Tag: problems
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Conditional neural control variates for variance reduction in Bayesian inverse problems
Conditional neural control variates for variance reduction in Bayesian inverse problems arXiv:2602.21357v1 Announce Type: new Abstract: Bayesian inference for inverse problems involves computing expectations under posterior distributions — e.g., posterior means, variances, or predictive quantities — typically via Monte Carlo (MC) estimation. When the quantity of interest varies significantly under the posterior, accurate estimates demand…
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Not All RecSys Problems Are Created Equal
Not All RecSys Problems Are Created Equal How baseline strength, churn, and subjectivity determine complexity The post Not All RecSys Problems Are Created Equal appeared first on Towards Data Science. Diogo Leitão Go to original source
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Latent-IMH: Efficient Bayesian Inference for Inverse Problems with Approximate Operators
Latent-IMH: Efficient Bayesian Inference for Inverse Problems with Approximate Operators arXiv:2601.20888v1 Announce Type: new Abstract: We study sampling from posterior distributions in Bayesian linear inverse problems where $A$, the parameters to observables operator, is computationally expensive. In many applications, $A$ can be factored in a manner that facilitates the construction of a cost-effective approximation $tilde{A}$.…
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Data Science Spotlight: Selected Problems from Advent of Code 2025
Data Science Spotlight: Selected Problems from Advent of Code 2025 Hands-on walkthroughs of problems and solution approaches that power real‑world data science use cases The post Data Science Spotlight: Selected Problems from Advent of Code 2025 appeared first on Towards Data Science. Chinmay Kakatkar Go to original source
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Online Inference of Constrained Optimization: Primal-Dual Optimality and Sequential Quadratic Programming
Online Inference of Constrained Optimization: Primal-Dual Optimality and Sequential Quadratic Programming arXiv:2512.08948v1 Announce Type: new Abstract: We study online statistical inference for the solutions of stochastic optimization problems with equality and inequality constraints. Such problems are prevalent in statistics and machine learning, encompassing constrained $M$-estimation, physics-informed models, safe reinforcement learning, and algorithmic fairness. We develop…
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Heuristics for Combinatorial Optimization via Value-based Reinforcement Learning: A Unified Framework and Analysis
Heuristics for Combinatorial Optimization via Value-based Reinforcement Learning: A Unified Framework and Analysis arXiv:2512.08601v1 Announce Type: new Abstract: Since the 1990s, considerable empirical work has been carried out to train statistical models, such as neural networks (NNs), as learned heuristics for combinatorial optimization (CO) problems. When successful, such an approach eliminates the need for experts…
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Bayesian Physics-Informed Neural Networks for Inverse Problems (BPINN-IP): Application in Infrared Image Processing
Bayesian Physics-Informed Neural Networks for Inverse Problems (BPINN-IP): Application in Infrared Image Processing arXiv:2512.02495v1 Announce Type: new Abstract: Inverse problems arise across scientific and engineering domains, where the goal is to infer hidden parameters or physical fields from indirect and noisy observations. Classical approaches, such as variational regularization and Bayesian inference, provide well established theoretical…
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Data-driven Projection Generation for Efficiently Solving Heterogeneous Quadratic Programming Problems
Data-driven Projection Generation for Efficiently Solving Heterogeneous Quadratic Programming Problems arXiv:2510.26061v1 Announce Type: new Abstract: We propose a data-driven framework for efficiently solving quadratic programming (QP) problems by reducing the number of variables in high-dimensional QPs using instance-specific projection. A graph neural network-based model is designed to generate projections tailored to each QP instance, enabling…
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Bilevel optimization for learning hyperparameters: Application to solving PDEs and inverse problems with Gaussian processes
Bilevel optimization for learning hyperparameters: Application to solving PDEs and inverse problems with Gaussian processes arXiv:2510.05568v1 Announce Type: new Abstract: Methods for solving scientific computing and inference problems, such as kernel- and neural network-based approaches for partial differential equations (PDEs), inverse problems, and supervised learning tasks, depend crucially on the choice of hyperparameters. Specifically, the…
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Risk Comparisons in Linear Regression: Implicit Regularization Dominates Explicit Regularization
Risk Comparisons in Linear Regression: Implicit Regularization Dominates Explicit Regularization arXiv:2509.17251v1 Announce Type: new Abstract: Existing theory suggests that for linear regression problems categorized by capacity and source conditions, gradient descent (GD) is always minimax optimal, while both ridge regression and online stochastic gradient descent (SGD) are polynomially suboptimal for certain categories of such problems.…
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Assumption-free stability for ranking problems
Assumption-free stability for ranking problems arXiv:2506.02257v1 Announce Type: new Abstract: In this work, we consider ranking problems among a finite set of candidates: for instance, selecting the top-$k$ items among a larger list of candidates or obtaining the full ranking of all items in the set. These problems are often unstable, in the sense that…
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Preconditioned Langevin Dynamics with Score-Based Generative Models for Infinite-Dimensional Linear Bayesian Inverse Problems
Preconditioned Langevin Dynamics with Score-Based Generative Models for Infinite-Dimensional Linear Bayesian Inverse Problems arXiv:2505.18276v1 Announce Type: new Abstract: Designing algorithms for solving high-dimensional Bayesian inverse problems directly in infinite-dimensional function spaces – where such problems are naturally formulated – is crucial to ensure stability and convergence as the discretization of the underlying problem is refined.…
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Selective Reviews of Bandit Problems in AI via a Statistical View
Selective Reviews of Bandit Problems in AI via a Statistical View arXiv:2412.02251v1 Announce Type: new Abstract: Reinforcement Learning (RL) is a widely researched area in artificial intelligence that focuses on teaching agents decision-making through interactions with their environment. A key subset includes stochastic multi-armed bandit (MAB) and continuum-armed bandit (SCAB) problems, which model sequential decision-making…