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ORCIDKeerthiram Murugesan
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2020 – today
- 2024
[j2]Vijay Sadashivaiah, Keerthiram Murugesan, Ronny Luss, Pin-Yu Chen, Chris R. Sims, James A. Hendler, Amit Dhurandhar:
To Transfer or Not to Transfer: Suppressing Concepts from Source Representations. Trans. Mach. Learn. Res. 2024 (2024)- [c28]Shreyas Basavatia, Keerthiram Murugesan, Shivam Ratnakar:
STARLING: Self-supervised Training of Text-based Reinforcement Learning Agent with Large Language Models. ACL (Findings) 2024: 15804-15819 - [c27]Kinjal Basu, Keerthiram Murugesan, Subhajit Chaudhury, Murray Campbell, Kartik Talamadupula, Tim Klinger:
EXPLORER: Exploration-guided Reasoning for Textual Reinforcement Learning. EACL (1) 2024: 394-405 - [c26]Saüc Abadal Lloret, Shehzaad Dhuliawala, Keerthiram Murugesan, Mrinmaya Sachan:
Towards Aligning Language Models with Textual Feedback. EMNLP 2024: 20240-20266 - [c25]Vishal Pallagani, Bharath C. Muppasani, Kaushik Roy, Francesco Fabiano, Andrea Loreggia, Keerthiram Murugesan, Biplav Srivastava, Francesca Rossi, Lior Horesh, Amit P. Sheth:
On the Prospects of Incorporating Large Language Models (LLMs) in Automated Planning and Scheduling (APS). ICAPS 2024: 432-444 - [c24]Heshan Devaka Fernando, Lisha Chen, Songtao Lu, Pin-Yu Chen, Miao Liu, Subhajit Chaudhury, Keerthiram Murugesan, Gaowen Liu, Meng Wang, Tianyi Chen:
Variance Reduction Can Improve Trade-Off in Multi-Objective Learning. ICASSP 2024: 6975-6979 - [c23]Subhajit Chaudhury, Keerthiram Murugesan, Thomas Carta, Kartik Talamadupula, Michiaki Tatsubori:
Leveraging Visual Handicaps for Text-Based Reinforcement Learning. ICASSP 2024: 12376-12380 - [c22]Shuai Zhang, Heshan Devaka Fernando, Miao Liu, Keerthiram Murugesan, Songtao Lu, Pin-Yu Chen, Tianyi Chen, Meng Wang:
SF-DQN: Provable Knowledge Transfer using Successor Feature for Deep Reinforcement Learning. ICML 2024 - [c21]Hitesh Golchha, Sahil Yerawar, Dhruvesh Patel, Soham Dan, Keerthiram Murugesan:
Language Guided Exploration for RL Agents in Text Environments. NAACL-HLT (Findings) 2024: 93-102 - [i29]Vishal Pallagani, Kaushik Roy, Bharath Muppasani, Francesco Fabiano
, Andrea Loreggia, Keerthiram Murugesan, Biplav Srivastava, Francesca Rossi, Lior Horesh, Amit P. Sheth:
On the Prospects of Incorporating Large Language Models (LLMs) in Automated Planning and Scheduling (APS). CoRR abs/2401.02500 (2024) - [i28]Hitesh Golchha, Sahil Yerawar, Dhruvesh Patel, Soham Dan, Keerthiram Murugesan:
Language Guided Exploration for RL Agents in Text Environments. CoRR abs/2403.03141 (2024) - [i27]Swapnaja Achintalwar, Adriana Alvarado Garcia, Ateret Anaby-Tavor, Ioana Baldini, Sara E. Berger, Bishwaranjan Bhattacharjee, Djallel Bouneffouf, Subhajit Chaudhury, Pin-Yu Chen, Lamogha Chiazor, Elizabeth M. Daly, Rogério Abreu de Paula, Pierre L. Dognin, Eitan Farchi, Soumya Ghosh, Michael Hind, Raya Horesh, George Kour, Ja Young Lee, Erik Miehling, Keerthiram Murugesan, Manish Nagireddy, Inkit Padhi, David Piorkowski, Ambrish Rawat, Orna Raz, Prasanna Sattigeri, Hendrik Strobelt, Sarathkrishna Swaminathan, Christoph Tillmann, Aashka Trivedi, Kush R. Varshney, Dennis Wei, Shalisha Witherspoon, Marcel Zalmanovici:
Detectors for Safe and Reliable LLMs: Implementations, Uses, and Limitations. CoRR abs/2403.06009 (2024) - [i26]Kinjal Basu, Keerthiram Murugesan, Subhajit Chaudhury, Murray Campbell, Kartik Talamadupula, Tim Klinger:
