English
Related papers

Related papers: Effective subfactor theory

200 papers

In previous papers on this project a general static logical framework for formalizing and mechanizing set theories of different strength was suggested, and the power of some predicatively acceptable theories in that framework was explored.…

Logic in Computer Science · Computer Science 2023-06-22 Arnon Avron , Liron Cohen

The class of Basic Feasible Functionals BFF$_2$ is the type-2 counterpart of the class FP of type-1 functions computable in polynomial time. Several characterizations have been suggested in the literature, but none of these present a…

Logic in Computer Science · Computer Science 2023-06-22 Emmanuel Hainry , Bruce M. Kapron , Jean-Yves Marion , Romain Péchoux

The roles played by decision factors in making complex subject are decisions are characterized by how these factors affect the overall decision. Evidence that partially matches a factor is evaluated, and then effective computational rules…

Artificial Intelligence · Computer Science 2013-04-15 Gerald Shao-Hung Liu

We show that it is possible to understand and identify a decision maker's subjective causal judgements by observing her preferences over interventions. Following Pearl [2000], we represent causality using causal models (also called…

Theoretical Economics · Economics 2024-01-23 Joseph Y. Halpern , Evan Piermont

Given a represented space (in the sense of TTE theory), an appropriate representation is constructed for the Moschovakis extension of its carrier (with paying attention to the cases of effective topological spaces and effective metric…

Logic · Mathematics 2023-06-22 Dimiter Skordev

Computing the expectation of kernel functions is a ubiquitous task in machine learning, with applications from classical support vector machines to exploiting kernel embeddings of distributions in probabilistic modeling, statistical…

Machine Learning · Computer Science 2021-07-23 Wenzhe Li , Zhe Zeng , Antonio Vergari , Guy Van den Broeck

We introduce MCCE: Monte Carlo sampling of valid and realistic Counterfactual Explanations for tabular data, a novel counterfactual explanation method that generates on-manifold, actionable and valid counterfactuals by modeling the joint…

Machine Learning · Statistics 2024-01-26 Annabelle Redelmeier , Martin Jullum , Kjersti Aas , Anders Løland

We use forcing over admissible sets to show that, for every ordinal $\alpha$ in a club $C\subset\omega_1$, there are copies of $\alpha$ such that the isomorphism between them is not computable in the join of the complete $\Pi^1_1$ set…

Logic · Mathematics 2024-08-21 Noah Schweber

We consider II$_1$ factors $M$ which can be realized as inductive limits of subfactors, $N_n \nearrow M$, having spectral gap in $M$ and satisfying the bi-commutant condition $(N_n'\cap M)'\cap M=N_n$. Examples are the enveloping algebras…

Operator Algebras · Mathematics 2009-10-14 Sorin Popa

The aim of this paper is to discuss a recent result which shows that probabilistic inference in the presence of (unknown) causal mechanisms can be tractable for models that have traditionally been viewed as intractable. This result was…

Artificial Intelligence · Computer Science 2022-02-08 Adnan Darwiche

There is a growing concern about typically opaque decision-making with high-performance machine learning algorithms. Providing an explanation of the reasoning process in domain-specific terms can be crucial for adoption in risk-sensitive…

Computer Vision and Pattern Recognition · Computer Science 2022-11-28 Aditya Chattopadhyay , Stewart Slocum , Benjamin D. Haeffele , Rene Vidal , Donald Geman

We show how to smoothly incorporate in the object-oriented paradigm constructs to raise, compose, and handle effects in an arbitrary monad. The underlying pure calculus is meant to be a representative of the last generation of OO languages,…

Programming Languages · Computer Science 2025-04-23 Francesco Dagnino , Paola Giannini , Elena Zucca

Machine Learning explainability techniques have been proposed as a means of `explaining' or interrogating a model in order to understand why a particular decision or prediction has been made. Such an ability is especially important at a…

Machine Learning · Statistics 2022-02-28 Matthew J. Vowels

We provide a logical framework in which a resource-bounded agent can be seen to perform approximations of probabilistic reasoning. Our main results read as follows. First we identify the conditions under which propositional probability…

Logic in Computer Science · Computer Science 2022-05-09 Paolo Baldi , Hykel Hosni

Kleene's computability theory based on the S1-S9 computation schemes constitutes a model for computing with objects of any finite type and extends Turing's 'machine model' which formalises computing with real numbers. A fundamental…

Logic · Mathematics 2024-01-17 Sam Sanders

We introduce two notions of effective reducibility for set-theoretical statements, based on computability with Ordinal Turing Machines (OTMs), one of which resembles Turing reducibility while the other is modelled after Weihrauch…

Logic · Mathematics 2026-05-19 Merlin Carl

We study perturbations of Feller generators under `lower order terms' with measurable coefficients. We investigate which properties of the original semigroup -- such as positivity, conservativeness and the Feller property -- are passed to…

Probability · Mathematics 2021-08-06 Franziska Kühn , Markus Kunze

We give a subfactor construction for a $II_{1}$ factor M which is not anti-isomorphic to itself. The $II_{1}$ factor we consider is essentially the same as the example previously given by Connes. However, our construction uses the recently…

Operator Algebras · Mathematics 2007-05-23 Maria Grazia Viola

In this paper we extend the approach of M. Cavaleri to effective amenability to the class of computably enumerable groups, i.e. in particular we do not assume that groups are finitely generated. In the case of computable groups we also…

Group Theory · Mathematics 2022-05-16 Karol Duda

In this paper we review important aspects of semiparametric theory and empirical processes that arise in causal inference problems. We begin with a brief introduction to the general problem of causal inference, and go on to discuss…

Statistics Theory · Mathematics 2016-07-25 Edward H. Kennedy