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We consider the problem of rational secret sharing introduced by Halpern and Teague [1], where the players involved in secret sharing play only if it is to their advantage. This can be characterized in the form of preferences. Players would…

Cryptography and Security · Computer Science 2011-12-20 William K. Moses , C. Pandu Rangan

A Dirichlet polynomial $d$ in one variable ${\mathcal{y}}$ is a function of the form $d({\mathcal{y}})=a_n n^{\mathcal{y}}+\cdots+a_22^{\mathcal{y}}+a_11^{\mathcal{y}}+a_00^{\mathcal{y}}$ for some $n,a_0,\ldots,a_n\in\mathbb{N}$. We will…

Information Theory · Computer Science 2021-10-29 David I. Spivak , Timothy Hosgood

A basic combinatorial interpretation of Shannon's entropy function is via the "20 questions" game. This cooperative game is played by two players, Alice and Bob: Alice picks a distribution $\pi$ over the numbers $\{1,\ldots,n\}$, and…

Discrete Mathematics · Computer Science 2017-04-26 Yuval Dagan , Yuval Filmus , Ariel Gabizon , Shay Moran

This paper studies the \emph{subset sampling} problem. The input is a set $\mathcal{S}$ of $n$ records together with a function $\textbf{p}$ that assigns each record $v\in\mathcal{S}$ a probability $\textbf{p}(v)$. A query returns a random…

Data Structures and Algorithms · Computer Science 2023-07-24 Jinchao Huang , Sibo Wang

While large language models (LLMs) demonstrate emerging reasoning capabilities, current inference-time expansion methods incur prohibitive computational costs by exhaustive sampling. Through analyzing decoding trajectories, we observe that…

Artificial Intelligence · Computer Science 2026-02-03 Ziheng Li , Hengyi Cai , Xiaochi Wei , Yuchen Li , Shuaiqiang Wang , Zhi-Hong Deng , Dawei Yin

The deterministic notions of capacity and entropy are studied in the context of communication and storage of information using square-integrable, bandlimited signals subject to perturbation. The $(\epsilon,\delta)$-capacity, that extends…

Information Theory · Computer Science 2015-04-21 Massimo Franceschetti , Taehyung J. Lim

Distributionally robust optimization (DRO) provides a framework for training machine learning models that are able to perform well on a collection of related data distributions (the "uncertainty set"). This is done by solving a min-max…

Machine Learning · Computer Science 2021-04-01 Paul Michel , Tatsunori Hashimoto , Graham Neubig

Let R^d -> A be a query problem over R^d for which there exists a data structure S that can compute P(q) in O(log n) time for any query point q in R^d. Let D be a probability measure over R^d representing a distribution of queries. We…

Computational Geometry · Computer Science 2010-02-08 Prosenjit Bose , Luc Devroye , Karim Douieb , Vida Dujmovic , James King , Pat Morin

Efficient probabilistic inference by variable elimination in graphical models requires an optimal elimination order. However, finding an optimal order is a challenging combinatorial optimisation problem for models with a large number of…

Artificial Intelligence · Computer Science 2025-03-13 Sagad Hamid , Tanya Braun

The influential work of Bravo et al. 2018 shows that derivative free play in strongly monotone games has complexity $O(d^2/\varepsilon^3)$, where $\varepsilon$ is the target accuracy on the expected squared distance to the solution. This…

Computer Science and Game Theory · Computer Science 2022-04-08 Dmitriy Drusvyatskiy , Maryam Fazel , Lillian J Ratliff

We consider importance sampling for estimating the probability that a light-tailed $d$-dimensional random walk exits through one of many disjoint rare-event regions before reaching an anticipated target. This problem arises in sequential…

Probability · Mathematics 2025-09-19 Yanglei Song , Georgios Fellouris

Nearly all practical neural models for classification are trained using cross-entropy loss. Yet this ubiquitous choice is supported by little theoretical or empirical evidence. Recent work (Hui & Belkin, 2020) suggests that training using…

Machine Learning · Computer Science 2023-02-09 Like Hui , Mikhail Belkin , Stephen Wright

We study non-monetary mechanisms for the fair and efficient allocation of reusable public resources, i.e., resources used for varying durations. We consider settings where a limited resource is repeatedly shared among a set of agents, each…

Computer Science and Game Theory · Computer Science 2023-10-17 Siddhartha Banerjee , Giannis Fikioris , Éva Tardos

The technique of Schroeppel and Shamir (SICOMP, 1981) has long been the most efficient way to trade space against time for the SUBSET SUM problem. In the random-instance setting, however, improved tradeoffs exist. In particular, the…

Data Structures and Algorithms · Computer Science 2013-03-05 Per Austrin , Petteri Kaski , Mikko Koivisto , Jussi Määttä

In the standard formulation of the occupancy problem one considers the distribution of r balls in n cells, with each ball assigned independently to a given cell with probability 1/n. Although closed form expressions can be given for the…

Probability · Mathematics 2007-05-23 Paul Dupuis , Carl Nuzman , Phil Whiting

The theory community has proposed several new heap variants in the recent past which have remained largely untested experimentally. We take the field back to the drawing board, with straightforward implementations of both classic and novel…

Data Structures and Algorithms · Computer Science 2014-03-04 Daniel H. Larkin , Siddhartha Sen , Robert E. Tarjan

A binary string transmitted via a memoryless i.i.d. deletion channel is received as a subsequence of the original input. From this, one obtains a posterior distribution on the channel input, corresponding to a set of candidate…

Information Theory · Computer Science 2019-03-05 Arash Atashpendar , Marc Beunardeau , Aisling Connolly , Rémi Géraud , David Mestel , A. W. Roscoe , Peter Y. A. Ryan

We introduce a new graph problem, the token dropping game, and we show how to solve it efficiently in a distributed setting. We use the token dropping game as a tool to design an efficient distributed algorithm for stable orientations and…

Distributed, Parallel, and Cluster Computing · Computer Science 2021-02-18 Sebastian Brandt , Barbara Keller , Joel Rybicki , Jukka Suomela , Jara Uitto

Social and real-world considerations such as robustness, fairness, social welfare and multi-agent tradeoffs have given rise to multi-distribution learning paradigms, such as collaborative learning, group distributionally robust…

Machine Learning · Computer Science 2024-04-04 Nika Haghtalab , Michael I. Jordan , Eric Zhao

Given a (possibly infinite) subset $A$ of the natural numbers, we ask how many times a fair six-sided die must be rolled until the rolled numbers add up to an element of $A$. Using a one-dimensional dynamic programming recursion together…

Probability · Mathematics 2026-05-14 Christoph Koutschan , Tipaluck Krityakierne , Thotsaporn Aek Thanatipanonda
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