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Related papers: Negatively Reinforced Balanced Urn Schemes

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We study the competing urn model in which $m$ balls are placed independently into $n$ urns according to (possibly distinct) ball distributions. Kahn and Neiman (2010) showed that, under identical ball distributions, the induced urn measure…

Probability · Mathematics 2025-09-09 Swee Hong Chan

We consider systems of interacting Generalized Friedman's Urns (GFUs) having irreducible mean replacement matrices. The interaction is modeled through the probability to sample the colors from each urn, that is defined as convex combination…

Probability · Mathematics 2018-01-09 Giacomo Aletti , Andrea Ghiglietti

Competing urns refers to the random experiment where m balls are dropped, randomly and independently, into urns 1,...,n. Formally, we have a random map $\sigma$ from {1,...,m} to {1,...,n} with the $\sigma(i)$'s i.i.d. With $x_j$ the…

Probability · Mathematics 2010-01-06 Jeff Kahn , Michael Neiman

In many learning problems, the training and testing data follow different distributions and a particularly common situation is the \textit{covariate shift}. To correct for sampling biases, most approaches, including the popular kernel mean…

Machine Learning · Computer Science 2020-03-13 Henry Lam , Fengpei Li , Siddharth Prusty

An extended Polya urn Model with two colors, black and white, is studied with some SLLN and CLT on the proportion of white balls.

Probability · Mathematics 2021-09-17 Rafik Aguech , Wissem Jedidi , Olfa Selmi

We define and prove limit results for a class of dominant P\'olya sequences, which are randomly reinforced urn processes with color-specific random weights and unbounded number of possible colors. Under fairly mild assumptions on the…

Probability · Mathematics 2023-12-12 Hristo Sariev , Sandra Fortini , Sonia Petrone

This work aims at solving the problems with intractable sparsity-inducing norms that are often encountered in various machine learning tasks, such as multi-task learning, subspace clustering, feature selection, robust principal component…

Machine Learning · Computer Science 2019-07-03 Feiping Nie , Zhanxuan Hu , Xiaoqian Wang , Rong Wang , Xuelong Li , Heng Huang

This paper extends the link between stochastic approximation (SA) theory and randomized urn models developed in Laruelle, Pag{\`e}s (2013), and their applications to clinical trials introduced in Bai, HU (1999,2005) and Bai, Hu, Shen…

Probability · Mathematics 2018-05-16 Sophie Laruelle , Gilles Pagès

Inverse probability weighting (IPW) is widely used in many areas when data are subject to unrepresentativeness, missingness, or selection bias. An inevitable challenge with the use of IPW is that the IPW estimator can be remarkably unstable…

Methodology · Statistics 2021-11-29 Yukun Liu , Yan Fan

We consider the urn setting with two different objects, ``good'' and ``bad'', and analyze the number of draws without replacement until a good object is picked. Although the expected number of draws for this setting is a standard textbook…

Probability · Mathematics 2014-04-07 John Ahlgren

This paper addresses how a recursive neural network model can automatically leave out useless information and emphasize important evidence, in other words, to perform "weight tuning" for higher-level representation acquisition. We propose…

Neural and Evolutionary Computing · Computer Science 2014-12-16 Jiwei Li

This paper studies a very general urn model stimulated by designs in clinical trials, where the number of balls of different types added to the urn at trial n depends on a random outcome directed by the composition at trials 1,2,...,n-1.…

Probability · Mathematics 2007-05-23 Zhi-Dong Bai , Feifang Hu

We characterise multi-candidate pure-strategy equilibria in the Hotelling-Downs spatial election model for the class of best-worst voting rules, in which each voter is endowed with both a positive and a negative vote, i.e., each voter can…

Computer Science and Game Theory · Computer Science 2016-10-11 Dodge Cahan , Arkadii Slinko

In the impartial selection problem, a subset of agents up to a fixed size $k$ among a group of $n$ is to be chosen based on votes cast by the agents themselves. A selection mechanism is impartial if no agent can influence its own chance of…

Computer Science and Game Theory · Computer Science 2024-08-06 Javier Cembrano , Svenja M. Griesbach , Maximilian J. Stahlberg

The Friedman's urn model is a popular urn model which is widely used in many disciplines. In particular, it is extensively used in treatment allocation schemes in clinical trials. In this paper, we prove that both the urn composition…

Probability · Mathematics 2009-08-30 Li-Xin Zhang , Feifang Hu

In the Colored Bin Packing problem a set of items with varying weights and colors must be packed into bins of uniform weight limit such that no two items of the same color may be packed adjacently within a bin. We solve this problem for the…

Data Structures and Algorithms · Computer Science 2015-11-17 Hamza Alsarhan , Davin Chia , Ananya Christman , Shannia Fu , Yanfeng Jin

We consider weighted sums of independent random variables regulated by an increment sequence. We provide operative conditions that ensure strong law of large numbers for such sums to hold in both the centered and non-centered case. The…

Probability · Mathematics 2021-03-11 Luca Avena , Conrado da Costa

This article describes a purely analytic approach to urn models of the generalized or extended P\'olya-Eggenberger type, in the case of two types of balls and constant ``balance,'' that is, constant row sum. The treatment starts from a…

Probability · Mathematics 2007-05-23 Philippe Flajolet , Joaquim Gabarro , Helmut Pekari

In the first part of this paper, we consider weighted domination in the case where the vertices of the complete graph on~\(n\) vertices are equipped with independent and identically distributed (i.i.d.) weights. We use the probabilistic…

Probability · Mathematics 2023-01-16 Ghurumuruhan Ganesan

Scholars frequently use covariate balance tests to test the validity of natural experiments and related designs. Unfortunately, when measured covariates are unrelated to potential outcomes, balance is uninformative about key identification…

Methodology · Statistics 2025-10-15 Clara Bicalho , Adam Bouyamourn , Thad Dunning