Resolution Complexity of Independent Sets in Random Graphs. - Paul Beame; Russell Impagliazzo; Ashish Sabharwal (CCC, 2001)

Understanding the Power of Clause Learning. - Paul Beame; Henry A. Kautz; Ashish Sabharwal (IJCAI, 2003)

Using Problem Structure for Efficient Clause Learning. - Ashish Sabharwal; Paul Beame; Henry A. Kautz (SAT, 2003)

Towards Understanding and Harnessing the Potential of Clause Learning. - Paul Beame; Henry A. Kautz; Ashish Sabharwal (J. Artif. Intell. Res., 2004)

Bounded-Depth Frege Lower Bounds for Weaker Pigeonhole Principles. - Joshua Buresh-Oppenheim; Paul Beame; Toniann Pitassi; Ran Raz; Ashish Sabharwal (SIAM J. Comput., 2004)

SymChaff: A Structure-Aware Satisfiability Solver. - Ashish Sabharwal (AAAI, 2005)

Algorithmic applications of propositional proof complexity. - Ashish Sabharwal (2005)

Model Counting: A New Strategy for Obtaining Good Bounds. - Carla P. Gomes; Ashish Sabharwal; Bart Selman (AAAI, 2006)

Friends or Foes? An AI Planning Perspective on Abstraction and Search. - Jörg Hoffmann 001; Ashish Sabharwal; Carmel Domshlak (ICAPS, 2006)

Revisiting the Sequence Constraint. - Willem Jan van Hoeve; Gilles Pesant; Louis-Martin Rousseau; Ashish Sabharwal (CP, 2006)

Near-Uniform Sampling of Combinatorial Spaces Using XOR Constraints. - Carla P. Gomes; Ashish Sabharwal; Bart Selman (NIPS, 2006)

QBF Modeling: Exploiting Player Symmetry for Simplicity and Efficiency. - Ashish Sabharwal; Carlos Ansótegui; Carla P. Gomes; Justin W. Hart; Bart Selman (SAT, 2006)

The Impact of Network Topology on Pure Nash Equilibria in Graphical Games. - Bistra Dilkina; Carla P. Gomes; Ashish Sabharwal (AAAI, 2007)

Counting CSP Solutions Using Generalized XOR Constraints. - Carla P. Gomes; Willem Jan van Hoeve; Ashish Sabharwal; Bart Selman (AAAI, 2007)

Tradeoffs in the Complexity of Backdoor Detection. - Bistra Dilkina; Carla P. Gomes; Ashish Sabharwal (CP, 2007)

Connections in Networks: Hardness of Feasibility Versus Optimality. - Jon Conrad; Carla P. Gomes; Willem Jan van Hoeve; Ashish Sabharwal; Jordan Suter (CPAIOR, 2007)

Paper Retraction: On the Hardness of Embeddings Between Two Finite Metrics. - Matthew Cary; Atri Rudra; Ashish Sabharwal (ICALP, 2007)

From Sampling to Model Counting. - Carla P. Gomes; Jörg Hoffmann 001; Ashish Sabharwal; Bart Selman (IJCAI, 2007)

Short XORs for Model Counting: From Theory to Practice. - Carla P. Gomes; Jörg Hoffmann 001; Ashish Sabharwal; Bart Selman (SAT, 2007)

Sampling and Soundness: Can We Have Both? - Carla P. Gomes; Jörg Hoffmann 001; Ashish Sabharwal; Bart Selman (New Forms of Reasoning for the Semantic Web, 2007)

Survey Propagation Revisited. - Lukas Kroc; Ashish Sabharwal; Bart Selman (UAI, 2007)

The Resolution Complexity of Independent Sets and Vertex Covers in Random Graphs. - Paul Beame; Russell Impagliazzo; Ashish Sabharwal (Comput. Complex., 2007)

Connections in Networks: A Hybrid Approach. - Carla P. Gomes; Willem Jan van Hoeve; Ashish Sabharwal (CPAIOR, 2008)

Filtering Atmost1 on Pairs of Set Variables. - Willem Jan van Hoeve; Ashish Sabharwal (CPAIOR, 2008)

Tradeoffs in Backdoors: Inconsistency Detection, Dynamic Simplification, and Preprocessing. - Bistra Dilkina; Carla P. Gomes; Ashish Sabharwal (ISAIM, 2008)

Counting Solution Clusters in Graph Coloring Problems Using Belief Propagation. - Lukas Kroc; Ashish Sabharwal; Bart Selman (NIPS, 2008)

