Extending Knowledge Bases Using Images. - Vincent P. A. Lonij; Ambrish Rawat; Maria-Irina Nicolae (AKBC@NIPS, 2017)

Efficient Defenses Against Adversarial Attacks. - Valentina Zantedeschi; Maria-Irina Nicolae; Ambrish Rawat (AISec@CCS, 2017)

Open-World Visual Recognition Using Knowledge Graphs. - Vincent P. A. Lonij; Ambrish Rawat; Maria-Irina Nicolae (CoRR, 2017)

Adversarial Phenomenon in the Eyes of Bayesian Deep Learning. - Ambrish Rawat; Martin Wistuba; Maria-Irina Nicolae (CoRR, 2017)

Non-parametric estimation of Jensen-Shannon Divergence in Generative Adversarial Network training. - Mathieu Sinn; Ambrish Rawat (AISTATS, 2018)

Scalable Multi-Class Bayesian Support Vector Machines for Structured and Unstructured Data. - Martin Wistuba; Ambrish Rawat (CoRR, 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 Large Margin Gaussian Process Classification. - Martin Wistuba; Ambrish Rawat (ECML/PKDD (2), 2019)

A Survey on Neural Architecture Search. - Martin Wistuba; Ambrish Rawat; Tejaswini Pedapati (CoRR, 2019)

How can AI Automate End-to-End Data Science? - Charu C. Aggarwal; Djallel Bouneffouf 001; Horst Samulowitz; Beat Buesser; Thanh Hoang; Udayan Khurana; Sijia Liu 001; Tejaswini Pedapati; Parikshit Ram; Ambrish Rawat; Martin Wistuba; Alexander G. Gray (CoRR, 2019)

Survey on Automated End-to-End Data Science? - Djallel Bouneffouf 001; Charu C. Aggarwal; Thanh Hoang; Udayan Khurana; Horst Samulowitz; Beat Buesser; Sijia Liu 001; Tejaswini Pedapati; Parikshit Ram; Ambrish Rawat; Martin Wistuba; Alexander G. Gray (IJCNN, 2020)

Automation of Deep Learning - Theory and Practice. - Martin Wistuba; Ambrish Rawat; Tejaswini Pedapati (ICMR, 2020)

IBM Federated Learning: an Enterprise Framework White Paper V0.1. - Heiko Ludwig; Nathalie Baracaldo; Gegi Thomas; Yi Zhou 015; Ali Anwar 001; Shashank Rajamoni; Yuya Jeremy Ong; Jayaram Radhakrishnan; Ashish Verma 001; Mathieu Sinn; Mark Purcell; Ambrish Rawat; et al. (CoRR, 2020)

FAT: Federated Adversarial Training. - Giulio Zizzo; Ambrish Rawat; Mathieu Sinn; Beat Buesser (CoRR, 2020)

Searching for Machine Learning Pipelines Using a Context-Free Grammar. - Radu Marinescu 002; Akihiro Kishimoto; Parikshit Ram; Ambrish Rawat; Martin Wistuba; Paulito P. Palmes; Adi Botea (AAAI, 2021)

The Devil is in the GAN: Defending Deep Generative Models Against Backdoor Attacks. - Ambrish Rawat; Killian Levacher; Mathieu Sinn (CoRR, 2021)

Automated Robustness with Adversarial Training as a Post-Processing Step. - Ambrish Rawat; Mathieu Sinn; Beat Buesser (CoRR, 2021)

Certified Federated Adversarial Training. - Giulio Zizzo; Ambrish Rawat; Mathieu Sinn; Sergio Maffeis; Chris Hankin (CoRR, 2021)

Security and Robustness in Federated Learning. - Ambrish Rawat; Giulio Zizzo; Muhammad Zaid Hameed; Luis Muñoz-González (Federated Learning, 2022)

Bandit Limited Discrepancy Search and Application to Machine Learning Pipeline Optimization. - Akihiro Kishimoto; Djallel Bouneffouf 001; Radu Marinescu 002; Parikshit Ram; Ambrish Rawat; Martin Wistuba; Paulito P. Palmes; Adi Botea (AAAI, 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 001; Nguyen Ho; Carlos E. Muñiz-Cuza; Jonas Brusokas; Torben Bach Pedersen; Giorgos Alexiou; et al. (IEEE Big Data, 2022)

Robust Learning Protocol for Federated Tumor Segmentation Challenge. - Ambrish Rawat; Giulio Zizzo; Swanand Kadhe; Jonathan P. Epperlein; Stefano Braghin (BrainLes@MICCAI (2), 2022)