EXPLORER: Exploration-guided Reasoning for Textual Reinforcement Learning. CoRR abs/2403.10692 (2024) - [i25]Maurício Gruppi, Soham Dan, Keerthiram Murugesan, Subhajit Chaudhury:
On the Effects of Fine-tuning Language Models for Text-Based Reinforcement Learning. CoRR abs/2404.10174 (2024) - [i24]Shuai Zhang, Heshan Devaka Fernando, Miao Liu, Keerthiram Murugesan, Songtao Lu, Pin-Yu Chen, Tianyi Chen, Meng Wang:
SF-DQN: Provable Knowledge Transfer using Successor Feature for Deep Reinforcement Learning. CoRR abs/2405.15920 (2024) - [i23]Hyo Jin Do, Rachel Ostrand, Justin D. Weisz, Casey Dugan, Prasanna Sattigeri, Dennis Wei, Keerthiram Murugesan, Werner Geyer:
Facilitating Human-LLM Collaboration through Factuality Scores and Source Attributions. CoRR abs/2405.20434 (2024) - [i22]Shreyas Basavatia, Keerthiram Murugesan, Shivam Ratnakar:
STARLING: Self-supervised Training of Text-based Reinforcement Learning Agent with Large Language Models. CoRR abs/2406.05872 (2024) - [i21]Nafis Neehal, Bowen Wang, Shayom Debopadhaya, Soham Dan, Keerthiram Murugesan, Vibha Anand, Kristin P. Bennett:
CTBench: A Comprehensive Benchmark for Evaluating Language Model Capabilities in Clinical Trial Design. CoRR abs/2406.17888 (2024) - [i20]Saüc Abadal Lloret, Shehzaad Dhuliawala, Keerthiram Murugesan, Mrinmaya Sachan:
Towards Aligning Language Models with Textual Feedback. CoRR abs/2407.16970 (2024) - [i19]Arpan Mukherjee, Shashanka Ubaru, Keerthiram Murugesan, Karthikeyan Shanmugam, Ali Tajer:
Combinatorial Multi-armed Bandits: Arm Selection via Group Testing. CoRR abs/2410.10679 (2024) - 2023
- [c20]Keerthiram Murugesan, Sarathkrishna Swaminathan, Soham Dan, Subhajit Chaudhury, R. Chulaka Gunasekara, Maxwell Crouse, Diwakar Mahajan, Ibrahim Abdelaziz, Achille Fokoue, Pavan Kapanipathi, Salim Roukos, Alexander Gray:
MISMATCH: Fine-grained Evaluation of Machine-generated Text with Mismatch Error Types. ACL (Findings) 2023: 4485-4503 - [c19]Subhajit Chaudhury, Sarathkrishna Swaminathan, Daiki Kimura, Prithviraj Sen, Keerthiram Murugesan, Rosario Uceda-Sosa, Michiaki Tatsubori, Achille Fokoue, Pavan Kapanipathi, Asim Munawar, Alexander Gray:
Learning Symbolic Rules over Abstract Meaning Representations for Textual Reinforcement Learning. ACL (1) 2023: 6764-6776 - [c18]Marianna Bergamaschi Ganapini, Francesco Fabiano, Lior Horesh, Andrea Loreggia, Nicholas Mattei, Keerthiram Murugesan, Vishal Pallagani, Francesca Rossi, Biplav Srivastava, Kristen Brent Venable:
Value-based Fast and Slow AI Nudging. ETHAICS@IJCAI 2023 - [c17]Heshan Devaka Fernando, Han Shen, Miao Liu, Subhajit Chaudhury, Keerthiram Murugesan, Tianyi Chen:
Mitigating Gradient Bias in Multi-objective Learning: A Provably Convergent Approach. ICLR 2023 - [c16]Debarun Bhattacharjya, Oktie Hassanzadeh, Ronny Luss, Keerthiram Murugesan:
Probabilistic Rule Induction from Event Sequences with Logical Summary Markov Models. IJCAI 2023: 5667-5675 - [c15]Vishal Pallagani, Bharath Muppasani, Biplav Srivastava, Francesca Rossi, Lior Horesh, Keerthiram Murugesan, Andrea Loreggia, Francesco Fabiano, Rony Joseph, Yathin Kethepalli:
Plansformer Tool: Demonstrating Generation of Symbolic Plans Using Transformers. IJCAI 2023: 7158-7162 - [c14]Shuai Zhang, Hongkang Li, Meng Wang, Miao Liu, Pin-Yu Chen, Songtao Lu, Sijia Liu, Keerthiram Murugesan, Subhajit Chaudhury:
On the Convergence and Sample Complexity Analysis of Deep Q-Networks with ε-Greedy Exploration. NeurIPS 2023 - [i18]Francesco Fabiano, Vishal Pallagani, Marianna Bergamaschi Ganapini, Lior Horesh, Andrea Loreggia, Keerthiram Murugesan, Francesca Rossi, Biplav Srivastava:
Fast and Slow Planning. CoRR abs/2303.04283 (2023) - [i17]Vishal Pallagani, Bharath Muppasani, Keerthiram Murugesan, Francesca Rossi, Biplav Srivastava, Lior Horesh, Francesco Fabiano, Andrea Loreggia:
Understanding the Capabilities of Large Language Models for Automated Planning. CoRR abs/2305.16151 (2023) - [i16]Keerthiram Murugesan, Sarathkrishna Swaminathan, Soham Dan, Subhajit Chaudhury, R. Chulaka Gunasekara, Maxwell Crouse, Diwakar Mahajan, Ibrahim Abdelaziz, Achille Fokoue, Pavan Kapanipathi, Salim Roukos, Alexander Gray:
MISMATCH: Fine-grained Evaluation of Machine-generated Text with Mismatch Error Types. CoRR abs/2306.10452 (2023) - [i15]Subhajit Chaudhury, Sarathkrishna Swaminathan, Daiki Kimura, Prithviraj Sen, Keerthiram Murugesan, Rosario Uceda-Sosa, Michiaki Tatsubori, Achille Fokoue, Pavan Kapanipathi, Asim Munawar, Alexander Gray:
Learning Symbolic Rules over Abstract Meaning Representations for Textual Reinforcement Learning. CoRR abs/2307.02689 (2023) - [i14]Marianna Bergamaschi Ganapini, Francesco Fabiano, Lior Horesh, Andrea Loreggia, Nicholas Mattei, Keerthiram Murugesan, Vishal Pallagani, Francesca Rossi, Biplav Srivastava, Kristen Brent Venable:
Value-based Fast and Slow AI Nudging. CoRR abs/2307.07628 (2023) - [i13]Shuai Zhang, Hongkang Li, Meng Wang, Miao Liu, Pin-Yu Chen, Songtao Lu, Sijia Liu, Keerthiram Murugesan, Subhajit Chaudhury:
On the Convergence and Sample Complexity Analysis of Deep Q-Networks with ε-Greedy Exploration. CoRR abs/2310.16173 (2023) - 2022
- [c13]Keerthiram Murugesan, Subhajit Chaudhury, Kartik Talamadupula:
Eye of the Beholder: Improved Relation Generalization for Text-Based Reinforcement Learning Agents. AAAI 2022: 11094-11102 - [c12]Subhajit Chaudhury, Sarathkrishna Swaminathan, R. Chulaka Gunasekara, Maxwell Crouse, Srinivas Ravishankar, Daiki Kimura, Keerthiram Murugesan, Ramón Fernandez Astudillo, Tahira Naseem, Pavan Kapanipathi, Alexander Gray:
X-FACTOR: A Cross-metric Evaluation of Factual Correctness in Abstractive Summarization. EMNLP 2022: 7100-7110 - [c11]Mattia Atzeni, Shehzaad Zuzar Dhuliawala, Keerthiram Murugesan, Mrinmaya Sachan:
Case-based reasoning for better generalization in textual reinforcement learning. ICLR 2022 - [c10]Keerthiram Murugesan, Vijay Sadashivaiah, Ronny Luss, Karthikeyan Shanmugam, Pin-Yu Chen, Amit Dhurandhar:
Auto-Transfer: Learning to Route Transferable Representations. ICLR 2022 - [i12]Keerthiram Murugesan, Vijay Sadashivaiah, Ronny Luss, Karthikeyan Shanmugam, Pin-Yu Chen, Amit Dhurandhar:
Auto-Transfer: Learning to Route Transferrable Representations. CoRR abs/2202.01011 (2022) - [i11]Tsuyoshi Idé, Keerthiram Murugesan, Djallel Bouneffouf, Naoki Abe:
Targeted Advertising on Social Networks Using Online Variational Tensor Regression. CoRR abs/2208.10627 (2022) - [i10]Heshan Devaka Fernando, Han Shen, Miao Liu, Subhajit Chaudhury, Keerthiram Murugesan, Tianyi Chen:
Mitigating Gradient Bias in Multi-objective Learning: A Provably Convergent Stochastic Approach. CoRR abs/2210.12624 (2022) - [i9]Vishal Pallagani, Bharath Muppasani, Keerthiram Murugesan, Francesca Rossi, Lior Horesh, Biplav Srivastava, Francesco Fabiano, Andrea Loreggia:
Plansformer: Generating Symbolic Plans using Transformers. CoRR abs/2212.08681 (2022) - 2021
