Some new directions in graph-based semi-supervised learning. - Xiaojin Zhu 001; Andrew B. Goldberg; Tushar Khot (ICME, 2009)

Exploiting Causal Independence in Markov Logic Networks: Combining Undirected and Directed Models. - Sriraam Natarajan; Tushar Khot; Daniel Lowd; Prasad Tadepalli; Kristian Kersting; Jude W. Shavlik (StarAI@AAAI, 2010)

Learning Markov Logic Networks via Functional Gradient Boosting. - Tushar Khot; Sriraam Natarajan; Kristian Kersting; Jude W. Shavlik (ICDM, 2011)

A Machine Learning Pipeline for Three-Way Classification of Alzheimer Patients from Structural Magnetic Resonance Images of the Brain. - Sriraam Natarajan; Saket Joshi; Baidya Nath Saha; Adam Edwards; Tushar Khot; Elizabeth Moody; Kristian Kersting; Christopher T. Whitlow; Joseph A. Maldjian (ICMLA (1), 2012)

Learning Relational Structure for Temporal Relation Extraction. - Tushar Khot; Siddharth Srivastava 001; Sriraam Natarajan; Jude W. Shavlik (StarAI@UAI, 2012)

Accelarating Imitation Learning in Relational Domains via Transfer by Initialization. - Sriraam Natarajan; Phillip Odom; Saket Joshi; Tushar Khot; Kristian Kersting; Prasad Tadepalli (StarAI@UAI, 2012)

Gradient-based boosting for statistical relational learning: The relational dependency network case. - Sriraam Natarajan; Tushar Khot; Kristian Kersting; Bernd Gutmann; Jude W. Shavlik (Mach. Learn., 2012)

Using Commonsense Knowledge to Automatically Create (Noisy) Training Examples from Text. - Sriraam Natarajan; Jose Picado; Tushar Khot; Kristian Kersting; Christopher Ré; Jude W. Shavlik (StarAI@AAAI, 2013)

Accelerating Imitation Learning in Relational Domains via Transfer by Initialization. - Sriraam Natarajan; Phillip Odom; Saket Joshi; Tushar Khot; Kristian Kersting; Prasad Tadepalli (ILP, 2013)

Bootstrapping Knowledge Base Acceleration. - Tushar Khot; Ce Zhang 001; Jude W. Shavlik; Sriraam Natarajan; Christopher Ré (TREC, 2013)

Relational One-Class Classification: A Non-Parametric Approach. - Tushar Khot; Sriraam Natarajan; Jude W. Shavlik (AAAI, 2014)

Classification from One Class of Examples for Relational Domains. - Tushar Khot; Sriraam Natarajan; Jude W. Shavlik (StarAI@AAAI, 2014)

Learning from Imbalanced Data in Relational Domains: A Soft Margin Approach. - Shuo Yang 004; Tushar Khot; Kristian Kersting; Gautam Kunapuli; Kris Hauser; Sriraam Natarajan (ICDM, 2014)

Effectively Creating Weakly Labeled Training Examples via Approximate Domain Knowledge. - Sriraam Natarajan; Jose Picado; Tushar Khot; Kristian Kersting; Christopher Ré; Jude W. Shavlik (ILP, 2014)

Statistical Relational Learning for Handwriting Recognition. - Arti Shivram; Tushar Khot; Sriraam Natarajan; Venu Govindaraju (ILP, 2014)

Relational learning helps in three-way classification of Alzheimer patients from structural magnetic resonance images of the brain. - Sriraam Natarajan; Baidya Nath Saha; Saket Joshi; Adam Edwards; Tushar Khot; Elizabeth M. Davenport; Kristian Kersting; Christopher T. Whitlow; Joseph A. Maldjian (Int. J. Mach. Learn. Cybern., 2014)

Boosted Statistical Relational Learners - From Benchmarks to Data-Driven Medicine - Sriraam Natarajan; Kristian Kersting; Tushar Khot; Jude W. Shavlik (Springer, 2014)

Knowledge-Based Probabilistic Logic Learning. - Phillip Odom; Tushar Khot; Reid B. Porter; Sriraam Natarajan (AAAI, 2015)

Learning Probabilistic Logic Models with Human Advice. - Phillip Odom; Tushar Khot; Sriraam Natarajan (AAAI Spring Symposia, 2015)

Extracting Adverse Drug Events from Text Using Human Advice. - Phillip Odom; Vishal Bangera; Tushar Khot; David Page; Sriraam Natarajan (AIME, 2015)

Exploring Markov Logic Networks for Question Answering. - Tushar Khot; Niranjan Balasubramanian; Eric Gribkoff; Ashish Sabharwal; Peter Clark; Oren Etzioni (EMNLP, 2015)