The Devil Is in the GAN: Backdoor Attacks and Defenses in Deep Generative Models. - Ambrish Rawat; Killian Levacher; Mathieu Sinn (ESORICS (3), 2022)

Towards an Accountable and Reproducible Federated Learning: A FactSheets Approach. - Nathalie Baracaldo; Ali Anwar 001; Mark Purcell; Ambrish Rawat; Mathieu Sinn; Bashar Altakrouri; Dian Balta; Mahdi Sellami; Peter Kuhn; Ulrich Schöpp; Matthias Buchinger (CoRR, 2022)

Challenges and Pitfalls of Bayesian Unlearning. - Ambrish Rawat; James Requeima; Wessel P. Bruinsma; Richard E. Turner (CoRR, 2022)

Federated Unlearning: How to Efficiently Erase a Client in FL? - Anisa Halimi; Swanand Kadhe; Ambrish Rawat; Nathalie Baracaldo (CoRR, 2022)

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 (ACL (1), 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 (IEEE Big Data, 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 015; Keith Houck; Ambrish Rawat; Mark Purcell; Naoise Holohan; Mikio Takeuchi; Ryo Kawahara; Nir Drucker; et al. (CoRR, 2023)

FairSISA: Ensemble Post-Processing to Improve Fairness of Unlearning in LLMs. - Swanand Ravindra Kadhe; Anisa Halimi; Ambrish Rawat; Nathalie Baracaldo (CoRR, 2023)

MoJE: Mixture of Jailbreak Experts, Naive Tabular Classifiers as Guard for Prompt Attacks. - Giandomenico Cornacchia; Giulio Zizzo; Kieran Fraser; Muhammad Zaid Hameed; Ambrish Rawat; Mark Purcell (AIES (1), 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)

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 001; Subhajit Chaudhury; Pin-Yu Chen; Lamogha Chiazor; Elizabeth M. Daly; Rogério Abreu de Paula; et al. (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; et al. (CoRR, 2024)

Granite Guardian. - Inkit Padhi; Manish Nagireddy; Giandomenico Cornacchia; Subhajit Chaudhury; Tejaswini Pedapati; Pierre L. Dognin; Keerthiram Murugesan; Erik Miehling; Martin Santillan Cooper; Kieran Fraser; Giulio Zizzo; Muhammad Zaid Hameed; et al. (CoRR, 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 (NAACL (Findings), 2025)

Granite Guardian: Comprehensive LLM Safeguarding. - Inkit Padhi; Manish Nagireddy; Giandomenico Cornacchia; Subhajit Chaudhury; Tejaswini Pedapati; Pierre L. Dognin; Keerthiram Murugesan; Erik Miehling; Martín Santillán Cooper; Kieran Fraser; Giulio Zizzo; Muhammad Zaid Hameed; et al. (NAACL (Industry Track), 2025)

Adversarial Prompt Evaluation: Systematic Benchmarking of Guardrails Against Prompt Input Attacks on LLMs. - Giulio Zizzo; Giandomenico Cornacchia; Kieran Fraser; Muhammad Zaid Hameed; Ambrish Rawat; Beat Buesser; Mark Purcell; Pin-Yu Chen; Prasanna Sattigeri; Kush R. Varshney (CoRR, 2025)

Agentic AI Needs a Systems Theory. - Erik Miehling; Karthikeyan Natesan Ramamurthy; Kush R. Varshney; Matthew Riemer; Djallel Bouneffouf 001; John T. Richards; Amit Dhurandhar; Elizabeth M. Daly; Michael Hind; Prasanna Sattigeri; Dennis Wei; Ambrish Rawat; et al. (CoRR, 2025)

AI Risk Atlas: Taxonomy and Tooling for Navigating AI Risks and Resources. - Frank Bagehorn; Kristina Brimijoin; Elizabeth M. Daly; Jessica He; Michael Hind; Luis Garcés-Erice; Christopher Giblin; Ioana Giurgiu; Jacquelyn Martino; Rahul Nair; David Piorkowski; Ambrish Rawat; et al. (CoRR, 2025)

MAD-MAX: Modular And Diverse Malicious Attack MiXtures for Automated LLM Red Teaming. - Stefan Schoepf; Muhammad Zaid Hameed; Ambrish Rawat; Kieran Fraser; Giulio Zizzo; Giandomenico Cornacchia; Mark Purcell (CoRR, 2025)

Activated LoRA: Fine-tuned LLMs for Intrinsics. - Kristjan Greenewald; Luis A. Lastras; Thomas Parnell; Vraj Shah; Lucian Popa 001; Giulio Zizzo; R. Chulaka Gunasekara; Ambrish Rawat; David Cox (CoRR, 2025)