Adaptive Smoothed Online Multi-Task Learning. - Keerthiram Murugesan; Hanxiao Liu; Jaime G. Carbonell; Yiming Yang (NIPS, 2016)

Self-Paced Multitask Learning with Shared Knowledge. - Keerthiram Murugesan; Jaime G. Carbonell (IJCAI, 2017)

Active Learning from Peers. - Keerthiram Murugesan; Jaime G. Carbonell (NIPS, 2017)

Multi-Task Multiple Kernel Relationship Learning. - Keerthiram Murugesan; Jaime G. Carbonell (SDM, 2017)

Co-Clustering for Multitask Learning. - Keerthiram Murugesan; Jaime G. Carbonell; Yiming Yang (CoRR, 2017)

Multitask Matrix Completion for Learning Protein Interactions Across Diseases. - Meghana Kshirsagar 001; Keerthiram Murugesan; Jaime G. Carbonell; Judith Klein-Seetharaman (J. Comput. Biol., 2017)

Enhancing Text-based Reinforcement Learning Agents with Commonsense Knowledge. - Keerthiram Murugesan; Mattia Atzeni; Pushkar Shukla; Mrinmaya Sachan; Pavan Kapanipathi; Kartik Talamadupula (CoRR, 2020)

Thinking Fast and Slow in AI. - Grady Booch; Francesco Fabiano; Lior Horesh; Kiran Kate; Jonathan Lenchner; Nick Linck; Andrea Loreggia; Keerthiram Murugesan; Nicholas Mattei; Francesca Rossi 001; Biplav Srivastava (AAAI, 2021)

Text-based RL Agents with Commonsense Knowledge: New Challenges, Environments and Baselines. - Keerthiram Murugesan; Mattia Atzeni; Pavan Kapanipathi; Pushkar Shukla; Sadhana Kumaravel; Gerald Tesauro; Kartik Talamadupula; Mrinmaya Sachan; Murray Campbell (AAAI, 2021)

Epistemic Planning in a Fast and Slow Setting. - Francesco Fabiano; Marianna Bergamaschi Ganapini; Lior Horesh; Andrea Loreggia; Keerthiram Murugesan; Vishal Pallagani; Francesca Rossi 001; Biplav Srivastava (TFSOCTAI@AAAI Fall Symposium, 2021)

Efficient Text-based Reinforcement Learning by Jointly Leveraging State and Commonsense Graph Representations. - Keerthiram Murugesan; Mattia Atzeni; Pavan Kapanipathi; Kartik Talamadupula; Mrinmaya Sachan; Murray Campbell (ACL/IJCNLP (2), 2021)

Case-based Reasoning for Better Generalization in Text-Adventure Games. - Mattia Atzeni; Shehzaad Dhuliawala; Keerthiram Murugesan; Mrinmaya Sachan (CoRR, 2021)

Eye of the Beholder: Improved Relation Generalization for Text-Based Reinforcement Learning Agents. - Keerthiram Murugesan; Subhajit Chaudhury; Kartik Talamadupula (AAAI, 2022)

X-FACTOR: A Cross-metric Evaluation of Factual Correctness in Abstractive Summarization. - Subhajit Chaudhury; Sarathkrishna Swaminathan; R. Chulaka Gunasekara; Maxwell Crouse; Srinivas Ravishankar; Daiki Kimura; Keerthiram Murugesan; Ramón Fernandez Astudillo; Tahira Naseem; Pavan Kapanipathi; Alexander Gray 001 (EMNLP, 2022)

Case-based reasoning for better generalization in textual reinforcement learning. - Mattia Atzeni; Shehzaad Zuzar Dhuliawala; Keerthiram Murugesan; Mrinmaya Sachan (ICLR, 2022)

Auto-Transfer: Learning to Route Transferable Representations. - Keerthiram Murugesan; Vijay Sadashivaiah; Ronny Luss; Karthikeyan Shanmugam; Pin-Yu Chen; Amit Dhurandhar (ICLR, 2022)

Auto-Transfer: Learning to Route Transferrable Representations. - Keerthiram Murugesan; Vijay Sadashivaiah; Ronny Luss; Karthikeyan Shanmugam; Pin-Yu Chen; Amit Dhurandhar (CoRR, 2022)

