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In the field of categorical probability, one uses concepts and techniques from category theory, such as monads and monoidal categories, to study the structures of probability and statistics. In this paper, we connect some ideas from…

Category Theory · Mathematics 2025-02-24 Mika Bohinen , Paolo Perrone

We explore asynchronous programming with algebraic effects. We complement their conventional synchronous treatment by showing how to naturally also accommodate asynchrony within them, namely, by decoupling the execution of operation calls…

Programming Languages · Computer Science 2020-11-17 Danel Ahman , Matija Pretnar

Classical methods such as Principal Component Analysis (PCA) and Canonical Correlation Analysis (CCA) are ubiquitous in statistics. However, these techniques are only able to reveal linear relationships in data. Although nonlinear variants…

Machine Learning · Statistics 2014-05-14 David Lopez-Paz , Suvrit Sra , Alex Smola , Zoubin Ghahramani , Bernhard Schölkopf

We investigate completions of partial combinatory algebras (pcas), in particular of Kleene's second model $\mathcal{K}_2$ and generalizations thereof. We consider weak and strong notions of embeddability and completion that have been…

Logic in Computer Science · Computer Science 2025-06-11 Sebastiaan A. Terwijn

There is a recent interest for the verification of monadic programs using proof assistants. This line of research raises the question of the integration of monad transformers, a standard technique to combine monads. In this paper, we extend…

Logic in Computer Science · Computer Science 2021-07-20 Reynald Affeldt , David Nowak

Principal component analysis (PCA) is a widely used method for data processing, such as for dimension reduction and visualization. Standard PCA is known to be sensitive to outliers, and thus, various robust PCA methods have been proposed.…

Machine Learning · Statistics 2020-08-11 Keishi Sando , Hideitsu Hino

We introduce a notion of signature whose sorts form a direct category, and study computads for such signatures. Algebras for such a signature are presheaves with an interpretation of every function symbol of the signature, and we describe…

Category Theory · Mathematics 2024-11-06 Ioannis Markakis

Many areas of machine learning and science involve large linear algebra problems, such as eigendecompositions, solving linear systems, computing matrix exponentials, and trace estimation. The matrices involved often have Kronecker,…

Machine Learning · Computer Science 2023-11-30 Andres Potapczynski , Marc Finzi , Geoff Pleiss , Andrew Gordon Wilson

Dependent types provide a lightweight and modular means to integrate programming and formal program verification. In particular, the types of programs written in dependently typed programming languages (Agda, Idris, F*, etc.) can be used to…

Logic in Computer Science · Computer Science 2017-10-10 Danel Ahman

When using cylindrical algebraic decomposition (CAD) to solve a problem with respect to a set of polynomials, it is likely not the signs of those polynomials that are of paramount importance but rather the truth values of certain quantifier…

Symbolic Computation · Computer Science 2016-02-23 Russell Bradford , James H. Davenport , Matthew England , Scott McCallum , David Wilson

The past decade has seen a significant interest in learning tractable probabilistic representations. Arithmetic circuits (ACs) were among the first proposed tractable representations, with some subsequent representations being instances of…

Artificial Intelligence · Computer Science 2017-08-25 Arthur Choi , Adnan Darwiche

Computational philosophy is the use of mechanized computational techniques to unearth philosophical insights that are either difficult or impossible to find using traditional philosophical methods. Computational metaphysics is computational…

Logic in Computer Science · Computer Science 2019-08-13 Daniel Kirchner , Christoph Benzmüller , Edward N. Zalta

Measurable cones, with linear and measurable functions as morphisms, are a model of intuitionistic linear logic and of call-by-name probabilistic PCF which accommodates "continuous data types" such as the real line. So far however, they…

Logic in Computer Science · Computer Science 2025-01-15 Thomas Ehrhard , Guillaume Geoffroy

We employ the notions of `sequential function' and `interrogation' (dialogue) in order to define new partial combinatory algebra structures on sets of functions. These structures are analyzed using J. Longley's preorder-enriched category of…

Logic · Mathematics 2009-05-19 Jaap van Oosten

Algebraic Combinatorics originated in Algebra and Representation Theory, studying their discrete objects and integral quantities via combinatorial methods which have since developed independent and self-contained lives and brought us some…

Combinatorics · Mathematics 2023-07-03 Greta Panova

We formulate a framework for describing behaviour of effectful higher-order recursive programs. Examples of effects are implemented using effect operations, and include: execution cost, nondeterminism, global store and interaction with a…

Logic in Computer Science · Computer Science 2021-12-30 Niccolò Veltri , Niels F. W. Voorneveld

We explore the possibility of extending Mardare et al. quantitative algebras to the structures which naturally emerge from Combinatory Logic and the lambda-calculus. First of all, we show that the framework is indeed applicable to those…

Logic in Computer Science · Computer Science 2022-04-29 Ugo Dal Lago , Furio Honsell , Marina Lenisa , Paolo Pistone

We explore representing the compact subsets of a given represented space by infinite sequences over Plotkin's $\mathbb{T}$. We show that computably compact computable metric spaces admit representations of their compact subsets in such a…

Logic in Computer Science · Computer Science 2018-12-05 Arno Pauly , Hideki Tsuiki

Composite likelihood provides approximate inference when the full likelihood is intractable and sub-likelihood functions of marginal events can be evaluated relatively easily. It has been successfully applied for many complex models.…

Methodology · Statistics 2024-09-05 Wentao Li , Rosabeth White , Dennis Prangle

Distributional and neural approaches to natural language semantics have been built almost exclusively on conventional linear algebra: vectors, matrices, tensors, and the operations that accompany them. These methods have achieved remarkable…

Computation and Language · Computer Science 2026-04-29 James Pustejovsky