Satisfiability Solvers. - Carla P. Gomes; Henry A. Kautz; Ashish Sabharwal; Bart Selman (Handbook of Knowledge Representation, 2008)

Backdoors to Combinatorial Optimization: Feasibility and Optimality. - Bistra Dilkina; Carla P. Gomes; Yuri Malitsky; Ashish Sabharwal; Meinolf Sellmann (CPAIOR, 2009)

Integrating Systematic and Local Search Paradigms: A New Strategy for MaxSAT. - Lukas Kroc; Ashish Sabharwal; Carla P. Gomes; Bart Selman (IJCAI, 2009)

Message-passing and local heuristics as decimation strategies for satisfiability. - Lukas Kroc; Ashish Sabharwal; Bart Selman (SAC, 2009)

Backdoors in the Context of Learning. - Bistra Dilkina; Carla P. Gomes; Ashish Sabharwal (SAT, 2009)

Relaxed DPLL Search for MaxSAT. - Lukas Kroc; Ashish Sabharwal; Bart Selman (SAT, 2009)

New filtering algorithms for combinations of among constraints. - Willem Jan van Hoeve; Gilles Pesant; Louis-Martin Rousseau; Ashish Sabharwal (Constraints An Int. J., 2009)

SymChaff: exploiting symmetry in a structure-aware satisfiability solver. - Ashish Sabharwal (Constraints An Int. J., 2009)

Friends or Foes? On Planning as Satisfiability and Abstract CNF Encodings. - Carmel Domshlak; Jörg Hoffmann 001; Ashish Sabharwal (J. Artif. Intell. Res., 2009)

Exploiting Runtime Variation in Complete Solvers. - Carla P. Gomes; Ashish Sabharwal (Handbook of Satisfiability, 2009)

Model Counting. - Carla P. Gomes; Ashish Sabharwal; Bart Selman (Handbook of Satisfiability, 2009)

Incomplete Algorithms. - Henry A. Kautz; Ashish Sabharwal; Bart Selman (Handbook of Satisfiability, 2009)

Approximate Inference for Clusters in Solution Spaces. - Lukas Kroc; Ashish Sabharwal; Bart Selman (Abstraction, Reformulation, and Approximation, 2010)

Preface. - Gregory M. Provan; Ashish Sabharwal (Abstraction, Reformulation, and Approximation, 2010)

On Adversarial Search Spaces and Sampling-Based Planning. - Raghuram Ramanujan; Ashish Sabharwal; Bart Selman (ICAPS, 2010)

An Empirical Study of Optimization for Maximizing Diffusion in Networks. - Kiyan Ahmadizadeh; Bistra Dilkina; Carla P. Gomes; Ashish Sabharwal (CP, 2010)

An Empirical Study of Optimal Noise and Runtime Distributions in Local Search. - Lukas Kroc; Ashish Sabharwal; Bart Selman (SAT, 2010)

Understanding Sampling Style Adversarial Search Methods. - Raghuram Ramanujan; Ashish Sabharwal; Bart Selman (UAI, 2010)

Maximizing the Spread of Cascades Using Network Design. - Daniel Sheldon; Bistra Dilkina; Adam N. Elmachtoub; Ryan Finseth; Ashish Sabharwal; Jon Conrad; Carla P. Gomes; David B. Shmoys; William Allen; Ole Amundsen; William Vaughan (UAI, 2010)

Reports of the AAAI 2010 Conference Workshops. - David W. Aha; Mark S. Boddy; Vadim Bulitko; Artur S. d'Avila Garcez; Prashant Doshi; Stefan Edelkamp; Christopher W. Geib; Piotr J. Gmytrasiewicz; Robert P. Goldman; Pascal Hitzler; Charles L. Isbell Jr.; Darsana P. Josyula; et al. (AI Mag., 2010)

Predicting direct protein interactions from affinity purification mass spectrometry data. - Ethan Kim; Ashish Sabharwal; Adrian Vetta; Mathieu Blanchette (Algorithms Mol. Biol., 2010)

Floodlight illumination of infinite wedges. - Matthew Cary; Atri Rudra; Ashish Sabharwal; Erik Vee (Comput. Geom., 2010)

A General Nogood-Learning Framework for Pseudo-Boolean Multi-Valued SAT. - Siddhartha Jain 001; Ashish Sabharwal; Meinolf Sellmann (AAAI, 2011)

