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Two commonly arising computational tasks in Bayesian learning are Optimization (Maximum A Posteriori estimation) and Sampling (from the posterior distribution). In the convex case these two problems are efficiently reducible to each other.…

Machine Learning · Computer Science 2019-11-07 Kunal Talwar

In this paper we provide proofs of two new theorems that provide a broad class of partition inequalities and that illustrate a na\"ive version of Andrews' anti-telescoping technique quite well. These new theorems also put to rest any notion…

Number Theory · Mathematics 2013-03-12 Alexander Berkovich , Keith Grizzell

In problems involving the allocation of a single non-disposable commodity, we study rules defined on a general domain of preferences requiring only that each preference exhibit a unique global maximum. Our focus is on rules that satisfy a…

Theoretical Economics · Economics 2025-12-18 R. Pablo Arribillaga , Agustin G. Bonifacio

We introduce proper display calculi for intuitionistic, bi-intuitionistic and classical linear logics with exponentials, which are sound, complete, conservative, and enjoy cut-elimination and subformula property. Based on the same design,…

Logic · Mathematics 2016-11-15 Giuseppe Greco , Alessandra Palmigiano

We propose a novel foundation for calculus that focuses on the notion of approximations while avoiding the use of limits altogether. Continuity is defined as approximation at a point, while differentiability is defined as approximation with…

History and Overview · Mathematics 2025-10-27 Michael P. Lamoureux , Matt Yedlin

Representation learning constructs low-dimensional representations to summarize essential features of high-dimensional data. This learning problem is often approached by describing various desiderata associated with learned representations;…

Machine Learning · Statistics 2022-02-14 Yixin Wang , Michael I. Jordan

One advantage of paraconsistent logic is that it can deal with inconsistencies without making the system trivial. However, unlike classical propositional calculus, its deductive system is limited, and the meaning of paraconsistent negation…

Logic · Mathematics 2025-10-14 Oscar Ramírez

Machine learning methods are widely and successfully used for probabilistic wind power forecasting, yet the pervasive issue of missing values (e.g., due to sensor faults or communication outages) has received limited attention. The…

Machine Learning · Computer Science 2025-12-04 Honglin Wen , Pierre Pinson , Jie Gu , Zhijian Jin

Reflection principles (or dually speaking, compactness principles) often give rise to combinatorial guessing principles. Uniformization properties, on the other hand, are examples of anti-guessing principles. We discuss the tension and the…

Logic · Mathematics 2021-10-07 Jing Zhang

We study the non-uniqueness of factorizations of non zero-divisors into atoms (irreducibles) in noncommutative rings. To do so, we extend concepts from the commutative theory of non-unique factorizations to a noncommutative setting. Several…

Rings and Algebras · Mathematics 2015-09-03 Nicholas R. Baeth , Daniel Smertnig

Recently, Miller and Wu introduced the positive $\lambda$-calculus, a call-by-value $\lambda$-calculus with sharing obtained by assigning proof terms to the positively polarized focused proofs for minimal intuitionistic logic. The positive…

Logic in Computer Science · Computer Science 2024-12-18 Beniamino Accattoli , Jui-Hsuan Wu

Learning correlations from data forms the foundation of today's machine learning (ML) and artificial intelligence research. While contemporary methods enable the automatic discovery of complex patterns, they are prone to failure when…

Machine Learning · Computer Science 2026-05-05 Samuel J. Bell , Skyler Wang

We present a new kind of Lagrangian duality theory for set-valued convex optimization problems whose objective and constraint maps are defined between preordered normed spaces. The theory is accomplished by introducing a new set-valued…

Optimization and Control · Mathematics 2024-01-17 Fernando García-Castaño , M. A. Melguizo Padial

We consider the differentiation of the value function for parametric optimization problems. Such problems are ubiquitous in Machine Learning applications such as structured support vector machines, matrix factorization and min-min or…

Optimization and Control · Mathematics 2020-12-29 Sheheryar Mehmood , Peter Ochs

With machine learning models being increasingly used to aid decision making even in high-stakes domains, there has been a growing interest in developing interpretable models. Although many supposedly interpretable models have been proposed,…

Artificial Intelligence · Computer Science 2021-08-17 Forough Poursabzi-Sangdeh , Daniel G. Goldstein , Jake M. Hofman , Jennifer Wortman Vaughan , Hanna Wallach

In this paper we present an abstraction-refinement approach to Satisfiability Modulo the theory of transcendental functions, such as exponentiation and trigonometric functions. The transcendental functions are represented as uninterpreted…

Logic in Computer Science · Computer Science 2018-01-29 Alessandro Cimatti , Alberto Griggio , Ahmed Irfan , Marco Roveri , Roberto Sebastiani

We introduce a refinement of the classical Liouville function to primes in arithmetic progressions. Using this, we discover new biases in the appearances of primes in a given arithmetic progression in the prime factorizations of integers.…

Number Theory · Mathematics 2020-07-24 Peter Humphries , Snehal M. Shekatkar , Tian An Wong

This paper is concerned with the asymptotic behavior of sums of terms which are a test function f evaluated at successive increments of a discretely sampled semimartingale. Typically the test function is a power function (when the power is…

Probability · Mathematics 2007-05-23 Jean Jacod

Following attempts at an analytic proof of the Pentagonal Number Theorem, we report on the discovery of a general principle leading to an unexpected cancellation of oscillating sums. After stating the motivation, and our theorem, we apply…

Number Theory · Mathematics 2022-06-24 Ernie Croot , Hamed Mousavi

The unmatched ability of Deep Neural Networks in capturing complex patterns in large and noisy datasets is often associated with their large hypothesis space, and consequently to the vast amount of parameters that characterize model…

Machine Learning · Computer Science 2026-02-25 Enrico Ballini , Luca Muscarnera , Alessio Fumagalli , Anna Scotti , Francesco Regazzoni
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