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We analyze general model selection procedures using penalized empirical loss minimization under computational constraints. While classical model selection approaches do not consider computational aspects of performing model selection, we…

Machine Learning · Statistics 2012-08-02 Alekh Agarwal , Peter L. Bartlett , John C. Duchi

In this paper,we consider a high-dimensional statistical estimation problem in which the the number of parameters is comparable or larger than the sample size. We present a unified analysis of the performance guarantees of exponential…

Statistics Theory · Mathematics 2017-10-04 Tung Duy Luu , Jalal Fadili , Christophe Chesneau

Sequential estimation of a vector of linear regression coefficients is considered under both centralized and decentralized setups. In sequential estimation, the number of observations used for estimation is determined by the observed…

Applications · Statistics 2014-12-18 Yasin Yilmaz , George V. Moustakides , Xiaodong Wang

Sequential recommendation methods can capture dynamic user preferences from user historical interactions to achieve better performance. However, most existing methods only use past information extracted from user historical interactions to…

Information Retrieval · Computer Science 2024-12-03 Jiafeng Xia , Dongsheng Li , Hansu Gu , Tun Lu , Peng Zhang , Li Shang , Ning Gu

We study the intrinsic limitations of sequential convex optimization through the lens of feedback information theory. In the oracle model of optimization, an algorithm queries an {\em oracle} for noisy information about the unknown…

Information Theory · Computer Science 2011-09-12 Maxim Raginsky , Alexander Rakhlin

We study distribution testing in the standard access model and the conditional access model when the memory available to the testing algorithm is bounded. In both scenarios, the samples appear in an online fashion and the goal is to test…

Data Structures and Algorithms · Computer Science 2023-09-08 Sampriti Roy , Yadu Vasudev

In the adaptive information gathering problem, a policy is required to select an informative sensing location using the history of measurements acquired thus far. While there is an extensive amount of prior work investigating effective…

Robotics · Computer Science 2017-05-23 Sanjiban Choudhury , Ashish Kapoor , Gireeja Ranade , Sebastian Scherer , Debadeepta Dey

Several recent works have proposed instance-dependent upper bounds on the number of episodes needed to identify, with probability $1-\delta$, an $\varepsilon$-optimal policy in finite-horizon tabular Markov Decision Processes (MDPs). These…

Machine Learning · Statistics 2023-11-13 Aymen Al-Marjani , Andrea Tirinzoni , Emilie Kaufmann

In this work we present strategies for (optimal) measurement selection in model-based sequential diagnosis. In particular, assuming a set of leading diagnoses being given, we show how queries (sets of measurements) can be computed and…

Artificial Intelligence · Computer Science 2017-05-30 Patrick Rodler , Wolfgang Schmid , Konstantin Schekotihin

Dealing with Partially Observable Markov Decision Processes is notably a challenging task. We face an average-reward infinite-horizon POMDP setting with an unknown transition model, where we assume the knowledge of the observation model.…

Machine Learning · Computer Science 2024-10-03 Alessio Russo , Alberto Maria Metelli , Marcello Restelli

We formulate a private learning model to study an intrinsic tradeoff between privacy and query complexity in sequential learning. Our model involves a learner who aims to determine a scalar value, $v^*$, by sequentially querying an external…

Machine Learning · Computer Science 2020-02-27 John N. Tsitsiklis , Kuang Xu , Zhi Xu

Sequential learning -- where complex tasks are broken down into simpler, hierarchical components -- has emerged as a paradigm in AI. This paper views sequential learning through the lens of low-rank linear regression, focusing specifically…

Machine Learning · Computer Science 2025-05-29 Mahtab Alizadeh Vandchali , Fangshuo , Liao , Anastasios Kyrillidis

In this paper, we consider the problem of estimating parameters in a linear regression model. We propose a sequential learning procedure to determine the sample size for achieving a given small estimation risk, under the widely used…

Methodology · Statistics 2023-11-07 Jun Hu , Yan Zhuang , Shunan Zhao

In most real-world applications of artificial intelligence, the distributions of the data and the goals of the learners tend to change over time. The Probably Approximately Correct (PAC) learning framework, which underpins most machine…

Machine Learning · Computer Science 2025-11-13 Yuxin Bai , Cecelia Shuai , Ashwin De Silva , Siyu Yu , Pratik Chaudhari , Joshua T. Vogelstein

Object pose distribution estimation is crucial in robotics for better path planning and handling of symmetric objects. Recent distribution estimation approaches employ contrastive learning-based approaches by maximizing the likelihood of a…

Computer Vision and Pattern Recognition · Computer Science 2024-09-12 Shishir Reddy Vutukur , Rasmus Laurvig Haugaard , Junwen Huang , Benjamin Busam , Tolga Birdal

We consider the segmentation problem of univariate distributions from the exponential family with multiple parameters. In segmentation, the choice of the number of segments remains a difficult issue due to the discrete nature of the…

Statistics Theory · Mathematics 2015-03-27 Alice Cleynen , Emilie Lebarbier

Inference-time scaling methods rely on Process Reward Models (PRMs), which are often poorly calibrated and overestimate success probabilities. We propose, to our knowledge, the first use of conditional optimal transport for calibrating…

Machine Learning · Computer Science 2026-05-13 Rachel Ma , Dylan Hadfield-Menell , Kristjan Greenewald

We explore the sequential decision making problem where the goal is to estimate uniformly well a number of linear models, given a shared budget of random contexts independently sampled from a known distribution. The decision maker must…

Machine Learning · Statistics 2017-08-01 Carlos Riquelme , Mohammad Ghavamzadeh , Alessandro Lazaric

Statistical query (SQ) algorithms are algorithms that have access to an {\em SQ oracle} for the input distribution $D$ instead of i.i.d.~ samples from $D$. Given a query function $\phi:X \rightarrow [-1,1]$, the oracle returns an estimate…

Machine Learning · Computer Science 2017-04-18 Vitaly Feldman

Given two distributions $\mathcal{P}$ and $\mathcal{Q}$ over a high-dimensional domain $\{0,1\}^n$, and a parameter $\varepsilon$, the goal of distance estimation is to determine the statistical distance between $\mathcal{P}$ and…

Data Structures and Algorithms · Computer Science 2025-09-09 Gunjan Kumar , Kuldeep S. Meel , Yash Pote
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