Search Results for author: Keerthiram Murugesan

Found 34 papers, 8 papers with code

CTBench: A Comprehensive Benchmark for Evaluating Language Model Capabilities in Clinical Trial Design

1 code implementation25 Jun 2024 Nafis Neehal, Bowen Wang, Shayom Debopadhaya, Soham Dan, Keerthiram Murugesan, Vibha Anand, Kristin P. Bennett

Given study-specific metadata, CTBench evaluates AI models' ability to determine the baseline features of a clinical trial (CT), which include demographic and relevant features collected at the trial's start from all participants.

Language Modelling Prompt Engineering

STARLING: Self-supervised Training of Text-based Reinforcement Learning Agent with Large Language Models

1 code implementation9 Jun 2024 Shreyas Basavatia, Keerthiram Murugesan, Shivam Ratnakar

In this work, we introduce an interactive environment for self-supervised RL, STARLING, for text-based games that bootstraps the text-based RL agents with automatically generated games (based on the seed set of game ideas) to boost the performance and generalization capabilities to reach a goal of the target environment.

Reinforcement Learning (RL) text-based games

Facilitating Human-LLM Collaboration through Factuality Scores and Source Attributions

no code implementations30 May 2024 Hyo Jin Do, Rachel Ostrand, Justin D. Weisz, Casey Dugan, Prasanna Sattigeri, Dennis Wei, Keerthiram Murugesan, Werner Geyer

To address this issue, we conducted a scenario-based study (N=104) to systematically compare the impact of various design strategies for communicating factuality and source attribution on participants' ratings of trust, preferences, and ease in validating response accuracy.

SF-DQN: Provable Knowledge Transfer using Successor Feature for Deep Reinforcement Learning

no code implementations24 May 2024 Shuai Zhang, Heshan Devaka Fernando, Miao Liu, Keerthiram Murugesan, Songtao Lu, Pin-Yu Chen, Tianyi Chen, Meng Wang

This paper studies the transfer reinforcement learning (RL) problem where multiple RL problems have different reward functions but share the same underlying transition dynamics.

Q-Learning Reinforcement Learning (RL) +1

On the Effects of Fine-tuning Language Models for Text-Based Reinforcement Learning

no code implementations15 Apr 2024 Mauricio Gruppi, Soham Dan, Keerthiram Murugesan, Subhajit Chaudhury

Moreover, we describe the occurrence of semantic degeneration as a consequence of inappropriate fine-tuning of language models in text-based reinforcement learning (TBRL).

reinforcement-learning

Language Guided Exploration for RL Agents in Text Environments

no code implementations5 Mar 2024 Hitesh Golchha, Sahil Yerawar, Dhruvesh Patel, Soham Dan, Keerthiram Murugesan

Real-world sequential decision making is characterized by sparse rewards and large decision spaces, posing significant difficulty for experiential learning systems like $\textit{tabula rasa}$ reinforcement learning (RL) agents.

Decision Making Language Modelling +2

On the Convergence and Sample Complexity Analysis of Deep Q-Networks with $ε$-Greedy Exploration

no code implementations24 Oct 2023 Shuai Zhang, Hongkang Li, Meng Wang, Miao Liu, Pin-Yu Chen, Songtao Lu, Sijia Liu, Keerthiram Murugesan, Subhajit Chaudhury

This paper provides the first theoretical convergence and sample complexity analysis of the practical setting of DQNs with $\epsilon$-greedy policy.

Q-Learning

Value-based Fast and Slow AI Nudging

no code implementations14 Jul 2023 Marianna B. Ganapini, Francesco Fabiano, Lior Horesh, Andrea Loreggia, Nicholas Mattei, Keerthiram Murugesan, Vishal Pallagani, Francesca Rossi, Biplav Srivastava, Brent Venable

Values that are relevant to a specific decision scenario are used to decide when and how to use each of these nudging modalities.

Learning Symbolic Rules over Abstract Meaning Representations for Textual Reinforcement Learning

1 code implementation5 Jul 2023 Subhajit Chaudhury, Sarathkrishna Swaminathan, Daiki Kimura, Prithviraj Sen, Keerthiram Murugesan, Rosario Uceda-Sosa, Michiaki Tatsubori, Achille Fokoue, Pavan Kapanipathi, Asim Munawar, Alexander Gray

Text-based reinforcement learning agents have predominantly been neural network-based models with embeddings-based representation, learning uninterpretable policies that often do not generalize well to unseen games.

reinforcement-learning Representation Learning

Plansformer: Generating Symbolic Plans using Transformers

no code implementations16 Dec 2022 Vishal Pallagani, Bharath Muppasani, Keerthiram Murugesan, Francesca Rossi, Lior Horesh, Biplav Srivastava, Francesco Fabiano, Andrea Loreggia

Large Language Models (LLMs) have been the subject of active research, significantly advancing the field of Natural Language Processing (NLP).

Question Answering Text Generation +2

Mitigating Gradient Bias in Multi-objective Learning: A Provably Convergent Stochastic Approach

1 code implementation23 Oct 2022 Heshan Fernando, Han Shen, Miao Liu, Subhajit Chaudhury, Keerthiram Murugesan, Tianyi Chen

Machine learning problems with multiple objective functions appear either in learning with multiple criteria where learning has to make a trade-off between multiple performance metrics such as fairness, safety and accuracy; or, in multi-task learning where multiple tasks are optimized jointly, sharing inductive bias between them.