- [c9]Keerthiram Murugesan, Mattia Atzeni, Pavan Kapanipathi, Pushkar Shukla, Sadhana Kumaravel, Gerald Tesauro, Kartik Talamadupula, Mrinmaya Sachan, Murray Campbell:
Text-based RL Agents with Commonsense Knowledge: New Challenges, Environments and Baselines. AAAI 2021: 9018-9027 - [c8]Grady Booch, Francesco Fabiano, Lior Horesh, Kiran Kate, Jonathan Lenchner, Nick Linck, Andrea Loreggia, Keerthiram Murugesan, Nicholas Mattei, Francesca Rossi, Biplav Srivastava:
Thinking Fast and Slow in AI. AAAI 2021: 15042-15046 - [c7]Francesco Fabiano, Marianna Bergamaschi Ganapini, Lior Horesh, Andrea Loreggia, Keerthiram Murugesan, Vishal Pallagani, Francesca Rossi, Biplav Srivastava:
Epistemic Planning in a Fast and Slow Setting. TFSOCTAI@AAAI Fall Symposium 2021 - [c6]Keerthiram Murugesan, Mattia Atzeni, Pavan Kapanipathi, Kartik Talamadupula, Mrinmaya Sachan, Murray Campbell:
Efficient Text-based Reinforcement Learning by Jointly Leveraging State and Commonsense Graph Representations. ACL/IJCNLP (2) 2021: 719-725 - [i8]Keerthiram Murugesan, Subhajit Chaudhury, Kartik Talamadupula:
Eye of the Beholder: Improved Relation Generalization for Text-based Reinforcement Learning Agents. CoRR abs/2106.05387 (2021) - [i7]Mattia Atzeni, Shehzaad Dhuliawala, Keerthiram Murugesan, Mrinmaya Sachan:
Case-based Reasoning for Better Generalization in Text-Adventure Games. CoRR abs/2110.08470 (2021) - 2020
- [i6]Keerthiram Murugesan, Mattia Atzeni, Pushkar Shukla, Mrinmaya Sachan, Pavan Kapanipathi, Kartik Talamadupula:
Enhancing Text-based Reinforcement Learning Agents with Commonsense Knowledge. CoRR abs/2005.00811 (2020) - [i5]Keerthiram Murugesan, Mattia Atzeni, Pavan Kapanipathi, Pushkar Shukla, Sadhana Kumaravel, Gerald Tesauro, Kartik Talamadupula, Mrinmaya Sachan, Murray Campbell:
Text-based RL Agents with Commonsense Knowledge: New Challenges, Environments and Baselines. CoRR abs/2010.03790 (2020) - [i4]Grady Booch, Francesco Fabiano, Lior Horesh, Kiran Kate, Jon Lenchner, Nick Linck, Andrea Loreggia, Keerthiram Murugesan, Nicholas Mattei, Francesca Rossi, Biplav Srivastava:
Thinking Fast and Slow in AI. CoRR abs/2010.06002 (2020)
2010 – 2019
- 2017
- [j1]Meghana Kshirsagar, Keerthiram Murugesan, Jaime G. Carbonell, Judith Klein-Seetharaman:
Multitask Matrix Completion for Learning Protein Interactions Across Diseases. J. Comput. Biol. 24(6): 501-514 (2017) - [c5]Keerthiram Murugesan, Jaime G. Carbonell:
Self-Paced Multitask Learning with Shared Knowledge. IJCAI 2017: 2522-2528 - [c4]Keerthiram Murugesan, Jaime G. Carbonell:
Active Learning from Peers. NIPS 2017: 7008-7017 - [c3]Keerthiram Murugesan, Jaime G. Carbonell:
Multi-Task Multiple Kernel Relationship Learning. SDM 2017: 687-695 - [i3]Keerthiram Murugesan, Jaime G. Carbonell:
Self-Paced Multitask Learning with Shared Knowledge. CoRR abs/1703.00977 (2017) - [i2]Keerthiram Murugesan, Jaime G. Carbonell, Yiming Yang:
Co-Clustering for Multitask Learning. CoRR abs/1703.00994 (2017) - 2016
- [c2]Keerthiram Murugesan, Hanxiao Liu, Jaime G. Carbonell, Yiming Yang:
Adaptive Smoothed Online Multi-Task Learning. NIPS 2016: 4296-4304 - [c1]Meghana Kshirsagar, Jaime G. Carbonell, Judith Klein-Seetharaman, Keerthiram Murugesan:
Multitask Matrix Completion for Learning Protein Interactions Across Diseases. RECOMB 2016: 53-64 - [i1]Keerthiram Murugesan, Jaime G. Carbonell:
Multi-Task Multiple Kernel Relationship Learning. CoRR abs/1611.03427 (2016)
Coauthor Index
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