Anomaly Detection in Text: The Value of Domain Knowledge. - Raksha Kumaraswamy; Anurag Wazalwar; Tushar Khot; Jude W. Shavlik; Sriraam Natarajan (FLAIRS, 2015)

Markov Logic Networks for Natural Language Question Answering. - Tushar Khot; Niranjan Balasubramanian; Eric Gribkoff; Ashish Sabharwal; Peter Clark; Oren Etzioni (CoRR, 2015)

Gradient-based boosting for statistical relational learning: the Markov logic network and missing data cases. - Tushar Khot; Sriraam Natarajan; Kristian Kersting; Jude W. Shavlik (Mach. Learn., 2015)

Combining Retrieval, Statistics, and Inference to Answer Elementary Science Questions. - Peter Clark; Oren Etzioni; Tushar Khot; Ashish Sabharwal; Oyvind Tafjord; Peter D. Turney; Daniel Khashabi (AAAI, 2016)

Learning Continuous-Time Bayesian Networks in Relational Domains: A Non-Parametric Approach. - Shuo Yang 004; Tushar Khot; Kristian Kersting; Sriraam Natarajan (AAAI, 2016)

Question Answering via Integer Programming over Semi-Structured Knowledge. - Daniel Khashabi; Tushar Khot; Ashish Sabharwal; Peter Clark; Oren Etzioni; Dan Roth 001 (IJCAI, 2016)

Inductive Logic Programming Meets Relational Databases: Efficient Learning of Markov Logic Networks. - Marcin Malec; Tushar Khot; James G. Nagy; Erik Blask; Sriraam Natarajan (ILP, 2016)

Scaling Lifted Probabilistic Inference and Learning Via Graph Databases. - Mayukh Das; Yuqing Wu; Tushar Khot; Kristian Kersting; Sriraam Natarajan (SDM, 2016)

Answering Complex Questions Using Open Information Extraction. - Tushar Khot; Ashish Sabharwal; Peter Clark (ACL (2), 2017)

Learning What is Essential in Questions. - Daniel Khashabi; Tushar Khot; Ashish Sabharwal; Dan Roth 001 (CoNLL, 2017)

Relational Restricted Boltzmann Machines: A Probabilistic Logic Learning Approach. - Navdeep Kaur; Gautam Kunapuli; Tushar Khot; Kristian Kersting; William Cohen; Sriraam Natarajan (ILP, 2017)

Markov logic networks for adverse drug event extraction from text. - Sriraam Natarajan; Vishal Bangera; Tushar Khot; Jose Picado; Anurag Wazalwar; Vítor Santos Costa; David Page; Michael Caldwell (Knowl. Inf. Syst., 2017)

Question Answering as Global Reasoning Over Semantic Abstractions. - Daniel Khashabi; Tushar Khot; Ashish Sabharwal; Dan Roth 001 (AAAI, 2018)

SciTaiL: A Textual Entailment Dataset from Science Question Answering. - Tushar Khot; Ashish Sabharwal; Peter Clark (AAAI, 2018)

AdvEntuRe: Adversarial Training for Textual Entailment with Knowledge-Guided Examples. - Dongyeop Kang; Tushar Khot; Ashish Sabharwal; Eduard H. Hovy (ACL (1), 2018)

Bridging Knowledge Gaps in Neural Entailment via Symbolic Models. - Dongyeop Kang; Tushar Khot; Ashish Sabharwal; Peter Clark (EMNLP, 2018)

Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering. - Todor Mihaylov; Peter Clark; Tushar Khot; Ashish Sabharwal (EMNLP, 2018)

Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge. - Peter Clark; Isaac Cowhey; Oren Etzioni; Tushar Khot; Ashish Sabharwal; Carissa Schoenick; Oyvind Tafjord (CoRR, 2018)

Exploiting Explicit Paths for Multi-hop Reading Comprehension. - Souvik Kundu 003; Tushar Khot; Ashish Sabharwal (CoRR, 2018)

Exploiting Explicit Paths for Multi-hop Reading Comprehension. - Souvik Kundu 003; Tushar Khot; Ashish Sabharwal; Peter Clark (ACL (1), 2019)

What's Missing: A Knowledge Gap Guided Approach for Multi-hop Question Answering. - Tushar Khot; Ashish Sabharwal; Peter Clark (EMNLP/IJCNLP (1), 2019)

Repurposing Entailment for Multi-Hop Question Answering Tasks. - Harsh Trivedi; Heeyoung Kwon; Tushar Khot; Ashish Sabharwal; Niranjan Balasubramanian (NAACL-HLT (1), 2019)

