skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


  • Home
  • Search Results
  • Page 1 of 2

Search for: All records

Award ID contains: 2334448

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. ; ; ; (, International Conference of Machine Learning (ICML))
    Free, publicly-accessible full text available July 9, 2026
  2. ; ; ; (, International Conference on Learning Representations (ICLR))
    Free, publicly-accessible full text available January 31, 2026
  3. ; ; (, AI For Material Science workshop, at NeurIPS-24)
    Free, publicly-accessible full text available January 1, 2026
  4. ; ; (, Advances in Neural Information Processing Systems (NeurIPS))
    Free, publicly-accessible full text available January 1, 2026
  5. ; ; ; (, IEEE Conference on Decision and Control (CDC))
    Free, publicly-accessible full text available November 1, 2025
  6. ; ; ; ; (, European Conference on Artificial Intelligence (ECAI))
    Accepted Manuscript Full Text Available
  7. ; ; ; ; ; ; (, Journal of artificial intelligence research)
    Decision-focused learning (DFL) is an emerging paradigm that integrates machine learning (ML) and constrained optimization to enhance decision quality by training ML models in an end-to-end system. This approach shows significant potential to revolutionize combinatorial decision-making in real-world applications that operate under uncertainty, where estimating unknown parameters within decision models is a major challenge. This paper presents a comprehensive review of DFL, providing an in-depth analysis of both gradient-based and gradient-free techniques used to combine ML and constrained optimization. It evaluates the strengths and limitations of these techniques and includes an extensive empirical evaluation of eleven methods across seven problems. The survey also offers insights into recent advancements and future research directions in DFL. 
    more » « less
    Accepted Manuscript Full Text Available
  8. ; ; ; ; (, arXivorg)
    Accepted Manuscript Full Text Available
  9. ; (, Advances in neural information processing systems)
    Accepted Manuscript Full Text Available
  10. ; (, NeurIPS 2023 Workshop: Tackling Climate Change with Machine Learning)
    While deep learning gradually penetrates operational planning, its inherent prediction errors may significantly affect electricity prices. This letter examines how prediction errors propagate into electricity prices, revealing notable pricing errors and their spatial disparity in congested power systems. To improve fairness, we propose to embed electricity market-clearing optimization as a deep learning layer. Differentiating through this layer allows for balancing between prediction and pricing errors, as oppose to minimizing prediction errors alone. This layer implicitly optimizes fairness and controls the spatial distribution of price errors across the system. We showcase the price-aware deep learning in the nexus of wind power forecasting and short-term electricity market clearing. 
    more » « less