arXiv Machine Learning
Jun 2
Towards Optimal Robustness in Learning-Augmented Paging
arXiv:2606. 01342v1 Announce Type: cross Abstract: Learning-augmented paging has been extensively studied in recent years.
Learning-Augmented Algorithms: Guarantees, Construction Mechanisms, and System-Level Implications
Learning-augmented algorithms combine fallible predictions with formal performance guarantees. This survey reviews prediction interfaces, error measures, consistency–robustness trade-offs, and five construction mechanisms across online optimization, caching, learned data structures, graph problems, and mechanism design. It distinguishes theorem-level upper bounds from matched asymptotic dependence, separates formal guarantees from empirical evidence, and outlines open problems in cost-aware prediction, endogenous error, semantic predictors, and benchmarking.
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arXiv Machine Learning
Jun 18
Learning Augmented Exact Exponential Algorithms
arXiv:2606. 18807v1 Announce Type: cross Abstract: The field of learning-augmented algorithms has demonstrated that machine-learned predictions can bypass worst-case lower bounds across a wide range of problems.
arXiv AI
Jun 2
Position Paper: Post-Solve Robustness in Decision Engines: Feasible Regions and Smoothness Under Perturbations
arXiv:2606. 00002v1 Announce Type: new Abstract: Mixed-Integer Linear Programming (MILP) decision engines routinely output nominally optimal plans for high-stakes industrial systems.
arXiv Machine Learning
Aug 4
T-TAMER: Provably Taming Trade-offs in ML Serving
arXiv:2509. 22992v2 Announce Type: replace Abstract: As machine learning models continue to grow in size and complexity, efficient serving faces increasingly broad trade-offs spanning accuracy, latency, resource usage, and other objectives.
Hugging Face Trending Papers
Jun 11
Learning-Augmented Approximation for Unrelated-Machines Makespan Scheduling
Recently, Antoniadis et al. (ICLR 2025) proposed a framework for incorporating predictions to approximate NP-hard selection problems.
arXiv Machine Learning
Jun 2
Learning-Augmented Scalable Linear Assignment Problem Optimization via Neural Dual Warm-Starts
arXiv:2605. 09382v2 Announce Type: replace Abstract: The Linear Assignment Problem is a fundamental combinatorial optimization task where classical exact solvers ensure optimality but suffer from an $\mathcal{O}(N^{3})$ bottleneck, while recent neural approximations struggle with scalability and exactness.
arXiv Machine Learning
Jun 5
Learning-Augmented Online Minimization with Dual Predictions
arXiv:2606. 05380v1 Announce Type: cross Abstract: We present learning-augmented algorithms for two general classes of online minimization problems: metrical task systems and laminar set cover.
arXiv Machine Learning
Jun 19
A Solver-Free Training Method for Predict-then-Optimize
arXiv:2606. 19587v1 Announce Type: cross Abstract: We propose a scalable method for training prediction (machine learning) models in the predict-then-optimize paradigm, where model outputs serve as coefficients for a subsequent linear optimization task.
arXiv AI
Aug 28
Learning-Augmented Online Allocation under Unreliable Advice: Robustness, Exposure Fairness, and Distribution Shift
The paper introduces a learning‑augmented algorithm for online allocation that handles unreliable predictions. It addresses finite candidate sets, irreversible decisions, and exposure constraints by combining advice with a conservative fallback and a fairness correction. The authors prove consistency and robustness under bounded‑error assumptions and demonstrate experimentally that the method remains stable against adversarial advice while substantially reducing exposure disparity.
arXiv Machine Learning
Jun 9
LARP: Learner-Agnostic Robust Data Prefiltering
arXiv:2506. 20573v4 Announce Type: replace-cross Abstract: Public datasets, crucial for modern machine learning and statistical inference, often contain low-quality or contaminated samples that can harm model performance.
arXiv AI
3d ago
TACIT: Optimization Models that Learn from Their Mistakes
arXiv:2609.38434v1 Announce Type: cross Abstract: Real-world optimization problems are difficult to model accurately because many objectives and constraints reside in domain experts' tacit knowledge,...
arXiv Machine Learning
Jul 31
Learning-Augmented Algorithms for Online Vertex Cover
arXiv:2606. 22831v2 Announce Type: replace-cross Abstract: This paper studies learning-augmented online weighted vertex cover with local advice and a tradeoff parameter $\lambda \in (0,1)$.