Yash Satsangi | DeepAI

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∙ 05/24/2023

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

This paper proposes an unsupervised method that leverages topological ch...

0 Najah Ghalyan, et al. ∙

∙ 04/14/2023

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

A key challenge for a reinforcement learning (RL) agent is to incorporat...

0 Yash Satsangi, et al. ∙

∙ 08/19/2022

Topical: Learning Repository Embeddings from Source Code using Attention

Machine learning on source code (MLOnCode) promises to transform how sof...

8 Agathe Lherondelle, et al. ∙

∙ 10/29/2021

Learning to Be Cautious

A key challenge in the field of reinforcement learning is to develop age...

8 Montaser Mohammedalamen, et al. ∙

∙ 11/02/2020

Useful Policy Invariant Shaping from Arbitrary Advice

Reinforcement learning is a powerful learning paradigm in which agents c...

0 Paniz Behboudian, et al. ∙

∙ 09/21/2020

Exploiting Submodular Value Functions For Scaling Up Active Perception

In active perception tasks, an agent aims to select sensory actions that...

5 Yash Satsangi, et al. ∙

∙ 05/11/2020

Maximizing Information Gain in Partially Observable Environments via Prediction Reward

Information gathering in a partially observable environment can be formu...

32 Yash Satsangi, et al. ∙

∙ 02/25/2016

Probably Approximately Correct Greedy Maximization

Submodular function maximization finds application in a variety of real-...

0 Yash Satsangi, et al. ∙

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