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.
Data-dependent Evaluations for Budgeted Submodular Maximization
arXiv:2607. 05759v1 Announce Type: cross Abstract: Submodular maximization is an important building block for developing algorithms in many areas such as machine learning and data mining.
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arXiv Machine Learning
Jun 4
A General Framework for Dynamic Consistent Submodular Maximization
arXiv:2606. 04946v1 Announce Type: cross Abstract: Consistency is an important property in dynamic submodular maximization and entails maintaining a near-optimal solution at all times, making only a small number of adjustments to the solution in each step.
arXiv AI
Jul 29
Finding Optimal Cost-Bounded Plan Reductions: Refined Model
arXiv:2607. 25484v1 Announce Type: new Abstract: In some real applications a plan may later become unfeasible due to newly imposed budget constraints, yet, at the same time, using only the original actions of the plan and their order is mandatory.
arXiv Machine Learning
Jul 27
Complexity Bounds and Approaches to Learning Projected Gradient Descent Solver Iterates
arXiv:2607. 22467v1 Announce Type: new Abstract: Data scarcity poses a fundamental challenge in training generative models to produce initial guesses for parametric optimization problems that are otherwise numerically expensive to solve.
arXiv Machine Learning
Jun 15
Which Directions Matter? Sparse Design for Affine Robust Optimization
arXiv:2606. 14648v1 Announce Type: new Abstract: Robust machine learning and optimization rely on the uncertainty model choice.
arXiv Machine Learning
Jul 27
Explicit Iteration Complexity of Exact Data-Driven Inverse Optimization for Integer Linear Programs
arXiv:2607. 22263v1 Announce Type: cross Abstract: A data-driven inverse optimization problem (DDIOP) is the problem of estimating the objective-function parameters (weights) that explain observed optimal-solution data, and it arises in many applications, including integer linear programming (ILP).
arXiv Machine Learning
6d ago
Reoptimization Algorithms for Contextual Bandits with Knapsack Constraints
arXiv:2608. 11383v1 Announce Type: new Abstract: We study new algorithms for Contextual Bandits with Knapsack.
arXiv Machine Learning
Jul 21
Regret Optimality of Sample Average Approximation for Data-Driven Newsvendor Problems: A General Optimization Perspective
arXiv:2407. 04900v2 Announce Type: replace Abstract: Numerous existing studies have examined the performance of Sample Average Approximation (SAA) in the fundamental newsvendor problem.
arXiv AI
Jun 9
Scaling Decision-Focused Learning to Large Problems with Lagrangian Decomposition
arXiv:2606. 08797v1 Announce Type: cross Abstract: Decision-focused learning has shown great promise for addressing predict-then-optimize problems, particularly in the presence of under-specified models.
arXiv Machine Learning
Jul 13
Information-Theoretic Bayesian Optimization for Bilevel Optimization Problems
arXiv:2509. 21725v3 Announce Type: replace Abstract: A bilevel optimization problem consists of two optimization problems nested as an upper- and a lower-level problem, in which the optimality of the lower-level problem defines a constraint for the upper-level problem.
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 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.