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Ambrish Rawat
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2020 – today
- 2024
- [i23]Subina Khanal, Seshu Tirupathi, Giulio Zizzo, Ambrish Rawat, Torben Bach Pedersen:
Domain Adaptation for Time series Transformers using One-step fine-tuning. CoRR abs/2401.06524 (2024) - [i22]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 abs/2403.06009 (2024) - [i21]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:
Attack Atlas: A Practitioner's Perspective on Challenges and Pitfalls in Red Teaming GenAI. CoRR abs/2409.15398 (2024) - [i20]Kuo-Han Hung, Ching-Yun Ko, Ambrish Rawat, I-Hsin Chung, Winston H. Hsu, Pin-Yu Chen:
Attention Tracker: Detecting Prompt Injection Attacks in LLMs. CoRR abs/2411.00348 (2024) - 2023
- [c13]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: 7423-7442 - [c12]Simone Magnani
Pruning Federated Learning Models for Anomaly Detection in Resource-Constrained Environments. IEEE Big Data 2023: 3274-3283 - [i19]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 abs/2306.09308 (2023) - [i18]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 abs/2310.19304 (2023) - [i17]Swanand Ravindra Kadhe, Anisa Halimi, Ambrish Rawat, Nathalie Baracaldo:
FairSISA: Ensemble Post-Processing to Improve Fairness of Unlearning in LLMs. CoRR abs/2312.07420 (2023) - 2022
- [c11]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: 10228-10237 - [c10]Seshu Tirupathi, Dhaval Salwala, Giulio Zizzo, Ambrish Rawat, Mark Purcell, Søren Kejser Jensen, Christian Thomsen
Machine Learning Platform for Extreme Scale Computing on Compressed IoT Data. IEEE Big Data 2022: 3179-3185 - [c9]Ambrish Rawat, Giulio Zizzo, Swanand Kadhe, Jonathan P. Epperlein, Stefano Braghin
Robust Learning Protocol for Federated Tumor Segmentation Challenge. BrainLes@MICCAI (2) 2022: 183-195 - [c8]Ambrish Rawat, Killian Levacher, Mathieu Sinn:
The Devil Is in the GAN: Backdoor Attacks and Defenses in Deep Generative Models. ESORICS (3) 2022: 776-783 - [p1]Ambrish Rawat, Giulio Zizzo, Muhammad Zaid Hameed, Luis Muñoz-González:
Security and Robustness in Federated Learning. Federated Learning 2022: 363-390 - [i16]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 abs/2202.12443 (2022) - [i15]Ambrish Rawat, James Requeima, Wessel P. Bruinsma, Richard E. Turner:
Challenges and Pitfalls of Bayesian Unlearning. CoRR abs/2207.03227 (2022) - [i14]Anisa Halimi, Swanand Kadhe, Ambrish Rawat, Nathalie Baracaldo:
Federated Unlearning: How to Efficiently Erase a Client in FL? CoRR abs/2207.05521 (2022) - [i13]Ambrish Rawat, Giulio Zizzo, Swanand Kadhe, Jonathan P. Epperlein, Stefano Braghin
Robust Learning Protocol for Federated Tumor Segmentation Challenge. CoRR abs/2212.08290 (2022) - 2021
- [c7]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: 8902-8911 - [i12]Ambrish Rawat, Killian Levacher, Mathieu Sinn:
The Devil is in the GAN: Defending Deep Generative Models Against Backdoor Attacks. CoRR abs/2108.01644 (2021) - [i11]Ambrish Rawat, Mathieu Sinn, Beat Buesser:
Automated Robustness with Adversarial Training as a Post-Processing Step. CoRR abs/2109.02532 (2021) - [i10]Giulio Zizzo, Ambrish Rawat, Mathieu Sinn, Sergio Maffeis, Chris Hankin:
Certified Federated Adversarial Training. CoRR abs/2112.10525 (2021) - 2020
- [c6]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: 1-9 - [c5]Martin Wistuba, Ambrish Rawat, Tejaswini Pedapati:
Automation of Deep Learning - Theory and Practice. ICMR 2020: 5-6 - [i9]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 abs/2007.10987 (2020) - [i8]Giulio Zizzo, Ambrish Rawat, Mathieu Sinn, Beat Buesser:
FAT: Federated Adversarial Training. CoRR abs/2012.01791 (2020)
2010 – 2019
- 2019
- [c4]Martin Wistuba, Ambrish Rawat:
Scalable Large Margin Gaussian Process Classification. ECML/PKDD (2) 2019: 501-516 - [i7]Martin Wistuba, Ambrish Rawat, Tejaswini Pedapati:
A Survey on Neural Architecture Search. CoRR abs/1905.01392 (2019) - [i6]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 abs/1910.14436 (2019) - 2018
- [c3]Mathieu Sinn, Ambrish Rawat:
Non-parametric estimation of Jensen-Shannon Divergence in Generative Adversarial Network training. AISTATS 2018: 642-651 - [i5]Martin Wistuba, Ambrish Rawat:
Scalable Multi-Class Bayesian Support Vector Machines for Structured and Unstructured Data. CoRR abs/1806.02659 (2018) - [i4]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 abs/1807.01069 (2018) - 2017
- [c2]Vincent P. A. Lonij, Ambrish Rawat, Maria-Irina Nicolae:
Extending Knowledge Bases Using Images. AKBC@NIPS 2017 - [c1]Valentina Zantedeschi, Maria-Irina Nicolae, Ambrish Rawat:
Efficient Defenses Against Adversarial Attacks. AISec@CCS 2017: 39-49 - [i3]Valentina Zantedeschi, Maria-Irina Nicolae, Ambrish Rawat:
Efficient Defenses Against Adversarial Attacks. CoRR abs/1707.06728 (2017) - [i2]Vincent P. A. Lonij, Ambrish Rawat, Maria-Irina Nicolae:
Open-World Visual Recognition Using Knowledge Graphs. CoRR abs/1708.08310 (2017) - [i1]Ambrish Rawat, Martin Wistuba, Maria-Irina Nicolae:
Adversarial Phenomenon in the Eyes of Bayesian Deep Learning. CoRR abs/1711.08244 (2017)
Coauthor Index
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