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- Tushar Khot, Ashish Sabharwal, Peter F. Clark
- AAAI
- 2018
We present a new dataset and model for textual entailment, derived from treating multiple-choice question-answering as an entailment problem. SCITAIL is the first entailment set that is created… (More)
- Paul Beame, Henry A. Kautz, Ashish Sabharwal
- J. Artif. Intell. Res.
- 2004
Effcient implementations of DPLL with the addition of clause learning are the fastest complete Boolean satisfiability solvers and can handle many significant real-world problems, such as… (More)
Algorithm portfolios aim to increase the robustness of our ability to solve problems efficiently. While recently proposed algorithm selection methods come ever closer to identifying the most… (More)
- Carla P. Gomes, Ashish Sabharwal, Bart Selman
- AAAI
- 2006
Model counting is the classical problem of computing the number of solutions of a given propositional formula. It vastly generalizes the NP-complete problem of propositional satisfiability, and hence… (More)
- Peter F. Clark, Isaac Cowhey, +4 authors Oyvind Tafjord
- ArXiv
- 2018
We present a new question set, text corpus, and baselines assembled to encourage AI research in advanced question answering. Together, these constitute the AI2 Reasoning Challenge (ARC), which… (More)
- Stefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman
- ICML
- 2013
Integration is affected by the curse of dimensionality and quickly becomes intractable as the dimensionality of the problem grows. We propose a randomized algorithm that, with high probability, gives… (More)
- Carla P. Gomes, Henry A. Kautz, Ashish Sabharwal, Bart Selman
- Handbook of Knowledge Representation
- 2008
Publisher Summary The past few years have seen enormous progress in the performance of Boolean satisfiability (SAT) solvers. Despite the worst-case exponential run time of all known algorithms,… (More)
- Carla P. Gomes, Jörg Hoffmann, Ashish Sabharwal, Bart Selman
- IJCAI
- 2007
We introduce a new technique for counting models of Boolean satisfiability problems. Our approach incorporates information obtained from sampling the solution space. Unlike previous approaches, our… (More)
- Daniel Khashabi, Tushar Khot, Ashish Sabharwal, Peter F. Clark, Oren Etzioni, Dan Roth
- IJCAI
- 2016
Answering science questions posed in natural language is an important AI challenge. Answering such questions often requires non-trivial inference and knowledge that goes beyond factoid retrieval.… (More)
- Stefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman
- NIPS
- 2013
We consider the problem of sampling from a probability distribution defined over a high-dimensional discrete set, specified for instance by a graphical model. We propose a sampling algorithm, called… (More)