Ambrish Rawat

According to our database1, Ambrish Rawat authored at least 32 papers between 2017 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.



In proceedings 
PhD thesis 




Detectors for Safe and Reliable LLMs: Implementations, Uses, and Limitations.
CoRR, 2024

Domain Adaptation for Time series Transformers using One-step fine-tuning.
CoRR, 2024

FairSISA: Ensemble Post-Processing to Improve Fairness of Unlearning in LLMs.
CoRR, 2023

Privacy-Preserving Federated Learning over Vertically and Horizontally Partitioned Data for Financial Anomaly Detection.
CoRR, 2023

Pruning Federated Learning Models for Anomaly Detection in Resource-Constrained Environments.
Proceedings of the IEEE International Conference on Big Data, 2023

Matching Pairs: Attributing Fine-Tuned Models to their Pre-Trained Large Language Models.
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2023

Federated Unlearning: How to Efficiently Erase a Client in FL?
CoRR, 2022

Challenges and Pitfalls of Bayesian Unlearning.
CoRR, 2022

Towards an Accountable and Reproducible Federated Learning: A FactSheets Approach.
CoRR, 2022

The Devil Is in the GAN: Backdoor Attacks and Defenses in Deep Generative Models.
Proceedings of the Computer Security - ESORICS 2022, 2022

Robust Learning Protocol for Federated Tumor Segmentation Challenge.
Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2022

Bandit Limited Discrepancy Search and Application to Machine Learning Pipeline Optimization.
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

Security and Robustness in Federated Learning.
Proceedings of the Federated Learning, 2022

Certified Federated Adversarial Training.
CoRR, 2021

Automated Robustness with Adversarial Training as a Post-Processing Step.
CoRR, 2021

The Devil is in the GAN: Defending Deep Generative Models Against Backdoor Attacks.
CoRR, 2021

Searching for Machine Learning Pipelines Using a Context-Free Grammar.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

FAT: Federated Adversarial Training.
CoRR, 2020

IBM Federated Learning: an Enterprise Framework White Paper V0.1.
CoRR, 2020

Automation of Deep Learning - Theory and Practice.
Proceedings of the 2020 on International Conference on Multimedia Retrieval, 2020

Survey on Automated End-to-End Data Science?
Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

How can AI Automate End-to-End Data Science?
CoRR, 2019

A Survey on Neural Architecture Search.
CoRR, 2019

Scalable Large Margin Gaussian Process Classification.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2019

Adversarial Robustness Toolbox v0.2.2.
CoRR, 2018

Scalable Multi-Class Bayesian Support Vector Machines for Structured and Unstructured Data.
CoRR, 2018

Non-parametric estimation of Jensen-Shannon Divergence in Generative Adversarial Network training.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

Adversarial Phenomenon in the Eyes of Bayesian Deep Learning.
CoRR, 2017

Open-World Visual Recognition Using Knowledge Graphs.
CoRR, 2017

Efficient Defenses Against Adversarial Attacks.
Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security, 2017

Extending Knowledge Bases Using Images.
Proceedings of the 6th Workshop on Automated Knowledge Base Construction, 2017