arXiv Machine Learning
Learning Samples Importance: Parameterizing Dual Variables in Everywhere Learning
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arXiv Machine Learning
Jun 2
Everywhere Learning: Artificial Intelligence with Pointwise Constraints
arXiv:2606. 01557v1 Announce Type: new Abstract: Everywhere learning is a new paradigm whereby Artificial Intelligence (AI) systems are trained to satisfy loss constraints with probability one over the data distribution.
arXiv Machine Learning
Jul 17
Primal-dual algorithm for contextual stochastic combinatorial optimization
arXiv:2505. 04757v2 Announce Type: replace Abstract: This paper introduces a novel approach to contextual stochastic optimization, integrating operations research and machine learning to address decision-making under uncertainty.
arXiv Machine Learning
Sep 11
Generalization Guarantees on Data-Driven Tuning of Gradient Descent with Langevin Updates
arXiv:2604. 13130v2 Announce Type: replace Abstract: We study learning to learn through the lens of hyperparameter tuning.
arXiv Machine Learning
Jul 7
Contaminated Multi-task Learning with Heterogeneity: Fundamental Limits and Optimal Algorithms
arXiv:2607. 02681v1 Announce Type: cross Abstract: Integrating information across related tasks can improve estimation and prediction in transfer, multi-task, and federated learning, but contamination and heterogeneity make robust borrowing challenging.
arXiv Statistics ML
Sep 10
A Farewell to the Bias-Variance Tradeoff? An Overview of the Theory of Overparameterized Machine Learning
arXiv:2109.02355v2 Announce Type: replace Abstract: The last decade of progress in machine learning (ML), especially the deep learning era, has raised a number of scientific questions that challenge...
arXiv Machine Learning
1d ago
HUANet: Hard-Constrained Unrolled ADMM for Constrained Convex Optimization
HUANet is a deep neural network architecture that unrolls the Alternating Direction Method of Multipliers (ADMM) into a trainable model for accelerating parametric constrained convex optimization. It embeds a hard‑constrained neural network in each ADMM iteration, using a differentiable correction stage to enforce affine equalities of the primal subproblem. The method also incorporates first‑order optimality conditions into a self‑supervised training loss, and numerical experiments on benchmark problems and a control application demonstrate its effectiveness in speeding up constrained convex optimization.