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Related papers: Separations of non-monotonic randomness notions

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We extend the formalism of Conjectural Variations games to Stackelberg games involving multiple leaders and a single follower. To solve these nonconvex games, a common assumption is that the leaders compute their strategies having perfect…

Computer Science and Game Theory · Computer Science 2025-07-24 Francesco Morri , Hélène Le Cadre , Luce Brotcorne

A number of generalizations of stochastic and information-theoretic randomness are known in the literature. However, they are not compatible with handling meaning in vague and dynamic contexts of rough reasoning (and therefore explainable…

Artificial Intelligence · Computer Science 2023-04-04 Mani A

We study the fundamental problem of sequential probability assignment, also known as online learning with logarithmic loss, with respect to an arbitrary, possibly nonparametric hypothesis class. Our goal is to obtain a complexity measure…

Machine Learning · Computer Science 2024-10-08 Ziyi Liu , Idan Attias , Daniel M. Roy

We consider arithmetic sequences, here defined as ordered lists of positive integers. Any such a sequence can be cast onto a quantum state, enabling the quantification of its `surprise' through von Neumann entropy. We identify typical…

Quantum Physics · Physics 2025-01-14 Ruge Lin , Germán Sierra , José I. Latorre

We investigate topological, combinatorial, statistical, and enumeration properties of finite graphs with high Kolmogorov complexity (almost all graphs) using the novel incompressibility method. Example results are: (i) the mean and variance…

Combinatorics · Mathematics 2007-05-23 Harry Buhrman , Ming Li , John Tromp , Paul Vitanyi

Imprecise probability is concerned with uncertainty about which probability distributions to use. It has applications in robust statistics and machine learning. We look at programming language models for imprecise probability. Our…

Programming Languages · Computer Science 2024-10-31 Jack Liell-Cock , Sam Staton

Mutual information I in infinite sequences (and in their finite prefixes) is essential in theoretical analysis of many situations. Yet its right definition has been elusive for a long time. I address it by generalizing Kolmogorov Complexity…

Computational Complexity · Computer Science 2021-08-03 Leonid A. Levin

Stochastic games are a natural model for the synthesis of controllers confronted to adversarial and/or random actions. In particular, $\omega$-regular games of infinite length can represent reactive systems which are not expected to reach a…

Computer Science and Game Theory · Computer Science 2009-02-17 Florian Horn

Nonmonotonic reasoning is a pattern of reasoning that allows an agent to make and retract (tentative) conclusions from inconclusive evidence. This paper gives a possible-worlds interpretation of the nonmonotonic reasoning problem based on…

Artificial Intelligence · Computer Science 2013-04-10 Carl Kadie

The intractability of any problem and the randomness of its solutions have an obvious intuitive connection. However, the challenge till now has been that there is no practical way to firmly establish if the solution to a problem is actually…

Computational Complexity · Computer Science 2020-04-06 Arun U

We address two central notions of fairness in the literature of planning on nondeterministic fully observable domains. The first, which we call stochastic fairness, is classical, and assumes an environment which operates probabilistically…

Artificial Intelligence · Computer Science 2019-12-25 Benjamin Aminof , Giuseppe De Giacomo , Sasha Rubin

The issue of defining a random sequence of qubits is studied in the framework of Algorithmic Free Probability Theory.Its connection with Quantum Algorithmic Information Theory is shown

Quantum Physics · Physics 2007-05-23 Gavriel Segre

Acquisition of data is a difficult task in many applications of machine learning, and it is only natural that one hopes and expects the population risk to decrease (better performance) monotonically with increasing data points. It turns…

Machine Learning · Computer Science 2022-01-19 Zakaria Mhammedi

A notion of combinatorial game over a partially ordered set of atomic outcomes was recently introduced by Selinger. These games are appropriate for describing the value of positions in Hex and other monotone set coloring games. It is…

Combinatorics · Mathematics 2022-03-29 Eric Demer , Peter Selinger

We propose a sequential optimizing betting strategy in the multi-dimensional bounded forecasting game in the framework of game-theoretic probability of Shafer and Vovk (2001). By studying the asymptotic behavior of its capital process, we…

Probability · Mathematics 2011-02-16 Masayuki Kumon , Akimichi Takemura , Kei Takeuchi

In this paper, we establish some general forms of the law of the iterated logarithm for independent random variables in a sub-linear expectation space, where the random variables are not necessarily identically distributed. Exponential…

Probability · Mathematics 2021-06-16 Li-Xin Zhang

We consider the manipulability of tournament rules which map the results of $\binom{n}{2}$ pairwise matches and select a winner. Prior work designs simple tournament rules such that no pair of teams can manipulate the outcome of their match…

Computer Science and Game Theory · Computer Science 2021-01-12 Kimberly Ding , S. Matthew Weinberg

Kolmogorov's axioms of probability theory are extended to conditional probabilities among distinct (and sometimes intertwining) contexts. Formally, this amounts to row stochastic matrices whose entries characterize the conditional…

Quantum Physics · Physics 2023-11-16 Karl Svozil

We study the power of randomness in the Number-on-Forehead (NOF) model in communication complexity. We construct an explicit 3-player function $f:[N]^3 \to \{0,1\}$, such that: (i) there exist a randomized NOF protocol computing it that…

Computational Complexity · Computer Science 2024-01-04 Zander Kelley , Shachar Lovett , Raghu Meka

We introduce new definitions of universal and superuniversal computable codes, which are based on a code's ability to approximate Kolmogorov complexity within the prescribed margin for all individual sequences from a given set. Such sets of…

Machine Learning · Statistics 2009-04-10 Łukasz Dębowski