Algorithm Selection and Scheduling. - Serdar Kadioglu; Yuri Malitsky; Ashish Sabharwal; Horst Samulowitz; Meinolf Sellmann (CP, 2011)

Constraint Reasoning and Kernel Clustering for Pattern Decomposition with Scaling. - Ronan LeBras; Theodoros Damoulas; John M. Gregoire; Ashish Sabharwal; Carla P. Gomes; R. Bruce van Dover (CP, 2011)

Accelerated Adaptive Markov Chain for Partition Function Computation. - Stefano Ermon; Carla P. Gomes; Ashish Sabharwal; Bart Selman (NIPS, 2011)

Non-Model-Based Algorithm Portfolios for SAT. - Yuri Malitsky; Ashish Sabharwal; Horst Samulowitz; Meinolf Sellmann (SAT, 2011)

S. Russell, P. Norvig, Artificial Intelligence: A Modern Approach, Third Edition. - Ashish Sabharwal; Bart Selman (Artif. Intell., 2011)

Leveraging belief propagation, backtrack search, and statistics for model counting. - Lukas Kroc; Ashish Sabharwal; Bart Selman (Ann. Oper. Res., 2011)

Parallel SAT Solver Selection and Scheduling. - Yuri Malitsky; Ashish Sabharwal; Horst Samulowitz; Meinolf Sellmann (CP, 2012)

Guiding Combinatorial Optimization with UCT. - Ashish Sabharwal; Horst Samulowitz; Chandra Reddy (CPAIOR, 2012)

Density Propagation and Improved Bounds on the Partition Function. - Stefano Ermon; Carla P. Gomes; Ashish Sabharwal; Bart Selman (NIPS, 2012)

SatX10: A Scalable Plug&Play Parallel SAT Framework - (Tool Presentation). - Bard Bloom; David Grove; Benjamin Herta; Ashish Sabharwal; Horst Samulowitz; Vijay A. Saraswat (SAT, 2012)

Augmenting Clause Learning with Implied Literals - (Poster Presentation). - Arie Matsliah; Ashish Sabharwal; Horst Samulowitz (SAT, 2012)

Learning Back-Clauses in SAT - (Poster Presentation). - Ashish Sabharwal; Horst Samulowitz; Meinolf Sellmann (SAT, 2012)

Large Landscape Conservation - Synthetic and Real-World Datasets. - Bistra Dilkina; Katherine J. Lai; Ronan LeBras; Yexiang Xue; Carla P. Gomes; Ashish Sabharwal; Jordan Suter; Kevin S. McKelvey; Michael K. Schwartz; Claire A. Montgomery (AAAI, 2013)

Resolution and Parallelizability: Barriers to the Efficient Parallelization of SAT Solvers. - George Katsirelos; Ashish Sabharwal; Horst Samulowitz; Laurent Simon 001 (AAAI, 2013)

Automated Design of Search with Composability. - Ashish Sabharwal; Horst Samulowitz; Tom Schrijvers; Peter J. Stuckey; Guido Tack (AAAI (Late-Breaking Developments), 2013)

Stronger Inference through Implied Literals from Conflicts and Knapsack Covers. - Tobias Achterberg; Ashish Sabharwal; Horst Samulowitz (CPAIOR, 2013)

Taming the Curse of Dimensionality: Discrete Integration by Hashing and Optimization. - Stefano Ermon; Carla P. Gomes; Ashish Sabharwal; Bart Selman (ICML (2), 2013)

Algorithm Portfolios Based on Cost-Sensitive Hierarchical Clustering. - Yuri Malitsky; Ashish Sabharwal; Horst Samulowitz; Meinolf Sellmann (IJCAI, 2013)

Boosting Sequential Solver Portfolios: Knowledge Sharing and Accuracy Prediction. - Yuri Malitsky; Ashish Sabharwal; Horst Samulowitz; Meinolf Sellmann (LION, 2013)

Embed and Project: Discrete Sampling with Universal Hashing. - Stefano Ermon; Carla P. Gomes; Ashish Sabharwal; Bart Selman (NIPS, 2013)

Snappy: A Simple Algorithm Portfolio. - Horst Samulowitz; Chandra Reddy; Ashish Sabharwal; Meinolf Sellmann (SAT, 2013)

Optimization With Parity Constraints: From Binary Codes to Discrete Integration. - Stefano Ermon; Carla P. Gomes; Ashish Sabharwal; Bart Selman (UAI, 2013)

Non-Restarting SAT Solvers with Simple Preprocessing Can Efficiently Simulate Resolution. - Paul Beame; Ashish Sabharwal (AAAI, 2014)

