Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Exact Sampling with Integer Linear Programs and Random Perturbations
We consider the problem of sampling from a discrete probability distribution specified by a graphical model. Exact samples can, in principle, be obtained by computing the mode of the original model…
Hierarchical Semi-supervised Classification with Incomplete Class Hierarchies
In an entity classification task, topic or concept hierarchies are often incomplete. Previous work by Dalvi et al. has shown that in non-hierarchical semi-supervised classification tasks, the…
Keeping AI Legal
AI programs make numerous decisions on their own, lack transparency, and may change frequently. Hence, the article shows, unassisted human agents — such as auditors, accountants, inspectors, and…
Learning Continuous-Time Bayesian Networks in Relational Domains: A Non-Parametric Approach
Many real world applications in medicine, biology, communication networks, web mining, and economics, among others, involve modeling and learning structured stochastic processes that evolve over…
Selecting Near-Optimal Learners via Incremental Data Allocation
We study a novel machine learning (ML) problem setting of sequentially allocating small subsets of training data amongst a large set of classifiers. The goal is to select a classifier that will give…
Segment-Phrase Table for Semantic Segmentation, Visual Entailment and Paraphrasing
We introduce Segment-Phrase Table (SPT), a large collection of bijective associations between textual phrases and their corresponding segmentations. Leveraging recent progress in object recognition…
Solving Geometry Problems: Combining Text and Diagram Interpretation
This paper introduces GeoS, the first automated system to solve unaltered SAT geometry questions by combining text understanding and diagram interpretation. We model the problem of understanding…
Answering Elementary Science Questions by Constructing Coherent Scenes using Background Knowledge
Much of what we understand from text is not explicitly stated. Rather, the reader uses his/her knowledge to fill in gaps and create a coherent, mental picture or “scene” depicting what text appears…
BDD-Guided Clause Generation
Nogood learning is a critical component of Boolean satisfiability (SAT) solvers, and increasingly popular in the context of integer programming and constraint programming. We present a generic…
Discriminative and Consistent Similarities in Instance-Level Multiple Instance Learning
In this paper we present a bottom-up method to instance level Multiple Instance Learning (MIL) that learns to discover positive instances with globally constrained reasoning about local pairwise…