Yash Satsangi

According to our database, Yash Satsangi authored at least 17 papers between 2015 and 2024.

Collaborative distances:

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

2024

Ranking Distance Metric for Privacy Budget in Distributed Learning of Finite Embedding Data.

Georgios Papadopoulos, Yash Satsangi, Shaltiel Eloul, Marco Pistoia

Proceedings of the Advances in Information Retrieval, 2024

Absolute Variation Distance: An Inversion Attack Evaluation Metric for Federated Learning.

Georgios Papadopoulos, Yash Satsangi, Shaltiel Eloul, Marco Pistoia

Proceedings of the Advances in Information Retrieval, 2024

2023

An Unsupervised Method for Estimating Class Separability of Datasets with Application to LLMs Fine-Tuning.

Najah Ghalyan, Kostis Gourgoulias, Yash Satsangi, Sean Moran, Maxime Labonne, Joseph Sabelja

CoRR, 2023

Bandit-Based Policy Invariant Explicit Shaping for Incorporating External Advice in Reinforcement Learning.

Yash Satsangi, Paniz Behboudian

CoRR, 2023

2022

Policy invariant explicit shaping: an efficient alternative to reward shaping.

Paniz Behboudian, Yash Satsangi, Matthew E. Taylor, Anna Harutyunyan, Michael Bowling

Neural Comput. Appl., 2022

Topical: Learning Repository Embeddings from Source Code using Attention.

Agathe Lherondelle, Yash Satsangi, Fran Silavong, Shaltiel Eloul, Sean J. Moran

CoRR, 2022

Ledgit: A Service to Diagnose Illicit Addresses on Blockchain using Multi-modal Unsupervised Learning.

Xiaoying Zhi, Yash Satsangi, Sean J. Moran, Shaltiel Eloul

Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022

2021

Learning to Be Cautious.

Montaser Mohammedalamen, Dustin Morrill, Alexander Sieusahai, Yash Satsangi, Michael Bowling

CoRR, 2021

2020

Useful Policy Invariant Shaping from Arbitrary Advice.

Paniz Behboudian, Yash Satsangi, Matthew E. Taylor, Anna Harutyunyan, Michael Bowling

CoRR, 2020

Maximizing Information Gain in Partially Observable Environments via Prediction Reward.

Yash Satsangi, Sungsu Lim, Shimon Whiteson, Frans A. Oliehoek, Martha White

CoRR, 2020

Maximizing Information Gain in Partially Observable Environments via Prediction Rewards.

Yash Satsangi, Sungsu Lim, Shimon Whiteson, Frans A. Oliehoek, Martha White

Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, 2020

2018

Exploiting submodular value functions for scaling up active perception.

Yash Satsangi, Shimon Whiteson, Frans A. Oliehoek, Matthijs T. J. Spaan

Auton. Robots, 2018

2017

Real-Time Resource Allocation for Tracking Systems.

Yash Satsangi, Shimon Whiteson, Frans A. Oliehoek, Henri Bouma

Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, 2017

2016

Probably Approximately Correct Greedy Maximization.

Yash Satsangi, Shimon Whiteson, Frans A. Oliehoek

CoRR, 2016

PAC Greedy Maximization with Efficient Bounds on Information Gain for Sensor Selection.

Yash Satsangi, Shimon Whiteson, Frans A. Oliehoek

Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, 2016

Probably Approximately Correct Greedy Maximization: (Extended Abstract).

Yash Satsangi, Shimon Whiteson, Frans A. Oliehoek

Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems, 2016

2015

Exploiting Submodular Value Functions for Faster Dynamic Sensor Selection.

Yash Satsangi, Shimon Whiteson, Frans A. Oliehoek

Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015