Designing Fast Absorbing Markov Chains. - Stefano Ermon; Carla P. Gomes; Ashish Sabharwal; Bart Selman (AAAI, 2014)

Insights into Parallelism with Intensive Knowledge Sharing. - Ashish Sabharwal; Horst Samulowitz (CP, 2014)

Parallel Combinatorial Optimization with Decision Diagrams. - David Bergman; André A. Ciré; Ashish Sabharwal; Horst Samulowitz; Vijay A. Saraswat; Willem Jan van Hoeve (CPAIOR, 2014)

Low-density Parity Constraints for Hashing-Based Discrete Integration. - Stefano Ermon; Carla P. Gomes; Ashish Sabharwal; Bart Selman (ICML, 2014)

Tradeoffs in the complexity of backdoors to satisfiability: dynamic sub-solvers and learning during search. - Bistra Dilkina; Carla P. Gomes; Ashish Sabharwal (Ann. Math. Artif. Intell., 2014)

BDD-Guided Clause Generation. - Brian Kell; Ashish Sabharwal; Willem-Jan van Hoeve (CPAIOR, 2015)

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

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

Parsing Algebraic Word Problems into Equations. - Rik Koncel-Kedziorski; Hannaneh Hajishirzi; Ashish Sabharwal; Oren Etzioni; Siena Dumas Ang (Trans. Assoc. Comput. Linguistics, 2015)

DPLL: The Core of Modern Satisfiability Solvers. - Donald W. Loveland; Ashish Sabharwal; Bart Selman (Martin Davis on Computability, Computational Logic, and Mathematical Foundations, 2016)

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)

Exact Sampling with Integer Linear Programs and Random Perturbations. - Carolyn Kim; Ashish Sabharwal; Stefano Ermon (AAAI, 2016)

Selecting Near-Optimal Learners via Incremental Data Allocation. - Ashish Sabharwal; Horst Samulowitz; Gerald Tesauro (AAAI, 2016)

Closing the Gap Between Short and Long XORs for Model Counting. - Shengjia Zhao; Sorathan Chaturapruek; Ashish Sabharwal; Stefano Ermon (AAAI, 2016)

Beyond Parity Constraints: Fourier Analysis of Hash Functions for Inference. - Tudor Achim; Ashish Sabharwal; Stefano Ermon (ICML, 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)

Adaptive Concentration Inequalities for Sequential Decision Problems. - Shengjia Zhao; Enze Zhou; Ashish Sabharwal; Stefano Ermon (NIPS, 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)

How Good Are My Predictions? Efficiently Approximating Precision-Recall Curves for Massive Datasets. - Ashish Sabharwal; Hanie Sedghi (UAI, 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)

Approximate Inference via Weighted Rademacher Complexity. - Jonathan Kuck; Ashish Sabharwal; Stefano Ermon (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)

Expanding Holographic Embeddings for Knowledge Completion. - Yexiang Xue; Yang Yuan; Zhitian Xu; Ashish Sabharwal (NeurIPS, 2018)

Adaptive Stratified Sampling for Precision-Recall Estimation. - Ashish Sabharwal; Yexiang Xue (UAI, 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)

Knowledge Completion for Generics Using Guided Tensor Factorization. - Hanie Sedghi; Ashish Sabharwal (Trans. Assoc. Comput. Linguistics, 2018)

QUAREL: A Dataset and Models for Answering Questions about Qualitative Relationships. - Oyvind Tafjord; Peter Clark; Matt Gardner 001; Wen-tau Yih; Ashish Sabharwal (AAAI, 2019)

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)

Approximating the Permanent by Sampling from Adaptive Partitions. - Jonathan Kuck; Tri Dao; Hamid Rezatofighi; Ashish Sabharwal; Stefano Ermon (NeurIPS, 2019)

Adaptive Hashing for Model Counting. - Jonathan Kuck; Tri Dao; Shenjia Zhao; Burak Bartan; Ashish Sabharwal; Stefano Ermon (UAI, 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)

AdaWISH: Faster Discrete Integration via Adaptive Quantiles. - Fan Ding; Hanjing Wang; Ashish Sabharwal; Yexiang Xue (CoRR, 2019)

Not All Claims are Created Equal: Choosing the Right Approach to Assess Your Hypotheses. - Erfan Sadeqi Azer; Daniel Khashabi; Ashish Sabharwal; Dan Roth 001 (CoRR, 2019)

