Study optimal investment under imitation of decision-changing rates.
problem Optimal investment under imitation of decision-changing rates.
method Proposed integral disparity to quantify imitation, derived general solution using variational method, analyzed asymptotic properties, validated with real data.
result Investor's optimal decisions under imitation of decision-changing rates.
CREDO assesses decision optimality under uncertainty without assuming a model.
problem Uncertainty in decision-making without reliable quantification of optimality.
method CREDO uses the inverse feasible region and conformal prediction balls to estimate decision optimality probability.
result CREDO provides accurate, efficient, and reliable evaluations of decision optimality.
Decision Machines embeds decision trees into vector spaces for improved optimization.
problem Overfitting and difficulty in finding optimal decision tree structure.
method Embedding Boolean tests into a binary vector space and representing tree structure as matrices.
result Optimized decision trees with enhanced predictive power.
GoBOED optimizes experiments for specific decision-making objectives, improving downstream outcomes.
problem Reducing parameter uncertainty does not always improve decision-making in critical settings.
method Combines variational posterior surrogate and differentiable convex decision layer for gradient-based design optimization.
result GoBOED identifies designs that better align with specific decision objectives and reveals wider optimal design windows.
New techniques optimize decision trees for interpretable machine learning.
problem Optimizing decision trees for interpretable machine learning.
method General framework for decision tree optimization addressing imbalanced data and continuous variables.
result Proves optimal decision trees for various objectives.
Study optimal investment with herd behavior using rational decision decomposition.
problem Optimal investment problem considering herd behavior between two agents.
method Introduce average deviation term, use variational method, rational decision decomposition, investment opinion.
result Quantitative analysis of herd behavior impact on investment decisions.
Proposes a new method for decision-aware learning in optimization.
problem Contextual linear optimization with cost prediction errors.
method Reweighing prediction error by decision regret for decision-aware predictor.
result Improves over predict-then-optimize framework for misspecified models.
The paper provides a method to minimize regret in estimate-then-optimize decision-making.
problem Errors in estimation lead to sub-optimal decisions in data-driven decision-making.
method A novel bound on regret for smooth and unconstrained optimization problems, followed by experimental design to minimize this regret.
result A general procedure for experimental design to minimize regret resulting from estimate-then-optimize.
Study minimax-optimal rates for offline decision-making with function approximation.
problem Statistical complexity of offline decision-making with function approximation.
method Near minimax-optimal rates for stochastic contextual bandits and Markov decision processes, using pseudo-dimension and behavior policy.
result Established performance limits and new characterization of behavior policy.
We consider the use of decision trees for decision-making problems under the predict-then-optimize framework. That is, we would like to first use a decision tree to predict unknown input parameters of an optimization problem, and then make decisions by solving the optimization problem using the predicted parameters. A …
Paper tackles risk-sensitive decision-making under uncertainty.
problem Risk-sensitive decision-making problem under uncertainty.
method Formulated as a stochastic control problem, delineated necessary optimality conditions.
result Illustrative examples from optimal betting and inventory management support the theory.
Framework learns linear programs from optimal decisions.
problem Learning linear programs from optimal decisions is challenging.
method Gradient-based framework for learning linear programs from optimal decisions.
result Successfully learns linear programs and multi-commodity flow instances.
DVA framework attributes value of predictive models to features, configurations, and interactions.
problem Lack of explanation for how predictive models influence operational decisions.
method Shapley-based cooperative game theory applied to predict-then-optimize systems.
result DVA can guide targeted interventions to align model beliefs with operational performance.
Develops optimal uncertainty quantification for risk-averse decision makers.
problem Quantifying prediction uncertainty for risk-sensitive domains.
method Decision-theoretic foundations connecting uncertainty quantification with risk-averse decision-making.
result Risk-Averse Calibration (RAC) algorithm provides optimal prediction sets for risk-averse decision makers.
Paper integrates LLMs into portfolio optimization to improve decision quality.
problem Suboptimal portfolio decisions due to mismatch between prediction and decision quality.
method Integrates LLMs with decision-focused learning, using attention mechanism to process asset relationships and macro variables.
result Model consistently outperforms state-of-the-art deep learning models in portfolio optimization.
Optimizes decision-making with uncertain variables using auxiliary observations.
problem Contextual stochastic optimization problems with uncertain variables and rich auxiliary observations.
method Trains forest decision policies by growing trees that optimize downstream decision quality, using optimization perturbation analysis for efficient approximations.
result Proves asymptotic optimality and empirical validation of the method's performance and efficiency.
dboost optimizes prediction models for convex cone problems.
problem Optimizing prediction models for decision-making.
method Gradient boosting with implicit differentiation for convex quadratic cone programming.
result dboost reduces out-of-sample decision regret.
