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Yash Satsangi
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2020 – today
- 2024
- [j3]Kostis Gourgoulias, Najah Ghalyan, Maxime Labonne, Yash Satsangi, Sean Moran, Joseph Sabelja:
Estimating class separability of text embeddings with persistent homology. Trans. Mach. Learn. Res. 2024 (2024) - [c9]Georgios Papadopoulos, Yash Satsangi, Shaltiel Eloul, Marco Pistoia:
Absolute Variation Distance: An Inversion Attack Evaluation Metric for Federated Learning. ECIR (4) 2024: 243-256 - [c8]Georgios Papadopoulos, Yash Satsangi, Shaltiel Eloul, Marco Pistoia:
Ranking Distance Metric for Privacy Budget in Distributed Learning of Finite Embedding Data. ECIR (4) 2024: 257-269 - [c7]Agathe Lherondelle, Varun Babbar, Yash Satsangi, Fran Silavong, Shaltiel Eloul, Sean Moran:
Topical: Automatic Repository Tagging using Attention on Hybrid Code Embeddings. FinanSE@ICSE 2024: 23-30 - 2023
- [i9]Yash Satsangi, Paniz Behboudian:
Bandit-Based Policy Invariant Explicit Shaping for Incorporating External Advice in Reinforcement Learning. CoRR abs/2304.07163 (2023) - [i8]Najah Ghalyan, Kostis Gourgoulias, Yash Satsangi, Sean Moran, Maxime Labonne, Joseph Sabelja:
An Unsupervised Method for Estimating Class Separability of Datasets with Application to LLMs Fine-Tuning. CoRR abs/2305.15016 (2023) - 2022
- [j2]Paniz Behboudian, Yash Satsangi, Matthew E. Taylor, Anna Harutyunyan, Michael Bowling:
Policy invariant explicit shaping: an efficient alternative to reward shaping. Neural Comput. Appl. 34(3): 1673-1686 (2022) - [c6]Xiaoying Zhi, Yash Satsangi, Sean J. Moran, Shaltiel Eloul:
Ledgit: A Service to Diagnose Illicit Addresses on Blockchain using Multi-modal Unsupervised Learning. CIKM 2022: 5069-5073 - [i7]Agathe Lherondelle, Yash Satsangi, Fran Silavong, Shaltiel Eloul, Sean J. Moran:
Topical: Learning Repository Embeddings from Source Code using Attention. CoRR abs/2208.09495 (2022) - 2021
- [i6]Montaser Mohammedalamen, Dustin Morrill, Alexander Sieusahai, Yash Satsangi, Michael Bowling:
Learning to Be Cautious. CoRR abs/2110.15907 (2021) - 2020
- [c5]Yash Satsangi, Sungsu Lim, Shimon Whiteson, Frans A. Oliehoek, Martha White:
Maximizing Information Gain in Partially Observable Environments via Prediction Rewards. AAMAS 2020: 1215-1223 - [i5]Yash Satsangi, Sungsu Lim, Shimon Whiteson, Frans A. Oliehoek, Martha White:
Maximizing Information Gain in Partially Observable Environments via Prediction Reward. CoRR abs/2005.04912 (2020) - [i4]Yash Satsangi, Shimon Whiteson, Frans A. Oliehoek, Matthijs T. J. Spaan:
Exploiting Submodular Value Functions For Scaling Up Active Perception. CoRR abs/2009.09696 (2020) - [i3]Yash Satsangi, Shimon Whiteson, Frans A. Oliehoek, Henri Bouma:
Real-Time Resource Allocation for Tracking Systems. CoRR abs/2010.03024 (2020) - [i2]Paniz Behboudian, Yash Satsangi, Matthew E. Taylor, Anna Harutyunyan, Michael Bowling:
Useful Policy Invariant Shaping from Arbitrary Advice. CoRR abs/2011.01297 (2020)
2010 – 2019
- 2018
- [j1]Yash Satsangi, Shimon Whiteson, Frans A. Oliehoek, Matthijs T. J. Spaan:
Exploiting submodular value functions for scaling up active perception. Auton. Robots 42(2): 209-233 (2018) - 2017
- [c4]Yash Satsangi, Shimon Whiteson, Frans A. Oliehoek, Henri Bouma:
Real-Time Resource Allocation for Tracking Systems. UAI 2017 - 2016
- [c3]Yash Satsangi, Shimon Whiteson, Frans A. Oliehoek:
Probably Approximately Correct Greedy Maximization: (Extended Abstract). AAMAS 2016: 1387-1388 - [c2]Yash Satsangi, Shimon Whiteson, Frans A. Oliehoek:
PAC Greedy Maximization with Efficient Bounds on Information Gain for Sensor Selection. IJCAI 2016: 3220-3227 - [i1]Yash Satsangi, Shimon Whiteson, Frans A. Oliehoek:
Probably Approximately Correct Greedy Maximization. CoRR abs/1602.07860 (2016) - 2015
- [c1]Yash Satsangi, Shimon Whiteson, Frans A. Oliehoek:
Exploiting Submodular Value Functions for Faster Dynamic Sensor Selection. AAAI 2015: 3356-3363
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