Fairness Inductive Bias +1

Targeted Advertising on Social Networks Using Online Variational Tensor Regression

no code implementations22 Aug 2022 Tsuyoshi Idé, Keerthiram Murugesan, Djallel Bouneffouf, Naoki Abe

The proposed framework is designed to accommodate any number of feature vectors in the form of multi-mode tensor, thereby enabling to capture the heterogeneity that may exist over user preferences, products, and campaign strategies in a unified manner.

Marketing regression

Auto-Transfer: Learning to Route Transferrable Representations

1 code implementation2 Feb 2022 Keerthiram Murugesan, Vijay Sadashivaiah, Ronny Luss, Karthikeyan Shanmugam, Pin-Yu Chen, Amit Dhurandhar

Knowledge transfer between heterogeneous source and target networks and tasks has received a lot of attention in recent times as large amounts of quality labeled data can be difficult to obtain in many applications.

Transfer Learning

Case-based Reasoning for Better Generalization in Textual Reinforcement Learning

no code implementations ICLR 2022 Mattia Atzeni, Shehzaad Dhuliawala, Keerthiram Murugesan, Mrinmaya Sachan

Text-based games (TBG) have emerged as promising environments for driving research in grounded language understanding and studying problems like generalization and sample efficiency.

Out-of-Distribution Generalization reinforcement-learning +2

Auto-Transfer: Learning to Route Transferable Representations

no code implementations ICLR 2022 Keerthiram Murugesan, Vijay Sadashivaiah, Ronny Luss, Karthikeyan Shanmugam, Pin-Yu Chen, Amit Dhurandhar

Knowledge transfer between heterogeneous source and target networks and tasks has received a lot of attention in recent times as large amounts of quality labelled data can be difficult to obtain in many applications.

Transfer Learning

Eye of the Beholder: Improved Relation Generalization for Text-based Reinforcement Learning Agents

no code implementations9 Jun 2021 Keerthiram Murugesan, Subhajit Chaudhury, Kartik Talamadupula

This improves the agent's overall understanding of the game 'scene' and objects' relationships to the world around them, and the variety of visual representations on offer allow the agent to generate a better generalization of a relationship.

reinforcement-learning Reinforcement Learning (RL) +2

Text-based RL Agents with Commonsense Knowledge: New Challenges, Environments and Approaches

no code implementations12 Jul 2020 Keerthiram Murugesan, Mattia Atzeni, Pavan Kapanipathi, Pushkar Shukla, Sadhana Kumaravel, Gerald Tesauro, Kartik Talamadupula, Mrinmaya Sachan, Murray Campbell

We introduce a number of RL agents that combine the sequential context with a dynamic graph representation of their beliefs of the world and commonsense knowledge from ConceptNet in different ways.

Decision Making Reinforcement Learning (RL) +1

Enhancing Text-based Reinforcement Learning Agents with Commonsense Knowledge

no code implementations2 May 2020 Keerthiram Murugesan, Mattia Atzeni, Pushkar Shukla, Mrinmaya Sachan, Pavan Kapanipathi, Kartik Talamadupula

In this paper, we consider the recent trend of evaluating progress on reinforcement learning technology by using text-based environments and games as evaluation environments.

reinforcement-learning Reinforcement Learning (RL)

Lifelong Learning with Output Kernels

no code implementations ICLR 2018 Keerthiram Murugesan, Jaime Carbonell

Lifelong learning poses considerable challenges in terms of effectiveness (minimizing prediction errors for all tasks) and overall computational tractability for real-time performance.

Active Learning from Peers

no code implementations NeurIPS 2017 Keerthiram Murugesan, Jaime Carbonell

This paper addresses the challenge of learning from peers in an online multitask setting.

Active Learning

Co-Clustering for Multitask Learning

no code implementations3 Mar 2017 Keerthiram Murugesan, Jaime Carbonell, Yiming Yang

This paper presents a new multitask learning framework that learns a shared representation among the tasks, incorporating both task and feature clusters.

Clustering

Self-Paced Multitask Learning with Shared Knowledge

no code implementations2 Mar 2017 Keerthiram Murugesan, Jaime Carbonell

This paper introduces self-paced task selection to multitask learning, where instances from more closely related tasks are selected in a progression of easier-to-harder tasks, to emulate an effective human education strategy, but applied to multitask machine learning.

Adaptive Smoothed Online Multi-Task Learning

no code implementations NeurIPS 2016 Keerthiram Murugesan, Hanxiao Liu, Jaime Carbonell, Yiming Yang

This paper addresses the challenge of jointly learning both the per-task model parameters and the inter-task relationships in a multi-task online learning setting.

Multi-Task Learning

Multi-Task Multiple Kernel Relationship Learning

no code implementations10 Nov 2016 Keerthiram Murugesan, Jaime Carbonell

The problem is formulated as a regularization-based approach called \textit{Multi-Task Multiple Kernel Relationship Learning} (\textit{MK-MTRL}), which models the task relationship matrix from the weights learned from latent feature spaces of task-specific base kernels.

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