Search Results for author: Keerthiram Murugesan

Found 19 papers, 2 papers with code

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 +1

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

no code implementations23 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 +1

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 text-based games

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

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.

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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