Targeted Advertising on Social Networks Using Online Variational Tensor Regression. - Tsuyoshi Idé; Keerthiram Murugesan; Djallel Bouneffouf 001; Naoki Abe (CoRR, 2022)

Mitigating Gradient Bias in Multi-objective Learning: A Provably Convergent Stochastic Approach. - Heshan Devaka Fernando; Han Shen; Miao Liu 001; Subhajit Chaudhury; Keerthiram Murugesan; Tianyi Chen (CoRR, 2022)

Plansformer: Generating Symbolic Plans using Transformers. - Vishal Pallagani; Bharath Muppasani; Keerthiram Murugesan; Francesca Rossi 001; Lior Horesh; Biplav Srivastava; Francesco Fabiano; Andrea Loreggia (CoRR, 2022)

Learning Symbolic Rules over Abstract Meaning Representations for Textual Reinforcement Learning. - Subhajit Chaudhury; Sarathkrishna Swaminathan; Daiki Kimura; Prithviraj Sen; Keerthiram Murugesan; Rosario Uceda-Sosa; Michiaki Tatsubori; Achille Fokoue; Pavan Kapanipathi; Asim Munawar; Alexander Gray 001 (ACL (1), 2023)

MISMATCH: Fine-grained Evaluation of Machine-generated Text with Mismatch Error Types. - 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 001 (ACL (Findings), 2023)

Value-based Fast and Slow AI Nudging. - Marianna Bergamaschi Ganapini; Francesco Fabiano; Lior Horesh; Andrea Loreggia; Nicholas Mattei; Keerthiram Murugesan; Vishal Pallagani; Francesca Rossi 001; Biplav Srivastava; Kristen Brent Venable (ETHAICS@IJCAI, 2023)

Mitigating Gradient Bias in Multi-objective Learning: A Provably Convergent Approach. - Heshan Devaka Fernando; Han Shen; Miao Liu 001; Subhajit Chaudhury; Keerthiram Murugesan; Tianyi Chen (ICLR, 2023)

Probabilistic Rule Induction from Event Sequences with Logical Summary Markov Models. - Debarun Bhattacharjya; Oktie Hassanzadeh; Ronny Luss; Keerthiram Murugesan (IJCAI, 2023)

Plansformer Tool: Demonstrating Generation of Symbolic Plans Using Transformers. - Vishal Pallagani; Bharath Muppasani; Biplav Srivastava; Francesca Rossi 001; Lior Horesh; Keerthiram Murugesan; Andrea Loreggia; Francesco Fabiano; Rony Joseph; Yathin Kethepalli (IJCAI, 2023)

On the Convergence and Sample Complexity Analysis of Deep Q-Networks with ε-Greedy Exploration. - Shuai Zhang 015; Hongkang Li; Meng Wang 003; Miao Liu 001; Pin-Yu Chen; Songtao Lu; Sijia Liu 001; Keerthiram Murugesan; Subhajit Chaudhury (NeurIPS, 2023)

Fast and Slow Planning. - Francesco Fabiano; Vishal Pallagani; Marianna Bergamaschi Ganapini; Lior Horesh; Andrea Loreggia; Keerthiram Murugesan; Francesca Rossi 001; Biplav Srivastava (CoRR, 2023)

Understanding the Capabilities of Large Language Models for Automated Planning. - Vishal Pallagani; Bharath Muppasani; Keerthiram Murugesan; Francesca Rossi 001; Biplav Srivastava; Lior Horesh; Francesco Fabiano; Andrea Loreggia (CoRR, 2023)

STARLING: Self-supervised Training of Text-based Reinforcement Learning Agent with Large Language Models. - Shreyas Basavatia; Keerthiram Murugesan; Shivam Ratnakar (ACL (Findings), 2024)

Examining Trustworthiness of LLM-as-a-Judge Systems in a Clinical Trial Design Benchmark. - Corey Curran; Nafis Neehal; Keerthiram Murugesan; Kristin P. Bennett (IEEE Big Data, 2024)

EXPLORER: Exploration-guided Reasoning for Textual Reinforcement Learning. - Kinjal Basu 002; Keerthiram Murugesan; Subhajit Chaudhury; Murray Campbell; Kartik Talamadupula; Tim Klinger (EACL (1), 2024)