What Does My QA Model Know? Devising Controlled Probes using Expert Knowledge. - Kyle Richardson 001; Ashish Sabharwal (CoRR, 2019)

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

Probing Natural Language Inference Models through Semantic Fragments. - Kyle Richardson 001; Hai Hu; Lawrence S. Moss; Ashish Sabharwal (AAAI, 2020)

Not All Claims are Created Equal: Choosing the Right Statistical Approach to Assess Hypotheses. - Erfan Sadeqi Azer; Daniel Khashabi; Ashish Sabharwal; Dan Roth 001 (ACL, 2020)

Towards Efficient Discrete Integration via Adaptive Quantile Queries. - Fan Ding; Hanjing Wang; Ashish Sabharwal; Yexiang Xue (ECAI, 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)

Adversarial Filters of Dataset Biases. - Ronan Le Bras 001; Swabha Swayamdipta; Chandra Bhagavatula; Rowan Zellers; Matthew E. Peters; Ashish Sabharwal; Yejin Choi 001 (ICML, 2020)

Belief Propagation Neural Networks. - Jonathan Kuck; Shuvam Chakraborty; Hao Tang 008; Rachel Luo; Jiaming Song; Ashish Sabharwal; Stefano Ermon (NeurIPS, 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)

What Does My QA Model Know? Devising Controlled Probes using Expert. - Kyle Richardson 001; Ashish Sabharwal (Trans. Assoc. Comput. Linguistics, 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)

How much coffee was consumed during EMNLP 2019? Fermi Problems: A New Reasoning Challenge for AI. - Ashwin Kalyan; Abhinav Kumar; Arjun Chandrasekaran; Ashish Sabharwal; Peter Clark (EMNLP (1), 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)

Exploiting Runtime Variation in Complete Solvers. - Carla P. Gomes; Ashish Sabharwal (Handbook of Satisfiability, 2021)

Model Counting. - Carla P. Gomes; Ashish Sabharwal; Bart Selman (Handbook of Satisfiability, 2021)

Incomplete Algorithms. - Henry A. Kautz; Ashish Sabharwal; Bart Selman (Handbook of Satisfiability, 2021)

Pushing the Limits of Rule Reasoning in Transformers through Natural Language Satisfiability. - Kyle Richardson 001; Ashish Sabharwal (AAAI, 2022)

Multi-Modal Answer Validation for Knowledge-Based VQA. - Jialin Wu; Jiasen Lu; Ashish Sabharwal; Roozbeh Mottaghi (AAAI, 2022)

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

Breakpoint Transformers for Modeling and Tracking Intermediate Beliefs. - Kyle Richardson 001; Ronen Tamari; Oren Sultan; Dafna Shahaf; Reut Tsarfaty; Ashish Sabharwal (EMNLP, 2022)

What Makes Instruction Learning Hard? An Investigation and a New Challenge in a Synthetic Environment. - Matthew Finlayson; Kyle Richardson 001; Ashish Sabharwal; Peter Clark (EMNLP, 2022)

LILA: A Unified Benchmark for Mathematical Reasoning. - Swaroop Mishra; Matthew Finlayson; Pan Lu; Leonard Tang; Sean Welleck; Chitta Baral; Tanmay Rajpurohit; Oyvind Tafjord; Ashish Sabharwal; Peter Clark; Ashwin Kalyan (EMNLP, 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)

Log-Precision Transformers are Constant-Depth Uniform Threshold Circuits. - William Merrill; Ashish Sabharwal (CoRR, 2022)

Transformers Implement First-Order Logic with Majority Quantifiers. - William Merrill; Ashish Sabharwal (CoRR, 2022)

DISCO: Distilling Phrasal Counterfactuals with Large Language Models. - Zeming Chen 001; Qiyue Gao; Kyle Richardson 001; Antoine Bosselut; Ashish Sabharwal (CoRR, 2022)

Saturated Transformers are Constant-Depth Threshold Circuits. - William Merrill; Ashish Sabharwal; Noah A. Smith (Trans. Assoc. Comput. Linguistics, 2022)

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

DISCO: Distilling Counterfactuals with Large Language Models. - Zeming Chen 001; Qiyue Gao; Antoine Bosselut; Ashish Sabharwal; Kyle Richardson 001 (ACL (1), 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)

IfQA: A Dataset for Open-domain Question Answering under Counterfactual Presuppositions. - Wenhao Yu 002; Meng Jiang 001; Peter Clark; Ashish Sabharwal (EMNLP, 2023)

