1 code implementation • NeurIPS 2020 • Chirag Pabbaraju, Po-Wei Wang, J. Zico Kolter
Probabilistic inference in pairwise Markov Random Fields (MRFs), i. e. computing the partition function or computing a MAP estimate of the variables, is a foundational problem in probabilistic graphical models.
1 code implementation • NeurIPS 2020 • Po-Wei Wang, J. Zico Kolter
Modularity maximization has been a fundamental tool for understanding the community structure of a network, but the underlying optimization problem is nonconvex and NP-hard to solve.
no code implementations • ICLR 2020 • Po-Wei Wang, Daria Stepanova, Csaba Domokos, J. Zico Kolter
Rules over a knowledge graph (KG) capture interpretable patterns in data and can be used for KG cleaning and completion.
3 code implementations • 29 May 2019 • Po-Wei Wang, Priya L. Donti, Bryan Wilder, Zico Kolter
We demonstrate that by integrating this solver into end-to-end learning systems, we can learn the logical structure of challenging problems in a minimally supervised fashion.
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1 code implementation • 15 Dec 2018 • Po-Wei Wang, J. Zico Kolter
This paper proposes a new algorithm for solving MAX2SAT problems based on combining search methods with semidefinite programming approaches.
no code implementations • ICLR 2018 • Po-Wei Wang, J. Zico Kolter, Vijai Mohan, Inderjit S. Dhillon
Search engine users nowadays heavily depend on query completion and correction to shape their queries.
1 code implementation • 1 Jun 2017 • Po-Wei Wang, Wei-Cheng Chang, J. Zico Kolter
In this paper, we propose a low-rank coordinate descent approach to structured semidefinite programming with diagonal constraints.