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We study the complexity of smoothed agnostic learning, recently introduced by~\cite{CKKMS24}, in which the learner competes with the best classifier in a target class under slight Gaussian perturbations of the inputs. Specifically, we focus…

Machine Learning · Computer Science 2026-02-25 Ilias Diakonikolas , Daniel M. Kane

Statistical inverse learning aims at recovering an unknown function $f$ from randomly scattered and possibly noisy point evaluations of another function $g$, connected to $f$ via an ill-posed mathematical model. In this paper we blend…

Statistics Theory · Mathematics 2024-01-22 Tapio Helin

This paper investigates whether sequence models can learn to perform numerical algorithms, e.g. gradient descent, on the fundamental problem of least squares. Our goal is to inherit two properties of standard algorithms from numerical…

Machine Learning · Computer Science 2025-03-18 Jerry Liu , Jessica Grogan , Owen Dugan , Ashish Rao , Simran Arora , Atri Rudra , Christopher Ré

The predictive quality of machine learning models is typically measured in terms of their (approximate) expected prediction accuracy or the so-called Area Under the Curve (AUC). Minimizing the reciprocals of these measures are the goals of…

Machine Learning · Statistics 2019-03-04 Hiva Ghanbari , Minhan Li , Katya Scheinberg

Most modern learning problems are highly overparameterized, meaning that there are many more parameters than the number of training data points, and as a result, the training loss may have infinitely many global minima (parameter vectors…

Machine Learning · Computer Science 2019-06-11 Navid Azizan , Sahin Lale , Babak Hassibi

We develop a general framework for estimating function-valued parameters under equality or inequality constraints in infinite-dimensional statistical models. Such constrained learning problems are common across many areas of statistics and…

Machine Learning · Statistics 2025-07-22 Razieh Nabi , Nima S. Hejazi , Mark J. van der Laan , David Benkeser

Acquisition of data is a difficult task in many applications of machine learning, and it is only natural that one hopes and expects the population risk to decrease (better performance) monotonically with increasing data points. It turns…

Machine Learning · Computer Science 2022-01-19 Zakaria Mhammedi

We study the fundamental mistake bound and sample complexity in the strategic classification, where agents can strategically manipulate their feature vector up to an extent in order to be predicted as positive. For example, given a…

Machine Learning · Computer Science 2024-01-17 Han Shao , Avrim Blum , Omar Montasser

We study classification problems using binary estimators where the decision boundary is described by horizon functions and where the data distribution satisfies a geometric margin condition. A key novelty of our work is the derivation of…

Machine Learning · Statistics 2026-03-16 Jonathan García , Philipp Petersen

Reinforcement Learning (RL) has demonstrated a huge potential in learning optimal policies without any prior knowledge of the process to be controlled. Model Predictive Control (MPC) is a popular control technique which is able to deal with…

Systems and Control · Computer Science 2019-04-10 Mario Zanon , Sébastien Gros , Alberto Bemporad

Semi-supervised learning (SSL) has shown notable potential in relieving the heavy demand of dense prediction tasks on large-scale well-annotated datasets, especially for the challenging multi-organ segmentation (MoS). However, the…

Computer Vision and Pattern Recognition · Computer Science 2025-01-08 Zhenghao Feng , Lu Wen , Yuanyuan Xu , Binyu Yan , Xi Wu , Jiliu Zhou , Yan Wang

Logistic models are commonly used for binary classification tasks. The success of such models has often been attributed to their connection to maximum-likelihood estimators. It has been shown that gradient descent algorithm, when applied on…

Machine Learning · Statistics 2020-10-30 Fariborz Salehi , Ehsan Abbasi , Babak Hassibi

Despite the development of numerous adaptive optimizers, tuning the learning rate of stochastic gradient methods remains a major roadblock to obtaining good practical performance in machine learning. Rather than changing the learning rate…

Machine Learning · Statistics 2019-09-27 Hunter Lang , Pengchuan Zhang , Lin Xiao

We give sublinear-time approximation algorithms for some optimization problems arising in machine learning, such as training linear classifiers and finding minimum enclosing balls. Our algorithms can be extended to some kernelized versions…

Machine Learning · Computer Science 2010-10-22 Kenneth L. Clarkson , Elad Hazan , David P. Woodruff

We address the problem of learning to benchmark the best achievable classifier performance. In this problem the objective is to establish statistically consistent estimates of the Bayes misclassification error rate without having to learn a…

Machine Learning · Statistics 2019-09-17 Morteza Noshad , Li Xu , Alfred Hero

Class imbalance remains a major challenge in machine learning, especially in multi-class problems with long-tailed distributions. Existing methods, such as data resampling, cost-sensitive techniques, and logistic loss modifications, though…

Machine Learning · Computer Science 2025-12-30 Corinna Cortes , Anqi Mao , Mehryar Mohri , Yutao Zhong

Stochastic gradient descent is one of the most successful approaches for solving large-scale problems, especially in machine learning and statistics. At each iteration, it employs an unbiased estimator of the full gradient computed from one…

Numerical Analysis · Mathematics 2018-12-05 Bangti Jin , Xiliang Lu

State space models (SSMs) are a flexible approach to modeling complex time series. However, inference in SSMs is often computationally prohibitive for long time series. Stochastic gradient MCMC (SGMCMC) is a popular method for scalable…

Machine Learning · Statistics 2019-07-11 Christopher Aicher , Yi-An Ma , Nicholas J. Foti , Emily B. Fox

Support vector regression (SVR) is one of the most popular machine learning algorithms aiming to generate the optimal regression curve through maximizing the minimal margin of selected training samples, i.e., support vectors. Recent…

Machine Learning · Computer Science 2019-05-07 Gaoyang Li , Jinyu Yang , Chunguo Wu , Qin Ma

Within the machine learning community, the widely-used uniform convergence framework has been used to answer the question of how complex, over-parameterized models can generalize well to new data. This approach bounds the test error of the…

Machine Learning · Statistics 2021-03-05 Ryan Theisen , Jason M. Klusowski , Michael W. Mahoney
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