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Related papers: The Bethe Partition Function of Log-supermodular G…

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We study the statistical complexity of estimating partition functions given sample access to a proposal distribution and an unnormalized density ratio for a target distribution. While partition function estimation is a classical problem,…

Machine Learning · Statistics 2026-03-02 Adam Block , Abhishek Shetty

We consider homogeneous factor models on uniformly sparse graph sequences converging locally to a (unimodular) random tree $T$, and study the existence of the free energy density $\phi$, the limit of the log-partition function divided by…

Probability · Mathematics 2013-12-17 Amir Dembo , Andrea Montanari , Nike Sun

Belief propagation is a fundamental message-passing algorithm for probabilistic reasoning and inference in graphical models. While it is known to be exact on trees, in most applications belief propagation is run on graphs with cycles.…

Machine Learning · Computer Science 2019-05-27 Frederic Koehler

We study Hilbert functions of certain non-reduced schemes A supported at finite sets of points in projective space, in particular, fat point schemes. We give combinatorially defined upper and lower bounds for the Hilbert function of A using…

Algebraic Geometry · Mathematics 2010-12-14 Susan Cooper , Brian Harbourne , Zach Teitler

Belief propagation is a fundamental message-passing algorithm for numerous applications in machine learning. It is known that belief propagation algorithm is exact on tree graphs. However, belief propagation is run on loopy graphs in most…

Machine Learning · Computer Science 2021-12-14 Yitao Chen , Deepanshu Vasal

Belief Propagation (BP) is a popular, distributed heuristic for performing MAP computations in Graphical Models. BP can be interpreted, from a variational perspective, as minimizing the Bethe Free Energy (BFE). BP can also be used to solve…

Artificial Intelligence · Computer Science 2013-05-20 Andrew Gelfand , Jinwoo Shin , Michael Chertkov

In this paper, we address the problem of finding a correspondence, or matching, between the functions of two programs in binary form, which is one of the most common task in binary diffing. We introduce a new formulation of this problem as…

Machine Learning · Computer Science 2022-01-03 Elie Mengin , Fabrice Rossi

In this paper, we investigate a class of submodular problems which in general are very hard. These include minimizing a submodular cost function under combinatorial constraints, which include cuts, matchings, paths, etc., optimizing a…

Machine Learning · Computer Science 2019-02-28 Rishabh Iyer , Jeff Bilmes

Weak submodularity is a natural relaxation of the diminishing return property, which is equivalent to submodularity. Weak submodularity has been used to show that many (monotone) functions that arise in practice can be efficiently maximized…

Data Structures and Algorithms · Computer Science 2020-09-24 Richard Santiago , Yuichi Yoshida

Combining the properties of monovariate internal functions as proposed in Kolmogorov superimposition theorem, in tandem with the bounds wielded by the multivariate formulation of Chebyshev inequality, a hybrid model is presented, that…

Computer Vision and Pattern Recognition · Computer Science 2011-06-03 Shriprakash Sinha , Gert J. ter Horst

We introduce several generalizations of classical computer science problems obtained by replacing simpler objective functions with general submodular functions. The new problems include submodular load balancing, which generalizes load…

Data Structures and Algorithms · Computer Science 2010-06-02 Zoya Svitkina , Lisa Fleischer

In the quantum theory, using the notion of partial supersymmetry, in which some, but not all, operators have superpartners we derive the Euler theorem in partition theory. The paraferminic partition function gives another identity in…

High Energy Physics - Theory · Physics 2007-05-23 Noureddine Chair

We construct the ($\beta$-deformed) partition function hierarchies with $W$-representations. Based on the $W$-representations, we analyze the superintegrability property and derive their character expansions with respect to the Schur…

High Energy Physics - Theory · Physics 2022-10-26 Rui Wang , Fan Liu , Chun-Hong Zhang , Wei-Zhong Zhao

It has recently been observed that the permanent of a non-negative square matrix, i.e., of a square matrix containing only non-negative real entries, can very well be approximated by solving a certain Bethe free energy function minimization…

Information Theory · Computer Science 2015-03-19 Pascal O. Vontobel

Let $X_1, \ldots, X_n$ be probability spaces, let $X$ be their direct product, let $\phi_1, \ldots, \phi_m: X \longrightarrow {\Bbb C}$ be random variables, each depending only on a few coordinates of a point $x=(x_1, \ldots, x_n)$, and let…

Probability · Mathematics 2024-06-28 Alexander Barvinok

In his famous 2007 paper on three dimensional quantum gravity, Witten defined candidates for the partition functions $$Z_k(q)=\sum_{n=-k}^{\infty}w_k(n)q^n$$ of potential extremal CFTs with central charges of the form $c=24k$. Although such…

Number Theory · Mathematics 2019-04-18 Ken Ono , Larry Rolen

We study the belief propagation algorithm for the graph bi-partitioning problem, i.e. the ground state of the ferromagnetic Ising model at a fixed magnetization. Application of a message passing scheme to a model with a fixed global…

Disordered Systems and Neural Networks · Physics 2010-06-16 P. Sulc , L. Zdeborova

A {\em cyclic graph} is a graph with at each vertex a cyclic order of the edges incident with it specified. We characterize which real-valued functions on the collection of cubic cyclic graphs are partition functions of a real vertex model…

Quantum Algebra · Mathematics 2016-08-02 Guus Regts , Alexander Schrijver , Bart Sevenster

We introduce the concept of quotient-convergence for sequences of submodular set functions, providing, among others, a new framework for the study of convergence of matroids through their rank functions. Extending the limit theory of…

Combinatorics · Mathematics 2024-06-17 Kristóf Bérczi , Márton Borbényi , László Lovász , László Márton Tóth

Inference in general Markov random fields (MRFs) is NP-hard, though identifying the maximum a posteriori (MAP) configuration of pairwise MRFs with submodular cost functions is efficiently solvable using graph cuts. Marginal inference,…

Machine Learning · Computer Science 2013-01-03 Adrian Weller , Tony Jebara
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