no code implementations • 24 May 2023 • Najah Ghalyan, Kostis Gourgoulias, Yash Satsangi, Sean Moran, Maxime Labonne, Joseph Sabelja
This paper proposes a method to estimate the class separability of an unlabeled text dataset by inspecting the topological characteristics of sentence-transformer embeddings of the text.
no code implementations • 14 Apr 2023 • Yash Satsangi, Paniz Behboudian
A key challenge for a reinforcement learning (RL) agent is to incorporate external/expert1 advice in its learning.
1 code implementation • 19 Aug 2022 • Agathe Lherondelle, Varun Babbar, Yash Satsangi, Fran Silavong, Shaltiel Eloul, Sean Moran
This paper presents Topical, a novel deep neural network for repository level embeddings.
no code implementations • 29 Oct 2021 • Montaser Mohammedalamen, Dustin Morrill, Alexander Sieusahai, Yash Satsangi, Michael Bowling
An agent that could learn to be cautious would overcome this challenge by discovering for itself when and how to behave cautiously.
no code implementations • 2 Nov 2020 • Paniz Behboudian, Yash Satsangi, Matthew E. Taylor, Anna Harutyunyan, Michael Bowling
Furthermore, if the reward is constructed from a potential function, the optimal policy is guaranteed to be unaltered.
no code implementations • 21 Sep 2020 • Yash Satsangi, Shimon Whiteson, Frans A. Oliehoek, Henri Bouma
Automated tracking is key to many computer vision applications.
no code implementations • 21 Sep 2020 • Yash Satsangi, Shimon Whiteson, Frans A. Oliehoek, Matthijs T. J. Spaan
Furthermore, we show that, under certain conditions, including submodularity, the value function computed using greedy PBVI is guaranteed to have bounded error with respect to the optimal value function.
no code implementations • 11 May 2020 • Yash Satsangi, Sungsu Lim, Shimon Whiteson, Frans Oliehoek, Martha White
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.
no code implementations • 25 Feb 2016 • Yash Satsangi, Shimon Whiteson, Frans A. Oliehoek
Submodular function maximization finds application in a variety of real-world decision-making problems.