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A hypothesis testing algorithm is replicable if, when run on two different samples from the same distribution, it produces the same output with high probability. This notion, defined by by Impagliazzo, Lei, Pitassi, and Sorell [STOC'22],…

Data Structures and Algorithms · Computer Science 2025-09-05 Anders Aamand , Maryam Aliakbarpour , Justin Y. Chen , Shyam Narayanan , Sandeep Silwal

Recent developments in the categorical foundations of universal algebra have given fresh impetus to an understanding of the lambda calculus coming from categorical logic: an interpretation is a semi-closed algebraic theory. Scott's…

Category Theory · Mathematics 2015-07-22 Martin Hyland

This paper studies normalisation by evaluation for typed lambda calculus from a categorical and algebraic viewpoint. The first part of the paper analyses the lambda definability result of Jung and Tiuryn via Kripke logical relations and…

Logic in Computer Science · Computer Science 2022-08-19 Marcelo Fiore

We consider the task of performing probabilistic inference with probabilistic logical models. Many algorithms for approximate inference with such models are based on sampling. From a logic programming perspective, sampling boils down to…

Artificial Intelligence · Computer Science 2015-03-19 Daan Fierens

We examine the meaning and the complexity of probabilistic logic programs that consist of a set of rules and a set of independent probabilistic facts (that is, programs based on Sato's distribution semantics). We focus on two semantics,…

Artificial Intelligence · Computer Science 2017-02-01 Fabio Gagliardi Cozman , Denis Deratani Mauá

A research frontier has emerged in scientific computation, wherein numerical error is regarded as a source of epistemic uncertainty that can be modelled. This raises several statistical challenges, including the design of statistical…

Machine Learning · Statistics 2017-10-19 François-Xavier Briol , Chris. J. Oates , Mark Girolami , Michael A. Osborne , Dino Sejdinovic

Probabilistic logic programs are logic programs in which some of the facts are annotated with probabilities. This paper investigates how classical inference and learning tasks known from the graphical model community can be tackled for…

Artificial Intelligence · Computer Science 2020-02-19 Daan Fierens , Guy Van den Broeck , Joris Renkens , Dimitar Shterionov , Bernd Gutmann , Ingo Thon , Gerda Janssens , Luc De Raedt

Probabilistic Logic Programming (PLP) under the Distribution Semantics is a leading approach to practical reasoning under uncertainty. An advantage of the Distribution Semantics is its suitability for implementation as a Prolog or Python…

Logic in Computer Science · Computer Science 2026-01-14 Damiano Azzolini , Fabrizio Riguzzi , Theresa Swift

The formal system lambda-delta is a typed lambda calculus that pursues the unification of terms, types, environments and contexts as the main goal. lambda-delta takes some features from the Automath-related lambda calculi and some from the…

Logic in Computer Science · Computer Science 2008-09-25 F. Guidi

We show that an intuitionistic version of counting propositional logic corresponds, in the sense of Curry and Howard, to an expressive type system for the probabilistic event lambda-calculus, a vehicle calculus in which both call-by-name…

Logic in Computer Science · Computer Science 2022-03-23 Melissa Antonelli , Ugo Dal Lago , Paolo Pistone

Quantum phase estimation is one of the key algorithms in the field of quantum computing, but up until now, only approximate expressions have been derived for the probability of error. We revisit these derivations, and find that by ensuring…

Quantum Physics · Physics 2012-02-13 James M. Chappell , Max A. Lohe , Lorenz von Smekal , Azhar Iqbal , Derek Abbott

Prediction, where observed data is used to quantify uncertainty about a future observation, is a fundamental problem in statistics. Prediction sets with coverage probability guarantees are a common solution, but these do not provide…

Statistics Theory · Mathematics 2022-11-22 Leonardo Cella , Ryan Martin

Recently Quantum Computation has generated a lot of interest due to the discovery of a quantum algorithm which can factor large numbers in polynomial time. The usefulness of a quantum com puter is limited by the effect of errors. Simulation…

Quantum Physics · Physics 2007-05-23 Kevin M. Obenland , Alvin M. Despain

This work proposes a dependent type theory that combines functions and session-typed processes (with value dependencies) through a contextual monad, internalising typed processes in a dependently-typed lambda-calculus. The proposed…

Programming Languages · Computer Science 2018-01-25 Bernardo Toninho , Nobuko Yoshida

We show that the standard computational pipeline of probabilistic programming systems (PPSs) can be inefficient for estimating expectations and introduce the concept of expectation programming to address this. In expectation programming,…

Machine Learning · Computer Science 2022-06-22 Tim Reichelt , Adam Goliński , Luke Ong , Tom Rainforth

Capture calculus has recently been proposed as a solution to effect checking, achieved by tracking the captured references of terms in the types. Boxes, along with the box and unbox operations, are a crucial construct in capture calculus,…

Programming Languages · Computer Science 2023-06-13 Yichen Xu , Martin Odersky

We introduce two extensions of the $\lambda$-calculus with a probabilistic choice operator, $\Lambda_\oplus^{cbv}$ and $\Lambda_\oplus^{cbn}$, modeling respectively call-by-value and call-by-name probabilistic computation. We prove that…

Logic in Computer Science · Computer Science 2019-05-13 Claudia Faggian , Simona Ronchi della Rocca

System F, the polymorphic lambda calculus, features the principle of impredicativity: polymorphic types may be (explicitly) instantiated at other types, enabling many powerful idioms such as Church encoding and data abstraction.…

Programming Languages · Computer Science 2022-03-04 Henry Mercer , Cameron Ramsay , Neel Krishnaswami

Inference is the process of using facts we know to learn about facts we do not know. A theory of inference gives assumptions necessary to get from the former to the latter, along with a definition for and summary of the resulting…

Machine Learning · Statistics 2021-09-27 Beau Coker , Cynthia Rudin , Gary King

Decisions by Machine Learning (ML) models have become ubiquitous. Trusting these decisions requires understanding how algorithms take them. Hence interpretability methods for ML are an active focus of research. A central problem in this…

Machine Learning · Computer Science 2019-01-25 Philipp Schmidt , Felix Biessmann
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