Publications

  • Applications of certified randomness, Omar Amer, Shouvanik Chakrabarti, Kaushik Chakraborty, Shaltiel Eloul, Niraj Kumar, Charles Lim, Minzhao Liu, Pradeep Niroula, Yash Satsangi, Ruslan Shaydulin, Marco Pistoia, Nature Physics Reviews, 2025, arxiv
  • A Numerical Gradient Inversion Attack in Variational Quantum Neural-Networks Georgios Papadopoulos, Shaltiel Eloul, Yash Satsangi, Jamie Heredge, Niraj Kumar, Chun-Fu Chen, Marco Pistoia, arxiv
  • Private, Auditable, and Distributed Ledger for Financial Institutes, Shaltiel Eloul, Yash Satsangi, Yeoh Wei Zhu, Omar Amer, Georgios Papadopoulos, Marco Pistoia, ZKProof 7, 2025, pdf
  • Absolute Variation Distance: An Inversion Attack Evaluation Metric for Federated Learning, Georgios Papadopoulos, Yash Satsangi, Shaltiel Eloul, Marco Pistoia, European Conference on Information Retrieval, 2024 pdf
  • Ranking Distance Metric for Privacy Budget in Distributed Learning of Finite Embedding Data, Georgios Papadopoulos, Yash Satsangi, Shaltiel Eloul, Marco Pistoia, European Conference on Information Retrieval, 2024 pdf
  • Estimating class separability of text embeddings with persistent homology, Kostis Gourgoulias, Najah Ghalyan, Maxime Labonne, Yash Satsangi, Sean Moran, Joseph Sabelja, Transactions on Machine Learning Research, 2024 pdf
  • Bandit-Based Policy Invariant Explicit Shaping for Incorporating External Advice in Reinforcement Learning, Yash Satsangi, Paniz Behboudian AAMAS-ALA Workshop, 2023 (pdf)
  • Learning to be Cautious, Montaser Mohammedalamen, Dustin Morrill, Alexander Sieusahai, Yash Satsangi, Michael Bowling, AAMAS-ALA Workshop 2023 ArXiv
  • Ledgit: A service to Diagnose Illicit Addresses on Blockchain using Multi-modal Unsupervised Learning, Xiaoying Zhi, Yash Satsangi, Sean Moran, Shaltiel Eloul, Conference on Information and Knowledge Management (CIKM), 2022 (pdf)
  • Topical: Learning Repository Embeddings from Source Code using Attention, Agathe Lherondelle, Yash Satsangi, Fran Silavong, Shaltiel Eloul, Sean Moran, 2022 (pdf)
  • Useful Policy invariant explicit shaping: an efficient alternative to reward shaping, Paniz Behboudian, Yash Satsangi, Matthew E. Taylor, Anna Harutyunyan, Michael Bowling, Neural Computing and Applications, 2022 (pdf)
  • Useful Policy Invariant Shaping from Arbitrary Advice, Paniz Behboudian, Yash Satsangi, Matthew E. Taylor, Anna Harutyunyan, Michael Bowling, AAMAS-ALA workshop, 2020 (pdf)
  • Maximizing Information Gain in Partially Observable Environments via Prediction Rewards, Yash Satsangi, Sungsu Lim, Shimon Whiteson, Frans A. Oliehoek, Martha White, AAMAS 2020 (pdf)
  • Active Perception for Person Tracking, Ph.D. thesis, University of Amsterdam, 2019 (pdf)
  • Exploiting Submodular Value Functions for Scaling Up Active Perception, Yash Satsangi, Shimon Whiteson, Frans A. Oliehoek, Matthijs Spaan, Autonomous Robots, 2018 (pdf)
  • Real-Time Resource Allocation for Tracking Systems, Yash Satsangi, Shimon Whiteson, Frans A. Oliehoek, Henri Bouma, UAI 2017 (pdf)
  • PAC Greedy Maximization with Efficient Bounds on Information Gain for Sensor Selection, Yash Satsangi, Shimon Whiteson and Frans A. Oliehoek, IJCAI 2016 (pdf)
  • Probably Approximately Correct Greedy Maximization (Extended Abstract), Yash Satsangi, Shimon Whiteson and Frans A. Oliehoek, AAMAS 2016 (pdf)
  • Exploiting Submodular Value Functions for Faster Dynamic Sensor Selection, Yash Satsangi, Shimon Whiteson, and Frans A. Oliehoek, AAAI 2015 (pdf)
  • An analysis of Piecewise-Linear and Convex Value Function for Active Perception POMDPs, Yash Satsangi, Shimon Whiteson, and Matthijs T. J. Spaan, IAS Technical Report, University of Amsterdam (pdf)
  • Exploiting Submodular Value Functions for Faster Dynamic Sensor Selection:Extended Version, Yash Satsangi, Shimon Whiteson, and Frans A. Oliehoek, IAS Technical Report, University of Amsterdam (pdf)