Towards Aligning Language Models with Textual Feedback. - Saüc Abadal Lloret; Shehzaad Dhuliawala; Keerthiram Murugesan; Mrinmaya Sachan (EMNLP, 2024)

On the Prospects of Incorporating Large Language Models (LLMs) in Automated Planning and Scheduling (APS). - Vishal Pallagani; Bharath C. Muppasani; Kaushik Roy 009; Francesco Fabiano; Andrea Loreggia; Keerthiram Murugesan; Biplav Srivastava; Francesca Rossi 001; Lior Horesh; Amit P. Sheth (ICAPS, 2024)

Leveraging Visual Handicaps for Text-Based Reinforcement Learning. - Subhajit Chaudhury; Keerthiram Murugesan; Thomas Carta; Kartik Talamadupula; Michiaki Tatsubori (ICASSP, 2024)

Variance Reduction Can Improve Trade-Off in Multi-Objective Learning. - Heshan Devaka Fernando; Lisha Chen; Songtao Lu; Pin-Yu Chen; Miao Liu 001; Subhajit Chaudhury; Keerthiram Murugesan; Gaowen Liu; Meng Wang 003; Tianyi Chen (ICASSP, 2024)

SF-DQN: Provable Knowledge Transfer using Successor Feature for Deep Reinforcement Learning. - Shuai Zhang 015; Heshan Devaka Fernando; Miao Liu 001; Keerthiram Murugesan; Songtao Lu; Pin-Yu Chen; Tianyi Chen; Meng Wang 003 (ICML, 2024)

Language Guided Exploration for RL Agents in Text Environments. - Hitesh Golchha; Sahil Yerawar; Dhruvesh Patel; Soham Dan; Keerthiram Murugesan (NAACL-HLT (Findings), 2024)

Detectors for Safe and Reliable LLMs: Implementations, Uses, and Limitations. - Swapnaja Achintalwar; Adriana Alvarado Garcia; Ateret Anaby-Tavor; Ioana Baldini; Sara E. Berger; Bishwaranjan Bhattacharjee; Djallel Bouneffouf 001; Subhajit Chaudhury; Pin-Yu Chen; Lamogha Chiazor; Elizabeth M. Daly; Rogério Abreu de Paula; et al. (CoRR, 2024)

Facilitating Human-LLM Collaboration through Factuality Scores and Source Attributions. - Hyo Jin Do; Rachel Ostrand; Justin D. Weisz; Casey Dugan; Prasanna Sattigeri; Dennis Wei; Keerthiram Murugesan; Werner Geyer (CoRR, 2024)

CTBench: A Comprehensive Benchmark for Evaluating Language Model Capabilities in Clinical Trial Design. - Nafis Neehal; Bowen Wang; Shayom Debopadhaya; Soham Dan; Keerthiram Murugesan; Vibha Anand; Kristin P. Bennett (CoRR, 2024)

Granite Guardian. - Inkit Padhi; Manish Nagireddy; Giandomenico Cornacchia; Subhajit Chaudhury; Tejaswini Pedapati; Pierre L. Dognin; Keerthiram Murugesan; Erik Miehling; Martin Santillan Cooper; Kieran Fraser; Giulio Zizzo; Muhammad Zaid Hameed; et al. (CoRR, 2024)

To Transfer or Not to Transfer: Suppressing Concepts from Source Representations. - Vijay Sadashivaiah; Keerthiram Murugesan; Ronny Luss; Pin-Yu Chen; Chris R. Sims; James A. Hendler; Amit Dhurandhar (Trans. Mach. Learn. Res., 2024)

Protecting Users From Themselves: Safeguarding Contextual Privacy in Interactions with Conversational Agents. - Ivoline C. Ngong; Swanand Ravindra Kadhe; Hao Wang 063; Keerthiram Murugesan; Justin D. Weisz; Amit Dhurandhar; Karthikeyan Natesan Ramamurthy (ACL (Findings), 2025)

NGQA: A Nutritional Graph Question Answering Benchmark for Personalized Health-aware Nutritional Reasoning. - Zheyuan Zhang; Yiyang Li; Nhi Ha Lan Le; Zehong Wang; Tianyi Ma; Vincent Galassi; Keerthiram Murugesan; Nuno Moniz; Werner Geyer; Nitesh V. Chawla; Chuxu Zhang; Yanfang Ye 001 (ACL (1), 2025)