On the Capabilities and Limitations of Reasoning for Natural Language Understanding. - Daniel Khashabi; Erfan Sadeqi Azer; Tushar Khot; Ashish Sabharwal; Dan Roth 001 (CoRR, 2019)

QASC: A Dataset for Question Answering via Sentence Composition. - Tushar Khot; Peter Clark; Michal Guerquin; Peter Jansen 001; Ashish Sabharwal (AAAI, 2020)

IIRC: A Dataset of Incomplete Information Reading Comprehension Questions. - James Ferguson; Matt Gardner 001; Hannaneh Hajishirzi; Tushar Khot; Pradeep Dasigi (EMNLP (1), 2020)

A Simple Yet Strong Pipeline for HotpotQA. - Dirk Groeneveld; Tushar Khot; Mausam; Ashish Sabharwal (EMNLP (1), 2020)

More Bang for Your Buck: Natural Perturbation for Robust Question Answering. - Daniel Khashabi; Tushar Khot; Ashish Sabharwal (EMNLP (1), 2020)

UnifiedQA: Crossing Format Boundaries With a Single QA System. - Daniel Khashabi; Sewon Min; Tushar Khot; Ashish Sabharwal; Oyvind Tafjord; Peter Clark; Hannaneh Hajishirzi (EMNLP (Findings), 2020)

UNQOVERing Stereotypical Biases via Underspecified Questions. - Tao Li 039; Daniel Khashabi; Tushar Khot; Ashish Sabharwal; Vivek Srikumar (EMNLP (Findings), 2020)

Is Multihop QA in DiRe Condition? Measuring and Reducing Disconnected Reasoning. - Harsh Trivedi; Niranjan Balasubramanian; Tushar Khot; Ashish Sabharwal (EMNLP (1), 2020)

From 'F' to 'A' on the N.Y. Regents Science Exams: An Overview of the Aristo Project. - Peter Clark; Oren Etzioni; Tushar Khot; Daniel Khashabi; Bhavana Dalvi Mishra; Kyle Richardson 001; Ashish Sabharwal; Carissa Schoenick; Oyvind Tafjord; Niket Tandon; Sumithra Bhakthavatsalam; Dirk Groeneveld; et al. (AI Mag., 2020)

Natural Perturbation for Robust Question Answering. - Daniel Khashabi; Tushar Khot; Ashish Sabharwal (CoRR, 2020)

UnifiedQA: Crossing Format Boundaries With a Single QA System. - Daniel Khashabi; Tushar Khot; Ashish Sabharwal; Oyvind Tafjord; Peter Clark; Hannaneh Hajishirzi (CoRR, 2020)

Measuring and Reducing Non-Multifact Reasoning in Multi-hop Question Answering. - Harsh Trivedi; Niranjan Balasubramanian; Tushar Khot; Ashish Sabharwal (CoRR, 2020)

UnQovering Stereotyping Biases via Underspecified Questions. - Tao Li 039; Tushar Khot; Daniel Khashabi; Ashish Sabharwal; Vivek Srikumar (CoRR, 2020)

ReadOnce Transformers: Reusable Representations of Text for Transformers. - Shih-Ting Lin; Ashish Sabharwal; Tushar Khot (ACL/IJCNLP (1), 2021)

Ethical-Advice Taker: Do Language Models Understand Natural Language Interventions? - Jieyu Zhao 001; Daniel Khashabi; Tushar Khot; Ashish Sabharwal; Kai-Wei Chang (ACL/IJCNLP (Findings), 2021)

GooAQ: Open Question Answering with Diverse Answer Types. - Daniel Khashabi; Amos Ng; Tushar Khot; Ashish Sabharwal; Hannaneh Hajishirzi; Chris Callison-Burch (EMNLP (Findings), 2021)

Text Modular Networks: Learning to Decompose Tasks in the Language of Existing Models. - Tushar Khot; Daniel Khashabi; Kyle Richardson 001; Peter Clark; Ashish Sabharwal (NAACL-HLT, 2021)

Temporal Reasoning on Implicit Events from Distant Supervision. - Ben Zhou; Kyle Richardson 001; Qiang Ning; Tushar Khot; Ashish Sabharwal; Dan Roth 001 (NAACL-HLT, 2021)

Think you have Solved Direct-Answer Question Answering? Try ARC-DA, the Direct-Answer AI2 Reasoning Challenge. - Sumithra Bhakthavatsalam; Daniel Khashabi; Tushar Khot; Bhavana Dalvi Mishra; Kyle Richardson 001; Ashish Sabharwal; Carissa Schoenick; Oyvind Tafjord; Peter Clark (CoRR, 2021)