Decision tree algorithms have been among the most popular algorithms for interpretable (transparent) machine learning since the early 1980's. The problem that has plagued decision tree algorithms since their inception is their lack of optimality, or lack of guarantees of closeness to optimality: decision tree algorithm…
Optimizes portfolio construction using Bayesian methods and variational techniques.
problem Balancing reward and risk in portfolio construction.
method Bayesian decision-theoretic formulation, saddle-point problem, variational Bayes relaxation, efficient algorithm, provable convergence.
result Proves statistical consistency of proposed decision with optimal Bayesian decision.
Paper introduces novel model selection for CRO to balance robustness and decision risk.
problem Balancing robustness and decision risk in CRO.
method Conformalized Robust Optimization with Model Selection (CROMS) framework.
result Significant improvements in decision efficiency across various applications.
Post-processing predictors reduces calibration errors for decision-making.
problem Predictors with low calibration error for machine learning may have high error for decision-making.
method Post-processing with ε distance to calibration adds noise to make predictions differentially private.
result Post-processing achieves O(√ε) ECE and CDL, asymptotically optimal.
Develops optimal decision-making framework for uncertain counterfactuals.
problem Ensuring reliability of predictions in high-stakes decisions.
method Policy-Coupled Risk-Averse Conformal Prediction (PC-RACP).
result Optimal prediction sets for counterfactual decisions with valid coverage.
To integrate strategic, tactical and operational decisions, the two-stage optimization has been widely used to guide dynamic decision making. In this paper, we study the two-stage stochastic programming for complex systems with unknown response estimated by simulation. We introduce the global-local metamodel assisted t…
Creating impact in real-world settings requires artificial intelligence techniques to span the full pipeline from data, to predictive models, to decisions. These components are typically approached separately: a machine learning model is first trained via a measure of predictive accuracy, and then its predictions are u…
New approach optimizes decisions based on uncertainty in predictions.
problem Mismatch between prediction accuracy and decision loss in sequential design.
method Directional uncertainty-guided approach to sequential experimental design.
result Directional uncertainty-based design stops earlier and performs better.
RISE learns decisions with sensitive variables, improving worst-case outcomes.
problem Uncertainty and bias in decisions due to delayed sensitive variable data.
method Incorporates sensitive variables offline but not at deployment, using quantile or infimum optimization.
result Improves worst-case outcomes for individuals affected by unavailable sensitive variables.
qEUBO optimizes decision-making with noisy feedback.
problem Optimizing decision-making with noisy preference feedback.
method Introduces qEUBO as a novel acquisition function for preferential Bayesian optimization.
result qEUBO is one-step Bayes optimal and enjoys an approximation guarantee under noise.
Meta-learning interpretable decision trees with synthetic data.
problem Lack of efficient, scalable methods for generating synthetic data for decision tree meta-learning.
method Synthetic generation of near-optimal decision trees using the MetaTree transformer architecture.
result Meta-learning of decision trees achieves performance comparable to real-world data or optimal decision trees, with significant computational cost reduction.
Study optimality in safety-constrained Markov decision processes using asynchronous value iteration and modified Q-learning.
problem Optimality in safety-constrained Markov decision processes with multichain structure.
method Formulated as a zero-sum game, constructed asynchronous value iteration scheme and modified Q-learning algorithm.
result Resolved Bellman's principle of optimality for multichain Markov decision processes and provided learning algorithms.
Bayesian models quantify uncertainty and facilitate optimal decision-making in downstream applications. For most models, however, practitioners are forced to use approximate inference techniques that lead to sub-optimal decisions due to incorrect posterior predictive distributions. We present a novel approach that corr…
A novel gradient-based method optimizes decision trees for complex tasks.
problem Training decision trees with arbitrary differentiable loss functions.
method Gradient-based optimization using first and second derivatives of loss functions.
result Improves accuracy and flexibility in decision tree optimization.
