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Every K-trivial set is computable from an incomplete Martin-L\"of random set, i.e., a Martin-L\"of random set that does not compute 0'.

In the real world, insufficient information, limited computation resources, and complex problem structures often force an autonomous agent to make a decision in time less than that required to solve the problem at hand completely. Flexible…

Artificial Intelligence · Computer Science 2013-02-01 Weixiong Zhang

Approximate Bayesian Computation (ABC) is a popular computational method for likelihood-free Bayesian inference. The term "likelihood-free" refers to problems where the likelihood is intractable to compute or estimate directly, but where it…

Statistics Theory · Mathematics 2014-07-21 Stuart Barber , Jochen Voss , Mark Webster

The notion of Schnorr randomness refers to computable reals or computable functions. We propose a version of Schnorr randomness for subcomputable classes and characterize it in different ways: by Martin L\"of tests, martingales or measure…

Logic in Computer Science · Computer Science 2019-03-14 Claude Sureson

Reversible forms of computations are often interesting from an energy efficiency point of view. When the computation device in question is an automaton, it is known that the minimal reversible automaton recognizing a given language is not…

Formal Languages and Automata Theory · Computer Science 2017-08-23 Kitti Gelle , Szabolcs Iván

For every given real value of the ratio $\mu:=A_X/G_X>1$ of the arithmetic and geometric means of a positive random variable $X$ and every real $v>0$, exact upper bounds on the right- and left-tail probabilities $\mathsf{P}(X/G_X\ge v)$ and…

Probability · Mathematics 2021-03-30 Iosif Pinelis

We consider the hardness of approximation of optimization problems from the point of view of definability. For many NP-hard optimization problems it is known that, unless P = NP, no polynomial-time algorithm can give an approximate solution…

Logic in Computer Science · Computer Science 2019-08-30 Albert Atserias , Anuj Dawar

Computing reachability probabilities is a fundamental problem in the analysis of probabilistic programs. This paper aims at a comprehensive and comparative account on various martingale-based methods for over- and under-approximating…

Programming Languages · Computer Science 2018-11-16 Toru Takisaka , Yuichiro Oyabu , Natsuki Urabe , Ichiro Hasuo

We give computable bounds on the rate of convergence of the transition probabilities to the stationary distribution for a certain class of geometrically ergodic Markov chains. Our results are different from earlier estimates of Meyn and…

Probability · Mathematics 2007-05-23 Peter H. Baxendale

We are interested in the computability between left c.e. reals $\alpha$ and their initial segments. We show that the quantity $C(C(\alpha_n)|\alpha_n)$ plays a crucial role in this and in their completeness. We look in particular at…

Logic in Computer Science · Computer Science 2022-08-02 George Davie

In this paper we combine two existing approaches for approximating attractors. One of them approximates the attractors arbitrarily well by sublevel sets related to solutions of infinite dimensional linear programming problems. A downside…

Optimization and Control · Mathematics 2023-10-06 Corbinian Schlosser

We introduce the notion of almost realizability, an arithmetic generalization of realizability for integer sequences, which is the property of counting periodic points for some map. We characterize the intersection between the set of…

Number Theory · Mathematics 2025-08-18 Piotr Miska , Tom Ward

Methods for the reduction of the complexity of computational problems are presented, as well as their connections to renormalization, scaling, and irreversible statistical mechanics. Several statistically stationary cases are analyzed; for…

Numerical Analysis · Mathematics 2007-05-23 Alexandre J. Chorin , Panagiotis Stinis

Bayesian inference for Markov processes has become increasingly relevant in recent years. Problems of this type often have intractable likelihoods and prior knowledge about model rate parameters is often poor. Markov Chain Monte Carlo…

Computation · Statistics 2014-10-23 Jamie Owen , Darren J. Wilkinson , Colin S. Gillespie

This work studies the problem of constructing capacity-achieving codes from an algorithmic perspective. Specifically, we prove that there exists a Turing machine which, given a discrete memoryless channel $p_{Y|X}$, a target rate $R$ less…

Information Theory · Computer Science 2025-11-06 Angelos Gkekas , Nikos A. Mitsiou , Ioannis Souldatos , George K. Karagiannidis

We call a subset of an ordinal $\lambda$ recognizable if it is the unique subset $x$ of $\lambda$ for which some Turing machine with ordinal time and tape, which halts for all subsets of $\lambda$ as input, halts with the final state $0$.…

Logic · Mathematics 2026-05-19 Merlin Carl , Philipp Schlicht , Philip Welch

Difference schemes are considered for dynamical systems $ \dot x = f (x) $ with a quadratic right-hand side, which have $t$-symmetry and are reversible. Reversibility is interpreted in the sense that the Cremona transformation is performed…

Classical Analysis and ODEs · Mathematics 2024-12-03 Mikhail Malykh , Leonid Sevastianov

A common lens to theoretically study neural net architectures is to analyze the functions they can approximate. However, constructions from approximation theory may be unrealistic and therefore less meaningful. For example, a common…

Machine Learning · Computer Science 2023-03-31 Colin Wei , Yining Chen , Tengyu Ma

The topic of this paper is the subtle interplay between countability and representations. In particular, we establish that the definition of countability of a certain set $X$ crucially hinges on the associated equivalence relation $=_{X}$.…

Logic · Mathematics 2026-02-09 Sam Sanders

In the rapidly growing literature on explanation algorithms, it often remains unclear what precisely these algorithms are for and how they should be used. In this position paper, we argue for a novel and pragmatic perspective: Explainable…

Machine Learning · Computer Science 2025-06-17 Sebastian Bordt , Eric Raidl , Ulrike von Luxburg
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