Language Models with Rationality. - Nora Kassner; Oyvind Tafjord; Ashish Sabharwal; Kyle Richardson 001; Hinrich Schütze; Peter Clark (EMNLP, 2023)

Increasing Probability Mass on Answer Choices Does Not Always Improve Accuracy. - Sarah Wiegreffe; Matthew Finlayson; Oyvind Tafjord; Peter Clark; Ashish Sabharwal (EMNLP, 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)

A Logic for Expressing Log-Precision Transformers. - William Merrill; Ashish Sabharwal (NeurIPS, 2023)

Improving Language Models via Plug-and-Play Retrieval Feedback. - Wenhao Yu 002; Zhihan Zhang 001; Zhenwen Liang; Meng Jiang 001; Ashish Sabharwal (CoRR, 2023)

Attentiveness to Answer Choices Doesn't Always Entail High QA Accuracy. - Sarah Wiegreffe; Matthew Finlayson; Oyvind Tafjord; Peter Clark; Ashish Sabharwal (CoRR, 2023)

The Parallelism Tradeoff: Limitations of Log-Precision Transformers. - William Merrill; Ashish Sabharwal (Trans. Assoc. Comput. Linguistics, 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)

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)

Closing the Curious Case of Neural Text Degeneration. - Matthew Finlayson; John Hewitt; Alexander Koller; Swabha Swayamdipta; Ashish Sabharwal (ICLR, 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)

The Expressive Power of Transformers with Chain of Thought. - William Merrill; Ashish Sabharwal (ICLR, 2024)

Position: Data-driven Discovery with Large Generative Models. - Bodhisattwa Prasad Majumder; Harshit Surana; Dhruv Agarwal 003; Sanchaita Hazra; Ashish Sabharwal; Peter Clark (ICML, 2024)

The Illusion of State in State-Space Models. - William Merrill; Jackson Petty; Ashish Sabharwal (ICML, 2024)

Leveraging Code to Improve In-Context Learning for Semantic Parsing. - Ben Bogin; Shivanshu Gupta; Peter Clark; Ashish Sabharwal (NAACL-HLT, 2024)

QualEval: Qualitative Evaluation for Model Improvement. - Vishvak Murahari; Ameet Deshpande; Peter Clark; Tanmay Rajpurohit; Ashish Sabharwal; Karthik Narasimhan; Ashwin Kalyan (NAACL-HLT, 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)

Data-driven Discovery with Large Generative Models. - Bodhisattwa Prasad Majumder; Harshit Surana; Dhruv Agarwal 003; Sanchaita Hazra; Ashish Sabharwal; Peter Clark (CoRR, 2024)

Answer, Assemble, Ace: Understanding How Transformers Answer Multiple Choice Questions. - Sarah Wiegreffe; Oyvind Tafjord; Yonatan Belinkov; Hannaneh Hajishirzi; Ashish Sabharwal (CoRR, 2024)

Understanding the Logic of Direct Preference Alignment through Logic. - Kyle Richardson 001; Vivek Srikumar; Ashish Sabharwal (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)

Answer, Assemble, Ace: Understanding How LMs Answer Multiple Choice Questions. - Sarah Wiegreffe; Oyvind Tafjord; Yonatan Belinkov; Hannaneh Hajishirzi; Ashish Sabharwal (ICLR, 2025)

Transformers as Transducers. - Lena Strobl; Dana Angluin; David Chiang 001; Jonathan Rawski; Ashish Sabharwal (Trans. Assoc. Comput. Linguistics, 2025)

ZebraLogic: On the Scaling Limits of LLMs for Logical Reasoning. - Bill Yuchen Lin; Ronan Le Bras 001; Kyle Richardson 001; Ashish Sabharwal; Radha Poovendran; Peter Clark; Yejin Choi 001 (CoRR, 2025)

A Little Depth Goes a Long Way: The Expressive Power of Log-Depth Transformers. - William Merrill; Ashish Sabharwal (CoRR, 2025)

Exact Expressive Power of Transformers with Padding. - William Merrill; Ashish Sabharwal (CoRR, 2025)

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

Open-ended Scientific Discovery via Bayesian Surprise. - Dhruv Agarwal 003; Bodhisattwa Prasad Majumder; Reece Adamson; Megha Chakravorty; Satvika Reddy Gavireddy; Aditya Parashar; Harshit Surana; Bhavana Dalvi Mishra; Andrew McCallum; Ashish Sabharwal; Peter Clark (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)