On the Effects of Fine-tuning Language Models for Text-Based Reinforcement Learning. - Maurício Gruppi; Soham Dan; Keerthiram Murugesan; Subhajit Chaudhury (COLING, 2025)

MOPI-HFRS: A Multi-objective Personalized Health-aware Food Recommendation System with LLM-enhanced Interpretation. - Zheyuan Zhang; Zehong Wang; Tianyi Ma; Varun Sameer Taneja; Sofia Nelson; Nhi Ha Lan Le; Keerthiram Murugesan; Mingxuan Ju; Nitesh V. Chawla; Chuxu Zhang; Yanfang Ye 001 (KDD (1), 2025)

Are Large Language Models Effective in Clinical Trial Design? A Study on Baseline Feature Generation. - Nafis Neehal; Bowen Wang; Shayom Debopadhaya; Corey Curran; Keerthiram Murugesan; Soham Dan; Vibha Anand; Kristin P. Bennett (NAACL (Findings), 2025)

Granite Guardian: Comprehensive LLM Safeguarding. - Inkit Padhi; Manish Nagireddy; Giandomenico Cornacchia; Subhajit Chaudhury; Tejaswini Pedapati; Pierre L. Dognin; Keerthiram Murugesan; Erik Miehling; Martín Santillán Cooper; Kieran Fraser; Giulio Zizzo; Muhammad Zaid Hameed; et al. (NAACL (Industry Track), 2025)

PEEL the Layers and Find Yourself: Revisiting Inference-Time Data Leakage for Residual Neural Networks. - Huzaifa Arif; Keerthiram Murugesan; Payel Das; Alex Gittens; Pin-Yu Chen (SaTML, 2025)

Group Fair Federated Learning via Stochastic Kernel Regularization. - Huzaifa Arif; Pin-Yu Chen; Keerthiram Murugesan; Alex Gittens (Trans. Mach. Learn. Res., 2025)

Combinatorial Multi-armed Bandits: Arm Selection via Group Testing. - Arpan Mukherjee; Shashanka Ubaru; Keerthiram Murugesan; Karthikeyan Shanmugam; Ali Tajer (Trans. Mach. Learn. Res., 2025)

Cross-Examiner: Evaluating Consistency of Large Language Model-Generated Explanations. - Danielle Villa; Maria D. Chang; Keerthiram Murugesan; Rosario Uceda-Sosa; Karthikeyan Natesan Ramamurthy (CoRR, 2025)

Sequential uncertainty quantification with contextual tensors for social targeting. - Tsuyoshi Idé; Keerthiram Murugesan; Djallel Bouneffouf 001; Naoki Abe (Knowl. Inf. Syst., 2025)

EfficientLLM: Efficiency in Large Language Models. - Zhengqing Yuan; Weixiang Sun; Yixin Liu 002; Huichi Zhou; Rong Zhou; Yiyang Li; Zheyuan Zhang; Wei Song; Yue Huang; Haolong Jia; Keerthiram Murugesan; Yu Wang; et al. (CoRR, 2025)

AutoData: A Multi-Agent System for Open Web Data Collection. - Tianyi Ma; Yiyue Qian; Zheyuan Zhang; Zehong Wang; Xiaoye Qian; Feifan Bai; Yifan Ding; Xuwei Luo; Shinan Zhang; Keerthiram Murugesan; Chuxu Zhang; Yanfang Ye 001 (CoRR, 2025)

Context Attribution with Multi-Armed Bandit Optimization. - Deng Pan; Keerthiram Murugesan; Nuno Moniz; Nitesh V. Chawla (CoRR, 2025)

Thinking Fast and Slow in Human and Machine Intelligence. - Francesco Fabiano; Marianna Bergamaschi Ganapini; Andrea Loreggia; Nicholas Mattei; Keerthiram Murugesan; Vishal Pallagani; Francesca Rossi 001; Biplav Srivastava; Kristen Brent Venable (Commun. ACM, 2025)

Highlight All the Phrases: Enhancing LLM Transparency through Visual Factuality Indicators. - Hyo Jin Do; Rachel Ostrand; Werner Geyer; Keerthiram Murugesan; Dennis Wei; Justin D. Weisz (CoRR, 2025)