MuSiQue: Multi-hop Questions via Single-hop Question Composition. - Harsh Trivedi; Niranjan Balasubramanian; Tushar Khot; Ashish Sabharwal (CoRR, 2021)

Learning to Solve Complex Tasks by Talking to Agents. - Tushar Khot; Kyle Richardson 001; Daniel Khashabi; Ashish Sabharwal (CoRR, 2021)

PROMPT WAYWARDNESS: The Curious Case of Discretized Interpretation of Continuous Prompts. - Daniel Khashabi; Shane Lyu; Sewon Min; Lianhui Qin; Kyle Richardson 001; Sameer Singh 001; Sean Welleck; Hannaneh Hajishirzi; Tushar Khot; Ashish Sabharwal; Yejin Choi 001 (CoRR, 2021)

Structure learning for relational logistic regression: an ensemble approach. - Nandini Ramanan; Gautam Kunapuli; Tushar Khot; Bahare Fatemi; Seyed Mehran Kazemi; David Poole 001; Kristian Kersting; Sriraam Natarajan (Data Min. Knowl. Discov., 2021)

Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies. - Mor Geva; Daniel Khashabi; Elad Segal; Tushar Khot; Dan Roth 001; Jonathan Berant (Trans. Assoc. Comput. Linguistics, 2021)

Hey AI, Can You Solve Complex Tasks by Talking to Agents? - Tushar Khot; Kyle Richardson 001; Daniel Khashabi; Ashish Sabharwal (ACL (Findings), 2022)

Teaching Broad Reasoning Skills for Multi-Step QA by Generating Hard Contexts. - Harsh Trivedi; Niranjan Balasubramanian; Tushar Khot; Ashish Sabharwal (EMNLP, 2022)

Prompt Waywardness: The Curious Case of Discretized Interpretation of Continuous Prompts. - Daniel Khashabi; Xinxi Lyu; Sewon Min; Lianhui Qin; Kyle Richardson 001; Sean Welleck; Hannaneh Hajishirzi; Tushar Khot; Ashish Sabharwal; Sameer Singh 001; Yejin Choi 001 (NAACL-HLT, 2022)

Better Retrieval May Not Lead to Better Question Answering. - Zhengzhong Liang; Tushar Khot; Steven Bethard; Mihai Surdeanu; Ashish Sabharwal (CoRR, 2022)

Teaching Broad Reasoning Skills via Decomposition-Guided Contexts. - Harsh Trivedi; Niranjan Balasubramanian; Tushar Khot; Ashish Sabharwal (CoRR, 2022)

♫ MuSiQue: Multihop Questions via Single-hop Question Composition. - Harsh Trivedi; Niranjan Balasubramanian; Tushar Khot; Ashish Sabharwal (Trans. Assoc. Comput. Linguistics, 2022)

The Tail Wagging the Dog: Dataset Construction Biases of Social Bias Benchmarks. - Nikil Roashan Selvam; Sunipa Dev; Daniel Khashabi; Tushar Khot; Kai-Wei Chang (ACL (2), 2023)

Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions. - Harsh Trivedi; Niranjan Balasubramanian; Tushar Khot; Ashish Sabharwal (ACL (1), 2023)

Complexity-Based Prompting for Multi-step Reasoning. - Yao Fu; Hao Peng 018; Ashish Sabharwal; Peter Clark; Tushar Khot (ICLR, 2023)

Decomposed Prompting: A Modular Approach for Solving Complex Tasks. - Tushar Khot; Harsh Trivedi; Matthew Finlayson; Yao Fu; Kyle Richardson 001; Peter Clark; Ashish Sabharwal (ICLR, 2023)

Specializing Smaller Language Models towards Multi-Step Reasoning. - Yao Fu; Hao Peng 018; Litu Ou; Ashish Sabharwal; Tushar Khot (ICML, 2023)

How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources. - Yizhong Wang; Hamish Ivison; Pradeep Dasigi; Jack Hessel; Tushar Khot; Khyathi Raghavi Chandu; David Wadden; Kelsey MacMillan; Noah A. Smith; Iz Beltagy; Hannaneh Hajishirzi (NeurIPS, 2023)

Improving Language Model Negotiation with Self-Play and In-Context Learning from AI Feedback. - Yao Fu; Hao Peng 018; Tushar Khot; Mirella Lapata (CoRR, 2023)

Chain-of-Thought Hub: A Continuous Effort to Measure Large Language Models' Reasoning Performance. - Yao Fu; Litu Ou; Mingyu Chen 011; Yuhao Wan; Hao Peng 018; Tushar Khot (CoRR, 2023)

