Cover Image

Learning-Augmented Online Scheduling with Parsimonious Preemption

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Blue, Mugen, Im, Sungjin, Lindermayr, Alexander
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Data Structures and Algorithms
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Parsimonious Learning-Augmented Online Metric Matching
by: Shin, Yongho, et al.
Published: (2026)

Online Scheduling via Gradient Descent for Weighted Flow Time Minimization
by: Chen, Qingyun, et al.
Published: (2024)

Delayed-Clairvoyant Flow Time Scheduling via a Borrow Graph Analysis
by: Lindermayr, Alexander, et al.
Published: (2026)

Binary Search with Distributional Predictions
by: Dinitz, Michael, et al.
Published: (2024)

The Power of Proportional Fairness for Non-Clairvoyant Scheduling under Polyhedral Constraints
by: Jäger, Sven, et al.
Published: (2024)

Accelerating Matroid Optimization through Fast Imprecise Oracles
by: Eberle, Franziska, et al.
Published: (2024)

Learning-Augmented Online Packet Scheduling with Deadlines
by: Liang, Ya-Chun, et al.
Published: (2023)

Non-Clairvoyant Scheduling with Progress Bars
by: Benomar, Ziyad, et al.
Published: (2025)

Transductive and Learning-Augmented Online Regression
by: Raman, Vinod, et al.
Published: (2025)

Learning-Augmented Algorithms for $k$-median via Online Learning
by: Hebbar, Anish, et al.
Published: (2026)

Online Conversion with Switching Costs: Robust and Learning-Augmented Algorithms
by: Lechowicz, Adam, et al.
Published: (2023)

Learning-Augmented Online Bipartite Matching in the Random Arrival Order Model
by: Burathep, Kunanon, et al.
Published: (2025)

Online Flow Time Minimization with Gradually Revealed Jobs
by: Lindermayr, Alexander, et al.
Published: (2026)

A Simpler Analysis for $\varepsilon$-Clairvoyant Flow Time Scheduling
by: Gupta, Anupam, et al.
Published: (2026)

Online Distributed Queue Length Estimation
by: Bhaskara, Aditya, et al.
Published: (2025)

Polytope Scheduling with Groups: Unified Models and Optimal Guarantees
by: Lindermayr, Alexander, et al.
Published: (2025)

Learning-Augmented Online Bipartite Fractional Matching
by: Choo, Davin, et al.
Published: (2025)

Robust Learning-Augmented Dictionaries
by: Zeynali, Ali, et al.
Published: (2024)

Learning-Augmented Hierarchical Clustering
by: Braverman, Vladimir, et al.
Published: (2025)

Learning-Augmented Frequent Directions
by: Aamand, Anders, et al.
Published: (2025)

Two-Edge Connectivity via Pac-Man Gluing
by: Garg, Mohit, et al.
Published: (2024)

A Better-Than-$5/4$-Approximation for Two-Edge Connectivity
by: Hommelsheim, Felix, et al.
Published: (2025)

Indirect Coflow Scheduling
by: Lindermayr, Alexander, et al.
Published: (2025)

Tradeoffs between Mistakes and ERM Oracle Calls in Online and Transductive Online Learning
by: Attias, Idan, et al.
Published: (2025)

Learning Augmented Graph $k$-Clustering
by: Fan, Chenglin, et al.
Published: (2025)

On the Power of Learning-Augmented Search Trees
by: Chen, Jingbang, et al.
Published: (2022)

Learning-Augmented Algorithms with Explicit Predictors
by: Elias, Marek, et al.
Published: (2024)

Learning-Augmented Search Data Structures
by: Fu, Chunkai, et al.
Published: (2024)

Online Learning in the Random Order Model
by: Bernasconi, Martino, et al.
Published: (2025)

Learning on the Edge: Online Learning with Stochastic Feedback Graphs
by: Esposito, Emmanuel, et al.
Published: (2022)

Parsimonious Learning-Augmented Approximations for Dense Instances of $\mathcal{NP}$-hard Problems
by: Bampis, Evripidis, et al.
Published: (2024)

Energy-Efficient Scheduling with Predictions
by: Balkanski, Eric, et al.
Published: (2024)

Towards Optimal Robustness in Learning-Augmented Paging
by: Chen, Peng, et al.
Published: (2026)

Learning-Augmented Frequency Estimation in Sliding Windows
by: Shahout, Rana, et al.
Published: (2024)

Learning-Augmented Streaming Algorithms for Correlation Clustering
by: Dong, Yinhao, et al.
Published: (2025)

Prediction-Specific Design of Learning-Augmented Algorithms
by: Li, Sizhe, et al.
Published: (2025)

Learning-Augmented Moment Estimation on Time-Decay Models
by: Nagawanshi, Soham, et al.
Published: (2026)

Decision-Theoretic Approaches for Improved Learning-Augmented Algorithms
by: Angelopoulos, Spyros, et al.
Published: (2025)

Overcoming Brittleness in Pareto-Optimal Learning-Augmented Algorithms
by: Angelopoulos, Spyros, et al.
Published: (2024)

Online Learning with Limited Information in the Sliding Window Model
by: Braverman, Vladimir, et al.
Published: (2026)