1 citations · 2 across the 12 of their papers we have counts for
14 papers
Weighted First-Order Model Counting over Ordered Domains
Jan Tóth, Qipeng Kuang, Kuncheng Zou +4
The Weighted First-Order Model Counting Problem (WFOMC) asks for the weighted sum of models of a first-order logical sentence over a domain. It is a fundamental problem in statisti…
Verifiable Counterfactual Supervision for Process Reward Models
Yinghui Chi, Yuanhong Wang
Process reward models (PRMs) require supervision that identifies not only whether a reasoning trajectory is correct, but also where the reasoning process first becomes unsupported…
CombEval: A Framework for Evaluating Combinatorial Counting in Large Language Models
Yuxu Zhou, Ondřej Kuželka, Yuyi Wang +2
We present CombEval, a dynamic benchmark for evaluating combinatorial counting in large language models. CombEval represents each problem as a typed Cofola specification over entit…
Solving Combinatorial Counting Problems with Weighted First-Order Model Counting
Yuanhong Wang, Juhua Pu, Yuxu Zhou +2
Combinatorial counting problems pervade artificial intelligence, statistics, and discrete mathematics. Whether the task is enumerating subsets, multisets, permutations, partitions,…
On Knowledge Compilation For Two-Variable First-Order Logic
Qiaolan Meng, Juhua Pu, Hongting Niu +3
Knowledge compilation transforms logical theories into circuit representations that support efficient reasoning. We study this problem for propositional groundings of FO2, the two-…
A Fast Model Counting Algorithm for Two-Variable Logic with Counting and Modulo Counting Quantifiers
Shixin Sun, Astrid Klipfel, Ondřej Kuželka +2
Weighted first-order model counting (WFOMC) is a central task in lifted probabilistic inference: It asks for the weighted sum of all models of a first-order sentence over a finite…
Tractable Weighted First-Order Model Counting with Bounded Treewidth Binary Evidence
Václav Kůla, Qipeng Kuang, Yuyi Wang +2
The Weighted First-Order Model Counting Problem (WFOMC) asks to compute the weighted sum of models of a given first-order logic sentence over a given domain. Conditioning WFOMC on…
Weighted First Order Model Counting for Two-variable Logic with Axioms on Two Relations
Qipeng Kuang, Václav Kůla, Ondřej Kuželka +2
The Weighted First-Order Model Counting Problem (WFOMC) asks to compute the weighted sum of models of a given first-order logic sentence over a given domain. The boundary between f…
Faster Lifting for Ordered Domains with Predecessor Relations
Kuncheng Zou, Jiahao Mai, Yonggang Zhang +4
We investigate lifted inference on ordered domains with predecessor relations, where the elements of the domain respect a total (cyclic) order, and every element has a distinct (cl…
Model Enumeration of Two-Variable Logic with Quadratic Delay Complexity
Qiaolan Meng, Juhua Pu, Hongting Niu +3
We study the model enumeration problem of the function-free, finite domain fragment of first-order logic with two variables (). Specifically, given an sentence and…
Bridging Weighted First Order Model Counting and Graph Polynomials
Qipeng Kuang, Ondřej Kuželka, Yuanhong Wang +1
The Weighted First-Order Model Counting Problem (WFOMC) asks to compute the weighted sum of models of a given first-order logic sentence over a given domain. It can be solved in ti…
Lifted Algorithms for Symmetric Weighted First-Order Model Sampling
Yuanhong Wang, Juhua Pu, Yuyi Wang +1
Weighted model counting (WMC) is the task of computing the weighted sum of all satisfying assignments (i.e., models) of a propositional formula. Similarly, weighted model sampling…
On Exact Sampling in the Two-Variable Fragment of First-Order Logic
Yuanhong Wang, Juhua Pu, Yuyi Wang +1
In this paper, we study the sampling problem for first-order logic proposed recently by Wang et al. -- how to efficiently sample a model of a given first-order sentence on a finite…
On the ERM Principle with Networked Data
Yuanhong Wang, Yuyi Wang, Xingwu Liu +1
Networked data, in which every training example involves two objects and may share some common objects with others, is used in many machine learning tasks such as learning to rank…