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A large class of problems in sciences and engineering can be formulated as the general problem of constructing random intervals with pre-specified coverage probabilities for the mean. Wee propose a general approach for statistical inference…

Statistics Theory · Mathematics 2013-06-11 Xinjia Chen

A new unequal probability sampling method is proposed. This method is sequential. The decision to select or not each unit is made based on the order in which the units appear. A variant of this method allows selecting a sample from a…

Methodology · Statistics 2021-11-17 Bardia Panahbehagh , Raphaël Jauslin , Yves Tillé

This work considers the problem of detecting signals from multiple sequentially observed data streams, where only one stream can be observed at every time instant. The goal is to detect signals as quickly as possible while controlling the…

Methodology · Statistics 2026-04-07 Yiming Xing , Georgios Fellouris

Consider a decision maker who is responsible to dynamically collect observations so as to enhance his information about an underlying phenomena of interest in a speedy manner while accounting for the penalty of wrong declaration. Due to the…

Information Theory · Computer Science 2013-12-19 Mohammad Naghshvar , Tara Javidi

Recent work has shown that models trained to the same objective, and which achieve similar measures of accuracy on consistent test data, may nonetheless behave very differently on individual predictions. This inconsistency is undesirable in…

Machine Learning · Computer Science 2021-11-17 Emily Black , Klas Leino , Matt Fredrikson

Consider a finite set of sources, each producing i.i.d. observations that follow a unique probability distribution on a finite alphabet. We study the problem of matching a finite set of observed sequences to the set of sources under the…

Information Theory · Computer Science 2014-12-09 Jayakrishnan Unnikrishnan

In this paper we develop a unified approach for solving a wide class of sequential selection problems. This class includes, but is not limited to, selection problems with no-information, rank-dependent rewards, and considers both fixed as…

Probability · Mathematics 2020-01-27 Alexander Goldenshluger , Yaakov Malinovsky , Assaf Zeevi

Subject selection plays a critical role in experimental studies, especially ones with human subjects. Anecdotal evidence suggests that many such studies, done at or near university campus settings suffer from selection bias, i.e., the…

Machine Learning · Computer Science 2020-12-21 Tahereh Arabghalizi , Alexandros Labrinidis

We consider the problem of detecting an odd process among a group of Poisson point processes, all having the same rate except the odd process. The actual rates of the odd and non-odd processes are unknown to the decision maker. We consider…

Information Theory · Computer Science 2015-09-24 Nidhin Koshy Vaidhiyan , Rajesh Sundaresan

Consider a sequence of $n$ independent random variables with a common continuous distribution $F$, and consider the task of choosing an increasing subsequence where the observations are revealed sequentially and where an observation must be…

Probability · Mathematics 2016-08-02 Alessandro Arlotto , Vinh V. Nguyen , J. Michael Steele

We introduce a notion of computable randomness for infinite sequences that generalises the classical version in two important ways. First, our definition of computable randomness is associated with imprecise probability models, in the sense…

Probability · Mathematics 2020-09-23 Floris Persiau , Jasper De Bock , Gert de Cooman

We propose information-directed sampling -- a new approach to online optimization problems in which a decision-maker must balance between exploration and exploitation while learning from partial feedback. Each action is sampled in a manner…

Machine Learning · Computer Science 2017-07-10 Daniel Russo , Benjamin Van Roy

Online sequence prediction is the problem of predicting the next element of a sequence given previous elements. This problem has been extensively studied in the context of individual sequence prediction, where no prior assumptions are made…

Machine Learning · Computer Science 2012-06-22 Elad Eban , Aharon Birnbaum , Shai Shalev-Shwartz , Amir Globerson

When artificial neural networks have demonstrated exceptional practical success in a variety of domains, investigations into their theoretical characteristics, such as their approximation power, statistical properties, and generalization…

Machine Learning · Statistics 2023-10-06 Shijin Gong , Xinyu Zhang

We revisit the problem of maximising the expected length of increasing subsequence that can be selected from a marked Poisson process by an online strategy. Resorting to a natural size variable, the problem is represented in terms of a…

Probability · Mathematics 2019-05-10 Alexander Gnedin , Amirlan Seksenbayev

We study the problems of sequential nonparametric two-sample and independence testing. Sequential tests process data online and allow using observed data to decide whether to stop and reject the null hypothesis or to collect more data,…

Machine Learning · Statistics 2023-07-21 Aleksandr Podkopaev , Aaditya Ramdas

The problem of joint sequential detection and isolation is considered in the context of multiple, not necessarily independent, data streams. A multiple testing framework is proposed, where each hypothesis corresponds to a different subset…

Statistics Theory · Mathematics 2022-07-04 Anamitra Chaudhuri , Georgios Fellouris

Finding the underlying probability distributions of a set of observed sequences under the constraint that each sequence is generated i.i.d by a distinct distribution is considered. The number of distributions, and hence the number of…

Information Theory · Computer Science 2018-10-16 Sara Shahi , Daniela Tuninetti , Natasha Devroye

Strategic test allocation plays a major role in the control of both emerging and existing pandemics (e.g., COVID-19, HIV). Widespread testing supports effective epidemic control by (1) reducing transmission via identifying cases, and (2)…

Methodology · Statistics 2022-12-06 Ivana Malenica , Jeremy R. Coyle , Mark J. van der Laan , Maya L. Petersen

Consider $K$ processes, each generating a sequence of identical and independent random variables. The probability measures of these processes have random parameters that must be estimated. Specifically, they share a parameter $\theta$…

Machine Learning · Computer Science 2022-10-12 Arpan Mukherjee , Ali Tajer , Pin-Yu Chen , Payel Das