Decision-Focused Learning

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Decision-focused learning studies how to train predictors for downstream decisions—such as resource allocation, portfolio selection, and energy scheduling—rather than for forecast accuracy alone. Common approaches differentiate through optimization layers or use KKT and implicit-gradient methods; surrogate losses, cost-sensitive regression, and solver-free updates aim to make training more scalable. Researchers also extend these ideas to stochastic forecasts, sequential decisions, fairness, and settings with delayed or partial feedback. A central challenge is that better decision outcomes are not guaranteed: results depend on the optimization problem, the chosen proxy loss, computational limits, and how well methods hold up under shifts in data or constraints.

Latest papers

50 papers5 papers in the last four weeks, up 25% on the four weeks before. 0.0% of all new papers.

FedRSPO+: A Heterogeneity-aware Algorithm for Decision-focused Federated Learning
Oct 6, 2026Konstantinos Ziliaskopoulos, Alexander Vinel, Jiaqi WangDecision-Focused Learning Federated Learning
Auburn University
Decision-Focused Learning in MDPs: An Occupancy Measure Approach
Oct 6, 2026Zihao Zhao, Ashwath K. Karunakaram, Ali Eshragh +2Markov Decision Processes Decision-Focused Learning
Georgia Institute of Technology, Atlanta, GA · Johns Hopkins Carey Business School, Washington, DC · International Computer Science Institute, Berkeley, CA
The Curvature of Regret in Contextual Linear Optimization
Oct 1, 2026Konstantinos Ziliaskopoulos, Alexander Vinel, Alice E. SmithDecision-Focused Learning Regret Minimization
Auburn University Auburn, AL, USA · University of Alabama Tuscaloosa, AL, USA
Jacobian Rank Collapse in Decision-Focused Learning
Sep 30, 2026Aojie Yuan, Haiyue Zhang, Zijian SuDecision-Focused Learning
University of Southern California · University of Michigan
RL-PaO: Prediction as Action in Decision Making under Uncertainty
Sep 29, 2026Jiahui Feng, Dafang Zhao, Zheng Chen +2Predict-Then-Optimize Decision-Making under Uncertainty
Great Bay University, School of Computing and Information Technology, China · The University of Osaka, School of Information Science and Technology, Japan · The University of Osaka, SANKEN, Japan +1
Geometric Identification in Predict-Then-Optimize Learning
Sep 27, 2026Jiaxiao Xu, Changhong Mou, Keji Liu +2Predict-Then-Optimize Decision-Focused Learning
Shanghai University of Finance and Economics · Utah State University
PolyStepOR: Learning to Decide Without Optimal Decisions
Sep 26, 2026Viet The Nguyen, Gunther Gust, An Thai LeDecision-Focused Learning Predict-Then-Optimize
University of Würzburg, Germany · Center for Artificial Intelligence and Data Science (CAIDAS), Germany · Center for AI Research, VinUniversity, Vietnam +1
Decision-Focused Learning for Mean-Variance Portfolio Optimization via KKT-Based Reformulation
Sep 18, 2026Kensei Nosaka, Shunnosuke Ikeda, Yuichi TakanoPortfolio Optimization Decision-Focused Learning
University of Tsukuba, Tsukuba-shi, Ibaraki 305-8573, Japan
Optimizing for the decision not the prediction: an exploration of Smooth Net Benefit as a training objective
Sep 11, 2026Koen M. F. Gorgels, Lasai Barreñada, Maarten van Smeden +3Predict-Then-Optimize Decision-Focused Learning
Department of Data Science and Biostatistics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands · Coo peratie VGZ, Arnhem, the Netherlands · Department of Development and Regeneration, KU Leuven, Leuven, Belgium
Accurate prediction is not profitable advice: profit-based evaluation of machine learning nitrogen recommendations in winter wheat
Aug 27, 2026Xulong Wang, Po YangDecision-Focused Learning Precision Agriculture
University of Sheffield
Counterfactual Quotient Models: Learning What Actions Change, Not What the World Does
Aug 22, 2026Junlin Chen, Ruijie Wang, Jianxin LiAction-Conditioned World Models Model-Based RL
School of Computer Science and Engineering, Beihang University Beijing, China
Decision-Driven Regularization: A Blended Model for Learning and Optimization
Aug 15, 2026Gar Goei Loke, Qinshen Tang, Yangge Xiao +1Predict-Then-Optimize Learning to Optimize
Department of Management and Marketing, Durham University Business School, Waterside, Durham DH1 1SL, United Kingdom · Nanyang Business School, Nanyang Technological University, Singapore · Faculty of Business and Economics, The University of Melbourne +1
Decision-Focused Learning in Network Interdiction Games
Aug 10, 2026Luca M. Hartmann, Parinaz NaghizadehAdversarial Training Stackelberg Games
ECE Department, UC San Diego
When Do Surrogate Updates Improve Decisions? A Local Theory of Trajectory-Wise Transfer
Aug 2, 2026Yuyang ShenSurrogate Modeling Decision-Focused Learning
The Chinese University of Hong Kong, Shenzhen
End-to-End Fairness Optimization with Fair Decision-Focused Learning
Jul 31, 2026Yu Wang, Violet Xinying ChenFair Resource Allocation Algorithmic Fairness
School of Business Stevens Institute of Technology
Scores Are Not Decisions: Cost-Aware Stopping for Tool Acquisition in LLM Agents
Jul 29, 2026Yicheng Feng, Yan Zhang, Yan Cheng +1Decision-Focused Learning Tool-Using Agents
School of Advanced Manufacturing and Robotics, Peking University · Desautels Faculty of Management, McGill University · Business School, Shanghai University of Finance and Economics +1
Smooth Learning with Hard Constraints via Legendre-Regularized Policies
Jul 27, 2026Zikun Lin, Rui Chen, Yijie WangDecision-Focused Learning Constrained Optimization
Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Hong Kong, China. · School of Data Science, The Chinese University of Hong Kong, Shenzhen, Guangdong, China. · School of Economics and Management, Tongji University, Shanghai, China.
Smart predict-then-robustly-optimize
Jul 23, 2026Aakil Caunhye, Xuefei Lu, Belen Martin-BarraganDistribution Shift Robustness Predict-Then-Optimize
Weak-to-Strong Learning in Decision Making
Jul 20, 2026Jingwei Ji, Renyuan XuStochastic Optimization Decision-Focused Learning
Management Science and Engineering, Stanford University
Learning Predictive Ambiguity Sets for Decision-Focused Distributionally Robust Optimization
Jul 10, 2026Junjie GuoDistributionally Robust Optimization Decision-Focused Learning
Rutgers University
Decision-Focused Scenario Generation and Selection for Efficient and Robust Grid Dispatch
Jul 7, 2026Yangze Zhou, Yihong Zhou, Thomas Morstyn +1Distributionally Robust Optimization Decision-Focused Learning
Decision-Aware Training for Sample-Based Generative Models
Jul 1, 2026Kornelius Raeth, Nicole LudwigCost-Sensitive Learning Probabilistic Forecasting
T ubingen AI Center, University of T ubingen, Germany · University of Augsburg, Germany
Decision-focused Sparse Tangent Portfolio Optimization
Jul 1, 2026Haeun Jeon, Seunghoon Choi, Hyunglip Bae +2Decision-Focused Learning Portfolio Optimization
Department of Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea · Department of Big Data Convergence, and Bigdata Research Lab, Chonnam National University, Gwangju, Republic of Korea · Department of Industrial Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea
$\text{DT}^2$ : Decision-Targeted Digital Twins
Jun 24, 2026Harry Amad, Mihaela van der SchaarDecision-Focused Learning Digital Twins
Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom.
Decision-Focused Learning: When and Why Traditional Prediction Models Fail
Jun 19, 2026Mo LiuPredict-Then-Optimize Decision-Focused Learning
Department of Statistics and Operations Research, University of North Carolina at Chapel Hill
A Solver-Free Training Method for Predict-then-Optimize
Jun 17, 2026Beichen Wan, Mo LiuPredict-Then-Optimize Learning to Optimize
Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, NC, USA.
Forecasting what Matters: Decision-Focused RL for Controlled EV Charging with Unknown Departure Times
Jun 17, 2026Giuseppe Gabriele, Fabio Pavirani, Seyed Soroush Karimi Madahi +1RL Control Electric Vehicle Charging
Ghent University – imec Ghent, Belgium
Decision-Weighted Flow Matching for Contextual Stochastic Optimization
Jun 15, 2026Jize Xie, Haomiao Wu, Qiang Chen +2Stochastic Optimization Conditional Flow Matching
Department of Industrial Engineering and Decision Analytics Hong Kong University of Science and Technology
Scaling Decision-Focused Learning to Large Problems with Lagrangian Decomposition
Jun 7, 2026Stéphane Eilles-Chan Way, Hugo Percot, Quentin Cappart +2Predict-Then-Optimize Learning to Optimize
Polytechnique Montréal, Montreal, Canada · Ecole Polytechnique, Palaiseau, France · UCLouvain, Louvain-la-Neuve, Belgium +2
Aligning Data-Driven Predictors with Allocation: A Decision-Focused Approach to Survival Analysis
Jun 1, 2026Itai Zilberstein, Ioannis Anagnostides, Tuomas SandholmLearning to Rank Survival Analysis
Department of Computer Science, Carnegie Mellon University, Pittsburgh, PA · Additional affiliations: Strategy Robot, Inc., Strategic Machine, Inc., Optimized Markets, Inc.

