Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Semantic Parsing to Probabilistic Programs for Situated Question Answering
Situated question answering is the problem of answering questions about an environment such as an image or diagram. This problem requires jointly interpreting a question and an environment using…
What's in an Explanation? Characterizing Knowledge and Inference Requirements for Elementary Science Exams
QA systems have been making steady advances in the challenging elementary science exam domain. In this work, we develop an explanation-based analysis of knowledge and inference requirements, which…
My Computer is an Honor Student — but how Intelligent is it? Standardized Tests as a Measure of AI
Given the well-known limitations of the Turing Test, there is a need for objective tests to both focus attention on, and measure progress towards, the goals of AI. In this paper we argue that…
Closing the Gap Between Short and Long XORs for Model Counting
Many recent algorithms for approximate model counting are based on a reduction to combinatorial searches over random subsets of the space defined by parity or XOR constraints. Long parity…
Combining Retrieval, Statistics, and Inference to Answer Elementary Science Questions
What capabilities are required for an AI system to pass standard 4th Grade Science Tests? Previous work has examined the use of Markov Logic Networks (MLNs) to represent the requisite background…
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…
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…
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…