DFF: Decision-Focused Fine-tuning for Smarter Predict-then-Optimize with Limited Data

• URL: http://arxiv.org/abs/2501.01874v1
• Date: Fri, 03 Jan 2025 15:46:25 GMT
• Title: DFF: Decision-Focused Fine-tuning for Smarter Predict-then-Optimize with Limited Data
• Authors: Jiaqi Yang, Enming Liang, Zicheng Su, Zhichao Zou, Peng Zhen, Jiecheng Guo, Wanjing Ma, Kun An,
• Abstract summary: Decision-focused learning (DFL) offers an end-to-end approach to the predict-then-optimize (PO) framework by training predictive models directly on decision loss (DL) Some predictive models are non-differentiable or black-box, which cannot be adjusted using gradient-based methods. We propose a novel framework, Decision-Focused Fine-tuning (DFF), which embeds the DFL module into the PO pipeline via a novel bias correction module.
• Score: 7.70699448711673
• License:
• Abstract: Decision-focused learning (DFL) offers an end-to-end approach to the predict-then-optimize (PO) framework by training predictive models directly on decision loss (DL), enhancing decision-making performance within PO contexts. However, the implementation of DFL poses distinct challenges. Primarily, DL can result in deviation from the physical significance of the predictions under limited data. Additionally, some predictive models are non-differentiable or black-box, which cannot be adjusted using gradient-based methods. To tackle the above challenges, we propose a novel framework, Decision-Focused Fine-tuning (DFF), which embeds the DFL module into the PO pipeline via a novel bias correction module. DFF is formulated as a constrained optimization problem that maintains the proximity of the DL-enhanced model to the original predictive model within a defined trust region. We theoretically prove that DFF strictly confines prediction bias within a predetermined upper bound, even with limited datasets, thereby substantially reducing prediction shifts caused by DL under limited data. Furthermore, the bias correction module can be integrated into diverse predictive models, enhancing adaptability to a broad range of PO tasks. Extensive evaluations on synthetic and real-world datasets, including network flow, portfolio optimization, and resource allocation problems with different predictive models, demonstrate that DFF not only improves decision performance but also adheres to fine-tuning constraints, showcasing robust adaptability across various scenarios.

Related papers

• Gen-DFL: Decision-Focused Generative Learning for Robust Decision Making [48.62706690668867]
  Decision-focused generative learning (Gen-DFL) is a novel framework that leverages generative models to adaptively model uncertainty and improve decision quality. The paper shows, theoretically, that Gen-DFL achieves improved worst-case performance bounds compared to traditional DFL.
  arXiv  Detail & Related papers  (2025-02-08T06:52:11Z)
• Self-Evolutionary Large Language Models through Uncertainty-Enhanced Preference Optimization [9.618391485742968]
  Iterative preference optimization has recently become one of the de-facto training paradigms for large language models (LLMs) We present an uncertainty-enhanced textbfPreference textbfOptimization framework to make the LLM self-evolve with reliable feedback. Our framework substantially alleviates the noisy problem and improves the performance of iterative preference optimization.
  arXiv  Detail & Related papers  (2024-09-17T14:05:58Z)
• On the Robustness of Decision-Focused Learning [0.0]
  Decision-Focused Learning (DFL) is an emerging learning paradigm that tackles the task of training a machine learning (ML) model to predict missing parameters of an incomplete optimization problem, where the missing parameters are predicted. DFL trains an ML model in an end-to-end system, by integrating the prediction and optimization tasks, providing better alignment of the training and testing objectives.
  arXiv  Detail & Related papers  (2023-11-28T04:34:04Z)
• ZooPFL: Exploring Black-box Foundation Models for Personalized Federated Learning [95.64041188351393]
  This paper endeavors to solve both the challenges of limited resources and personalization. We propose a method named ZOOPFL that uses Zeroth-Order Optimization for Personalized Federated Learning. To reduce the computation costs and enhance personalization, we propose input surgery to incorporate an auto-encoder with low-dimensional and client-specific embeddings.
  arXiv  Detail & Related papers  (2023-10-08T12:26:13Z)
• Statistical Rejection Sampling Improves Preference Optimization [42.57245965632205]
  We introduce a novel approach to source preference data from the target optimal policy using rejection sampling. We also propose a unified framework that enhances the loss functions used in both Sequence Likelihood (SLiC) and Direct Preference Optimization (DPO) from a preference modeling standpoint.
  arXiv  Detail & Related papers  (2023-09-13T01:07:25Z)
• DF2: Distribution-Free Decision-Focused Learning [53.2476224456902]
  Decision-focused learning (DFL) has recently emerged as a powerful approach for predictthen-optimize problems. Existing end-to-end DFL methods are hindered by three significant bottlenecks: model error, sample average approximation error, and distribution-based parameterization of the expected objective. We present DF2 -- the first textit-free decision-focused learning method explicitly designed to address these three bottlenecks.
  arXiv  Detail & Related papers  (2023-08-11T00:44:46Z)
• Personalized Federated Learning under Mixture of Distributions [98.25444470990107]
  We propose a novel approach to Personalized Federated Learning (PFL), which utilizes Gaussian mixture models (GMM) to fit the input data distributions across diverse clients. FedGMM possesses an additional advantage of adapting to new clients with minimal overhead, and it also enables uncertainty quantification. Empirical evaluations on synthetic and benchmark datasets demonstrate the superior performance of our method in both PFL classification and novel sample detection.
  arXiv  Detail & Related papers  (2023-05-01T20:04:46Z)
• Uncertainty-guided Source-free Domain Adaptation [77.3844160723014]
  Source-free domain adaptation (SFDA) aims to adapt a classifier to an unlabelled target data set by only using a pre-trained source model. We propose quantifying the uncertainty in the source model predictions and utilizing it to guide the target adaptation.
  arXiv  Detail & Related papers  (2022-08-16T08:03:30Z)
• Error-based Knockoffs Inference for Controlled Feature Selection [49.99321384855201]
  We propose an error-based knockoff inference method by integrating the knockoff features, the error-based feature importance statistics, and the stepdown procedure together. The proposed inference procedure does not require specifying a regression model and can handle feature selection with theoretical guarantees.
  arXiv  Detail & Related papers  (2022-03-09T01:55:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.