most citedExploiting Submodular Value Functions For Scaling Up Active Perception

22 citations · 29 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CR2025

Private, Auditable, and Distributed Ledger for Financial Institutes

Shaltiel Eloul, Yash Satsangi, Yeoh Wei Zhu +3

Distributed ledger technology offers several advantages for banking and finance industry, including efficient transaction processing and cross-party transaction reconciliation. The…

cs.LG20203 cited

Useful Policy Invariant Shaping from Arbitrary Advice

Paniz Behboudian, Yash Satsangi, Matthew E. Taylor +2

Reinforcement learning is a powerful learning paradigm in which agents can learn to maximize sparse and delayed reward signals. Although RL has had many impressive successes in com…

cs.CV2020

Real-Time Resource Allocation for Tracking Systems

Yash Satsangi, Shimon Whiteson, Frans A. Oliehoek +1

Automated tracking is key to many computer vision applications. However, many tracking systems struggle to perform in real-time due to the high computational cost of detecting peop…

cs.AI202022 cited

Exploiting Submodular Value Functions For Scaling Up Active Perception

Yash Satsangi, Shimon Whiteson, Frans A. Oliehoek +1

In active perception tasks, an agent aims to select sensory actions that reduce its uncertainty about one or more hidden variables. While partially observable Markov decision proce…

cs.AI20204 cited

Maximizing Information Gain in Partially Observable Environments via Prediction Reward

Yash Satsangi, Sungsu Lim, Shimon Whiteson +2

Information gathering in a partially observable environment can be formulated as a reinforcement learning (RL), problem where the reward depends on the agent's uncertainty. For exa…