Paper presents an algorithm for optimal regret in communicating Markov decision processes.
problem Achieving optimal regret in Markov decision processes with a communicating assumption.
method The algorithm explicitly tracks the constant K(M) to learn optimally, balancing exploration, co-exploration, and exploitation.
result The algorithm achieves asymptotically optimal regret K(M)log(T)+o(log(T)) for communicating Markov decision processes. We are witnessing an increasing use of data-driven predictive models to inform decisions. As decisions have implications for individuals and society, there is increasing pressure on decision makers to be transparent about their decision policies. At the same time, individuals may use knowledge, gained by transparency, …
Optimal decision-making using prediction sets to minimize risk.
problem Using prediction sets optimally for decision-making in uncertain scenarios.
method Decision-theoretic framework that seeks to minimize expected loss against a worst-case distribution.
result ROCP algorithm reduces critical mistakes compared to baselines, especially in costly out-of-set errors.
Paper uses DFL to optimize portfolio risk and outperforms conventional methods.
problem Optimizing portfolio risk and return under uncertainty.
method Decision-focused learning (DFL) to derive global minimum variance portfolio (GMVP).
result DFL-based methods consistently deliver superior decision performance in portfolio optimization.
The paper corrects bias in fluid approximation for better decision-making in stochastic optimization.
problem Bias introduced by using mean values in fluid approximation leads to suboptimal decisions.
method Identifying a decision-corrected point estimate that yields optimal decisions.
result A corrected point estimate exists under certain conditions and can be computed algorithmically.
Given a set of human's decisions that are observed, inverse optimization has been developed and utilized to infer the underlying decision making problem. The majority of existing studies assumes that the decision making problem is with a single objective function, and attributes data divergence to noises, errors or bou…
The topic of learning to solve optimization problems has received interest from both the operations research and machine learning communities. In this work, we combine techniques from both fields to address the problem of learning to generate decisions to instances of continuous optimization problems where the feasible…
SRO optimizes decisions against worst-case sampler induced by generative models.
problem Operational uncertainty shifts from explicit probability law to sampler induced by learned generators.
method SRO optimizes decisions against the worst-case sampler induced by perturbing the learned generator.
result Empirical worst-case objective provides high-probability upper certificate for true population objective.
A model for human-machine decision-making with private info and opacity.
problem Optimizing decisions in a human-machine system with private info and opacity.
method Formulated as a two-player learning problem, proved lower and upper bounds on optimality.
result Simple coordination strategy is nearly minimax optimal, efficient learning possible under certain assumptions.
Bayesian framework for learning optimal action-value function in MDPs.
problem Uncertainty quantification in MDPs for optimal decision-making strategies.
method Full Bayesian framework including modelling, inference, and decision-making.
result Demonstrates exploration benefits of posterior sampling in MDPs.
A new method for decision-focused learning using diffusion models.
problem Inability of deterministic point predictions to capture stochasticity in real-world environments.
method Proposes a diffusion-based DFL approach that trains a diffusion model to represent uncertain parameters and optimizes decisions through stochastic optimization.
result Empirically shows consistent outperformance over strong baselines in decision quality.
New complexity measure for interactive learning reduces regret to near-optimal levels.
problem Challenges in sample-efficient, adaptive learning algorithms for interactive decision making.
method Introduces the Decision-Estimation Coefficient and the Estimation-to-Decisions (E2D) principle.
result Unified algorithm design principle E2D achieves optimal sample-efficient learning.
Recent exploration of optimal individualized decision rules (IDRs) for patients in precision medicine has attracted a lot of attention due to the heterogeneous responses of patients to different treatments. In the existing literature of precision medicine, an optimal IDR is defined as a decision function mapping from t…
Dynamic probabilistic forecasts guide optimal decisions in uncertain processes.
problem Optimal decision making in processes influenced by uncertain random factors.
method Stochastic models for probabilistic forecast evolution, calibrated from ensemble forecasts.
result Optimal decision strategies determined using dynamic probabilistic forecasts.
Paper extends transfer learning for decision rules, improving treatment rule estimation.
problem Estimating optimal individualized treatment rules under changing conditions.
method Bayes decision rules and low-dimensional empirical risk minimization.
result Consistent estimators and risk bounds established under mild conditions.
This study explains and mitigates inflated returns and turnover in SPO-based portfolio optimization.
problem Inflated returns and excessive turnover in SPO-based portfolio optimization.
method KKT-based interpretation of portfolio decisions as ranking over adjusted scores, empirical evaluation of stabilization mechanisms.
result Realistic output constraints and portfolio-level turnover control improve SPO-based strategies.
New PG losses improve decision optimization in misspecified models.
problem Improving decision optimization in models that are not perfectly specified.
method Introducing Perturbation Gradient (PG) losses to connect decision loss with directional derivatives and optimizing using gradient techniques.
result PG losses yield best-in-class policies asymptotically, even in misspecified settings.