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Tag: error

  • Error Analysis of Bayesian Inverse Problems with Generative Priors

    Error Analysis of Bayesian Inverse Problems with Generative Priors arXiv:2601.17374v1 Announce Type: new Abstract: Data-driven methods for the solution of inverse problems have become widely popular in recent years thanks to the rise of machine learning techniques. A popular approach concerns the training of a generative model on additional data to learn a bespoke prior…

  • Error Analysis of Generalized Langevin Equations with Approximated Memory Kernels

    Error Analysis of Generalized Langevin Equations with Approximated Memory Kernels arXiv:2512.10256v1 Announce Type: new Abstract: We analyze prediction error in stochastic dynamical systems with memory, focusing on generalized Langevin equations (GLEs) formulated as stochastic Volterra equations. We establish that, under a strongly convex potential, trajectory discrepancies decay at a rate determined by the decay of…

  • Accuracy estimation of neural networks by extreme value theory

    Accuracy estimation of neural networks by extreme value theory arXiv:2511.00490v1 Announce Type: new Abstract: Neural networks are able to approximate any continuous function on a compact set. However, it is not obvious how to quantify the error of the neural network, i.e., the remaining bias between the function and the neural network. Here, we propose…

  • Understanding Fairness and Prediction Error through Subspace Decomposition and Influence Analysis

    Understanding Fairness and Prediction Error through Subspace Decomposition and Influence Analysis arXiv:2510.23935v1 Announce Type: new Abstract: Machine learning models have achieved widespread success but often inherit and amplify historical biases, resulting in unfair outcomes. Traditional fairness methods typically impose constraints at the prediction level, without addressing underlying biases in data representations. In this work, we…

  • High-Order Error Bounds for Markovian LSA with Richardson-Romberg Extrapolation

    High-Order Error Bounds for Markovian LSA with Richardson-Romberg Extrapolation arXiv:2508.05570v1 Announce Type: new Abstract: In this paper, we study the bias and high-order error bounds of the Linear Stochastic Approximation (LSA) algorithm with Polyak-Ruppert (PR) averaging under Markovian noise. We focus on the version of the algorithm with constant step size $alpha$ and propose a…

  • How does Labeling Error Impact Contrastive Learning? A Perspective from Data Dimensionality Reduction

    How does Labeling Error Impact Contrastive Learning? A Perspective from Data Dimensionality Reduction arXiv:2507.11161v1 Announce Type: new Abstract: In recent years, contrastive learning has achieved state-of-the-art performance in the territory of self-supervised representation learning. Many previous works have attempted to provide the theoretical understanding underlying the success of contrastive learning. Almost all of them rely…

  • Classification with Reject Option: Distribution-free Error Guarantees via Conformal Prediction

    Classification with Reject Option: Distribution-free Error Guarantees via Conformal Prediction arXiv:2506.21802v1 Announce Type: new Abstract: Machine learning (ML) models always make a prediction, even when they are likely to be wrong. This causes problems in practical applications, as we do not know if we should trust a prediction. ML with reject option addresses this issue…

  • Overfitting has a limitation: a model-independent generalization error bound based on R’enyi entropy

    Overfitting has a limitation: a model-independent generalization error bound based on R’enyi entropy arXiv:2506.00182v1 Announce Type: new Abstract: Will further scaling up of machine learning models continue to bring success? A significant challenge in answering this question lies in understanding generalization error, which is the impact of overfitting. Understanding generalization error behavior of increasingly large-scale…

  • Lower Bounds on the MMSE of Adversarially Inferring Sensitive Features

    Lower Bounds on the MMSE of Adversarially Inferring Sensitive Features arXiv:2505.09004v1 Announce Type: new Abstract: We propose an adversarial evaluation framework for sensitive feature inference based on minimum mean-squared error (MMSE) estimation with a finite sample size and linear predictive models. Our approach establishes theoretical lower bounds on the true MMSE of inferring sensitive features…

  • Safe-EF: Error Feedback for Nonsmooth Constrained Optimization

    Safe-EF: Error Feedback for Nonsmooth Constrained Optimization arXiv:2505.06053v1 Announce Type: cross Abstract: Federated learning faces severe communication bottlenecks due to the high dimensionality of model updates. Communication compression with contractive compressors (e.g., Top-K) is often preferable in practice but can degrade performance without proper handling. Error feedback (EF) mitigates such issues but has been largely…

  • Data Science: From School to Work, Part III

    Data Science: From School to Work, Part III Introduction Writing code is about solving problems, but not every problem is predictable. In the real world, your software will encounter unexpected situations: missing files, invalid user inputs, network timeouts, or even hardware failures. This is why handling errors isn’t just a nice-to-have; it’s a critical part…