565 citations · 2.3k across the 68 of their papers we have counts for
3 papers · 1 filter
Selecting Near-Optimal Learners via Incremental Data Allocation
Ashish Sabharwal, Horst Samulowitz, Gerald Tesauro
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 classif…
Closing the Gap Between Short and Long XORs for Model Counting
Shengjia Zhao, Sorathan Chaturapruek, Ashish Sabharwal +1
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…
Markov Logic Networks for Natural Language Question Answering
Tushar Khot, Niranjan Balasubramanian, Eric Gribkoff +3
Our goal is to answer elementary-level science questions using knowledge extracted automatically from science textbooks, expressed in a subset of first-order logic. Given the incom…