Oct 6, 2026J/K move · Enter open · S save

FedRSPO+: A Heterogeneity-aware Algorithm for Decision-focused Federated Learning

Konstantinos Ziliaskopoulos, Alexander Vinel, Jiaqi Wang

Auburn University

Decision-Focused Learning Federated Learning Predict-Then-Optimize Heterogeneous Federated Learning

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Decision-focused learning (DFL) trains predictive models for downstream optimization, but existing methods largely assume centralized data. In cross-silo settings, federated learning offers a natural alternative, yet standard federated methods optimize prediction over decision quality and do not address heterogeneity in downstream objectives or feasible sets. This heterogeneity is especially challenging for DFL because small perturbations in polyhedral problems can cause discontinuous changes in optimal decisions, destabilizing client updates and aggregation. We propose FedRSPO+, a heterogeneity-aware framework for decision-focused federated learning, built on RSPO+, a regularized predict-then-optimize surrogate that smooths the decision map through projection. We show that RSPO+ upper bounds decision error and regret for the regularized decision and, under exact regularization and consistent LP solution selection, for the original LP decision. We further derive cross-client heterogeneity bounds that depend on both objective and feasible-set heterogeneity, vanish at homogeneity, and require no strong convexity. FedRSPO+ uses an annealed, modular training procedure compatible with standard federated personalization and aggregation methods. Experiments on synthetic knapsack, shortest-path, and real-world energy pricing tasks compare against prediction-only federated learning and DFL baselines under varying heterogeneity and communication budgets. Results suggest that smoothing is a useful ingredient for stable collaborative decision learning and provide a heterogeneity-aware foundation for federated DFL.