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20212023
most citedMachine Learning Methods in Solving the Boolean Satisfiability Problem

26 citations · 39 across the 6 of their papers we have counts for

collaborators

6 papers

cs.AI2023★ 1 cited

HardSATGEN: Understanding the Difficulty of Hard SAT Formula Generation and A Strong Structure-Hardness-Aware Baseline

Yang Li, Xinyan Chen, Wenxuan Guo +6

Industrial SAT formula generation is a critical yet challenging task. Existing SAT generation approaches can hardly simultaneously capture the global structural properties and main…

q-bio.NC2022★ 4 cited

Distinguishing representational geometries with controversial stimuli: Bayesian experimental design and its application to face dissimilarity judgments

Tal Golan, Wenxuan Guo, Heiko H. Schütt +1

Comparing representations of complex stimuli in neural network layers to human brain representations or behavioral judgments can guide model development. However, even qualitativel…

cs.AI2022★ 26 cited

Machine Learning Methods in Solving the Boolean Satisfiability Problem

Wenxuan Guo, Junchi Yan, Hui-Ling Zhen +3

This paper reviews the recent literature on solving the Boolean satisfiability problem (SAT), an archetypal NP-complete problem, with the help of machine learning techniques. Despi…

cs.LG2022★ 1 cited

Online Learning to Transport via the Minimal Selection Principle

Wenxuan Guo, YoonHaeng Hur, Tengyuan Liang +1

Motivated by robust dynamic resource allocation in operations research, we study the \textit{Online Learning to Transport} (OLT) problem where the decision variable is a probabilit…

cs.CV2021★ 5 cited

ZARTS: On Zero-order Optimization for Neural Architecture Search

Xiaoxing Wang, Wenxuan Guo, Junchi Yan +2

Differentiable architecture search (DARTS) has been a popular one-shot paradigm for NAS due to its high efficiency. It introduces trainable architecture parameters to represent the…

stat.ME2021★ 2 cited

Reversible Gromov-Monge Sampler for Simulation-Based Inference

YoonHaeng Hur, Wenxuan Guo, Tengyuan Liang

This paper introduces a new simulation-based inference procedure to model and sample from multi-dimensional probability distributions given access to i.i.d.\ samples, circumventing…