, Mark Purcell, Ambrish Rawat, Mathieu Sinn, Bashar Altakrouri, Dian Balta, Mahdi Sellami, Peter Kuhn, Ulrich Schöpp, Matthias Buchinger
Towards an Accountable and Reproducible Federated Learning: A FactSheets Approach. CoRR (2022)
Anisa Halimi, Swanand Kadhe, Ambrish Rawat, Nathalie Baracaldo
Federated Unlearning: How to Efficiently Erase a Client in FL? CoRR (2022)
Ambrish Rawat, Giulio Zizzo, Swanand Kadhe, Jonathan P. Epperlein, Stefano Braghin
Robust Learning Protocol for Federated Tumor Segmentation Challenge. BrainLes@MICCAI (2) (2022)
Ambrish Rawat, Giulio Zizzo, Swanand Kadhe, Jonathan P. Epperlein, Stefano Braghin
Robust Learning Protocol for Federated Tumor Segmentation Challenge. CoRR (2022)
Ambrish Rawat, Mathieu Sinn, Beat Buesser
Automated Robustness with Adversarial Training as a Post-Processing Step. CoRR (2021)
Ambrish Rawat, Killian Levacher, Mathieu Sinn
The Devil is in the GAN: Defending Deep Generative Models Against Backdoor Attacks. CoRR (2021)
Giulio Zizzo, Ambrish Rawat, Mathieu Sinn, Sergio Maffeis, Chris Hankin
Certified Federated Adversarial Training. CoRR (2021)
Radu Marinescu, Akihiro Kishimoto, Parikshit Ram, Ambrish Rawat, Martin Wistuba, Paulito P. Palmes, Adi Botea
Searching for Machine Learning Pipelines Using a Context-Free Grammar. AAAI (2021)
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
IBM Federated Learning: an Enterprise Framework White Paper V0.1. CoRR (2020)
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
Survey on Automated End-to-End Data Science? IJCNN (2020)
Martin Wistuba, Ambrish Rawat, Tejaswini Pedapati
Automation of Deep Learning - Theory and Practice. ICMR (2020)
Giulio Zizzo, Ambrish Rawat, Mathieu Sinn, Beat Buesser
FAT: Federated Adversarial Training. CoRR (2020)
Martin Wistuba, Ambrish Rawat
Scalable Large Margin Gaussian Process Classification. ECML/PKDD (2) (2019)
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
How can AI Automate End-to-End Data Science? CoRR (2019)
Martin Wistuba, Ambrish Rawat, Tejaswini Pedapati
A Survey on Neural Architecture Search. CoRR (2019)
Maria-Irina Nicolae, Mathieu Sinn, Tran Ngoc Minh, Ambrish Rawat, Martin Wistuba, Valentina Zantedeschi, Ian M. Molloy, Benjamin Edwards
Adversarial Robustness Toolbox v0.2.2. CoRR (2018)
Mathieu Sinn, Ambrish Rawat
Non-parametric estimation of Jensen-Shannon Divergence in Generative Adversarial Network training. AISTATS (2018)
Martin Wistuba, Ambrish Rawat
Scalable Multi-Class Bayesian Support Vector Machines for Structured and Unstructured Data. CoRR (2018)
Valentina Zantedeschi, Maria-Irina Nicolae, Ambrish Rawat
Efficient Defenses Against Adversarial Attacks. AISec@CCS (2017)
Vincent P. A. Lonij, Ambrish Rawat, Maria-Irina Nicolae
Open-World Visual Recognition Using Knowledge Graphs. CoRR (2017)
Ambrish Rawat, Martin Wistuba, Maria-Irina Nicolae
Adversarial Phenomenon in the Eyes of Bayesian Deep Learning. CoRR (2017)
Valentina Zantedeschi, Maria-Irina Nicolae, Ambrish Rawat
Efficient Defenses Against Adversarial Attacks. CoRR (2017)
Vincent P. A. Lonij, Ambrish Rawat, Maria-Irina Nicolae
Extending Knowledge Bases Using Images. AKBC@NIPS (2017)