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Boosting is a celebrated machine learning approach which is based on the idea of combining weak and moderately inaccurate hypotheses to a strong and accurate one. We study boosting under the assumption that the weak hypotheses belong to a…

Machine Learning · Computer Science 2024-02-14 Noga Alon , Alon Gonen , Elad Hazan , Shay Moran

In a recent paper, Braun, Chung and Graham [1] have addressed a single-processor scheduling problem with time restrictions. Given a fixed integer $B \geq 2$, there is a set of jobs to be processed by a single processor subject to the…

Data Structures and Algorithms · Computer Science 2017-03-03 An Zhang , Yong Chen , Lin Chen , Guangting Chen

Modern statistical software and machine learning libraries are enabling semi-automated statistical inference. Within this context, it appears easier and easier to try and fit many models to the data at hand, reversing thereby the Fisherian…

Methodology · Statistics 2020-09-28 Pierre-Alexandre Mattei

In his seminal paper ``A census of planar triangulations", published in 1962, the iconic graph theorist (and code-breaker), W.T. Tutte, spent a few pages to prove that a certain bi-variate generating function that enumerates triangulations,…

Combinatorics · Mathematics 2024-03-13 Shalosh B. Ekhad , Doron Zeilberger

As inductive inference and machine learning methods in computer science see continued success, researchers are aiming to describe ever more complex probabilistic models and inference algorithms. It is natural to ask whether there is a…

Logic · Mathematics 2019-11-19 Nathanael L. Ackerman , Cameron E. Freer , Daniel M. Roy

The synthesis problem asks to automatically generate, if it exists, an algorithm from a specification of correct input-output pairs. In this paper, we consider the synthesis of computable functions of infinite words, for a classical Turing…

Formal Languages and Automata Theory · Computer Science 2024-02-09 Emmanuel Filiot , Sarah Winter

The binary value function, or BinVal, has appeared in several studies in theory of evolutionary computation as one of the extreme examples of linear pseudo-Boolean functions. Its unbiased black-box complexity was previously shown to be at…

Neural and Evolutionary Computing · Computer Science 2019-04-11 Nina Bulanova , Maxim Buzdalov

We study so-called invariant games played with a fixed number $d$ of heaps of matches. A game is described by a finite list $\mathcal{M}$ of integer vectors of length $d$ specifying the legal moves. A move consists in changing the current…

Computational Complexity · Computer Science 2012-02-06 Urban Larsson , Johan Wästlund

We study the question of what is computable by Turing machines equipped with time travel into the past; i.e., with Deutschian closed timelike curves (CTCs) having no bound on their width or length. An alternative viewpoint is that we study…

The beautiful theory of statistical gambling, started by Dubins and Savage (for subfair games) and continued by Kelly and Breiman (for superfair games) has mostly been studied under the unrealistic assumption that we live in a continuous…

Probability · Mathematics 2014-11-27 Shalosh B. Ekhad , Doron Zeilberger

The TTE approach to Computable Analysis is the study of so-called representations (encodings for continuous objects such as reals, functions, and sets) with respect to the notions of computability they induce. A rich variety of such…

Computational Complexity · Computer Science 2023-06-22 Carsten Rösnick-Neugebauer

Scheduling a sports tournament is a complex optimization problem, which requires a large number of hard constraints to satisfy. Despite the availability of several such constraints in the literature, there remains a gap since most of the…

Numerical Analysis · Mathematics 2021-06-18 Syed Rameez Naqvi , Adnan Ahmad , S. M. Riazul Islam , Tallha Akram , M. Abdullah-Al-Wadud , Atif Alamri

Causality serves as an abstract notion of time for concurrent systems. A computation is causal, or simply valid, if each observation of a computation event is preceded by the observation of its causes. The present work establishes that this…

Logic in Computer Science · Computer Science 2026-03-03 Clément Aubert , Jean Krivine

Approximate computing frameworks configure applications so they can operate at a range of points in an accuracy-performance trade-off space. Prior work has introduced many frameworks to create approximate programs. As approximation…

Artificial Intelligence · Computer Science 2021-02-18 Saeid Barati , Gordon Kindlmann , Hank Hoffmann

The past decades have seen enormous improvements in computational inference based on statistical models, with continual enhancement in a wide range of computational tools, in competition. In Bayesian inference, first and foremost, MCMC…

Computation · Statistics 2015-05-12 Peter J. Green , Krzysztof Łatuszyński , Marcelo Pereyra , Christian P. Robert

I study sequential contests where the efforts of earlier players may be disclosed to later players by nature or by design. The model has a range of applications, including rent seeking, R&D, oligopoly, public goods provision, and tragedy of…

Computer Science and Game Theory · Computer Science 2021-02-22 Toomas Hinnosaar

Priced timed games are optimal-cost reachability games played between two players---the controller and the environment---by moving a token along the edges of infinite graphs of configurations of priced timed automata. The goal of the…

Logic in Computer Science · Computer Science 2015-07-22 Shibashis Guha , Shankara Narayanan Krishna , Lakshmi Manasa , Ashutosh Trivedi

Ladder tournaments are widely used to rank individuals in real-world organizations and games. Their mathematical properties however are still poorly understood. We formalize the ranking rule generated by a ladder tournament, and we show…

Combinatorics · Mathematics 2015-07-07 Roland Pongou , Bertrand Tchantcho , Narcisse Tedjeugang

Inspired from a joint work by A. Beckmann, S. Buss and S. Friedman, we propose a class of set-theoretic functions, predicatively computable functions. Each function in this class is polynomial time computable when we restrict to finite…

Logic · Mathematics 2014-11-27 Toshiyasu Arai

Lossy compression and clustering fundamentally involve a decision about what features are relevant and which are not. The information bottleneck method (IB) by Tishby, Pereira, and Bialek formalized this notion as an information-theoretic…

Neurons and Cognition · Quantitative Biology 2017-02-23 DJ Strouse , David J Schwab