SynerGPT: In-Context Learning for Personalized Drug Synergy Prediction and Drug Design. - Carl Edwards; Aakanksha Naik; Tushar Khot; Martin D. Burke; Heng Ji 001; Tom Hope (CoRR, 2023)

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models. - Aarohi Srivastava; Abhinav Rastogi; Abhishek Rao; Abu Awal Md Shoeb; Abubakar Abid; Adam Fisch; Adam R. Brown; Adam Santoro; Aditya Gupta; Adrià Garriga-Alonso; Agnieszka Kluska; Aitor Lewkowycz; et al. (Trans. Mach. Learn. Res., 2023)

OLMo: Accelerating the Science of Language Models. - Dirk Groeneveld; Iz Beltagy; Evan Pete Walsh; Akshita Bhagia; Rodney Kinney; Oyvind Tafjord; Ananya Harsh Jha; Hamish Ivison; Ian Magnusson; Yizhong Wang; Shane Arora; David Atkinson; et al. (ACL (1), 2024)

AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents. - Harsh Trivedi; Tushar Khot; Mareike Hartmann; Ruskin Manku; Vinty Dong; Edward Li; Shashank Gupta; Ashish Sabharwal; Niranjan Balasubramanian (ACL (1), 2024)

SUPER: Evaluating Agents on Setting Up and Executing Tasks from Research Repositories. - Ben Bogin; Kejuan Yang; Shashank Gupta; Kyle Richardson 001; Erin Bransom; Peter Clark; Ashish Sabharwal; Tushar Khot (EMNLP, 2024)

Bias Runs Deep: Implicit Reasoning Biases in Persona-Assigned LLMs. - Shashank Gupta; Vaishnavi Shrivastava; Ameet Deshpande; Ashwin Kalyan; Peter Clark; Ashish Sabharwal; Tushar Khot (ICLR, 2024)

ADaPT: As-Needed Decomposition and Planning with Language Models. - Archiki Prasad; Alexander Koller; Mareike Hartmann; Peter Clark; Ashish Sabharwal; Mohit Bansal; Tushar Khot (NAACL-HLT (Findings), 2024)

DiscoveryWorld: A Virtual Environment for Developing and Evaluating Automated Scientific Discovery Agents. - Peter A. Jansen; Marc-Alexandre Côté; Tushar Khot; Erin Bransom; Bhavana Dalvi Mishra; Bodhisattwa Prasad Majumder; Oyvind Tafjord; Peter Clark (NeurIPS, 2024)

OLMo: Accelerating the Science of Language Models. - Dirk Groeneveld; Iz Beltagy; Pete Walsh; Akshita Bhagia; Rodney Kinney; Oyvind Tafjord; Ananya Harsh Jha; Hamish Ivison; Ian Magnusson; Yizhong Wang; Shane Arora; David Atkinson; et al. (CoRR, 2024)

Husky: A Unified, Open-Source Language Agent for Multi-Step Reasoning. - Joongwon Kim; Bhargavi Paranjape; Tushar Khot; Hannaneh Hajishirzi (CoRR, 2024)

DiscoveryBench: Towards Data-Driven Discovery with Large Language Models. - Bodhisattwa Prasad Majumder; Harshit Surana; Dhruv Agarwal 003; Bhavana Dalvi Mishra; Abhijeetsingh Meena; Aryan Prakhar; Tirth Vora; Tushar Khot; Ashish Sabharwal; Peter Clark (ICLR, 2025)

Latent Factor Models Meets Instructions: Goal-conditioned Latent Factor Discovery without Task Supervision. - Zhouhang Xie; Tushar Khot; Bhavana Dalvi Mishra; Harshit Surana; Julian J. McAuley; Peter Clark; Bodhisattwa Prasad Majumder (NAACL (Long Papers), 2025)

Latent Factor Models Meets Instructions:Goal-conditioned Latent Factor Discovery without Task Supervision. - Zhouhang Xie; Tushar Khot; Bhavana Dalvi Mishra; Harshit Surana; Julian J. McAuley; Peter Clark; Bodhisattwa Prasad Majumder (CoRR, 2025)

Leveraging In-Context Learning for Language Model Agents. - Shivanshu Gupta; Sameer Singh 001; Ashish Sabharwal; Tushar Khot; Ben Bogin (CoRR, 2025)

MoNaCo: More Natural and Complex Questions for Reasoning Across Dozens of Documents. - Tomer Wolfson; Harsh Trivedi; Mor Geva; Yoav Goldberg; Dan Roth 001; Tushar Khot; Ashish Sabharwal; Reut Tsarfaty (CoRR, 2025)