DF2: Distribution-Free Decision-Focused Learning

• URL: http://arxiv.org/abs/2308.05889v1
• Date: Fri, 11 Aug 2023 00:44:46 GMT
• Title: DF2: Distribution-Free Decision-Focused Learning
• Authors: Lingkai Kong, Wenhao Mu, Jiaming Cui, Yuchen Zhuang, B. Aditya Prakash, Bo Dai, Chao Zhang
• Abstract summary: 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.
• Score: 53.2476224456902
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Decision-focused learning (DFL) has recently emerged as a powerful approach for predict-then-optimize problems by customizing a predictive model to a downstream optimization task. However, existing end-to-end DFL methods are hindered by three significant bottlenecks: model mismatch error, sample average approximation error, and gradient approximation error. Model mismatch error stems from the misalignment between the model's parameterized predictive distribution and the true probability distribution. Sample average approximation error arises when using finite samples to approximate the expected optimization objective. Gradient approximation error occurs as DFL relies on the KKT condition for exact gradient computation, while most methods approximate the gradient for backpropagation in non-convex objectives. In this paper, we present DF2 -- the first \textit{distribution-free} decision-focused learning method explicitly designed to address these three bottlenecks. Rather than depending on a task-specific forecaster that requires precise model assumptions, our method directly learns the expected optimization function during training. To efficiently learn the function in a data-driven manner, we devise an attention-based model architecture inspired by the distribution-based parameterization of the expected objective. Our method is, to the best of our knowledge, the first to address all three bottlenecks within a single model. We evaluate DF2 on a synthetic problem, a wind power bidding problem, and a non-convex vaccine distribution problem, demonstrating the effectiveness of DF2.

Related papers

• Online Decision-Focused Learning [63.83903681295497]
  Decision-focused learning (DFL) is an increasingly popular paradigm for training predictive models whose outputs are used in decision-making tasks.<n>We investigate DFL in dynamic environments where the objective function does not evolve over time.<n>We establish bounds on the expected dynamic regret, both when decision space is a simplex and when it is a general bounded convex polytope.
  arXiv  Detail & Related papers  (2025-05-19T10:40:30Z)
• Minimax Optimality of the Probability Flow ODE for Diffusion Models [8.15094483029656]
  This work develops the first end-to-end theoretical framework for deterministic ODE-based samplers.<n>We propose a smooth regularized score estimator that simultaneously controls both the $L2$ score error and the associated mean Jacobian error.<n>We demonstrate that the resulting sampler achieves the minimax rate in total variation distance, modulo logarithmic factors.
  arXiv  Detail & Related papers  (2025-03-12T17:51:29Z)
• Debiasing Mini-Batch Quadratics for Applications in Deep Learning [22.90473935350847]
  Quadratic approximations form a fundamental building block of machine learning methods. When computations on the entire training set are intractable - typical for deep learning - the relevant quantities are computed on mini-batches. We show that this bias introduces a systematic error, (ii) provide a theoretical explanation for it, (iii) explain its relevance for second-order optimization and uncertainty via the Laplace approximation in deep learning, and (iv) develop and evaluate debiasing strategies.
  arXiv  Detail & Related papers  (2024-10-18T09:37:05Z)
• Bayesian Estimation and Tuning-Free Rank Detection for Probability Mass Function Tensors [17.640500920466984]
  This paper presents a novel framework for estimating the joint PMF and automatically inferring its rank from observed data. We derive a deterministic solution based on variational inference (VI) to approximate the posterior distributions of various model parameters. Additionally, we develop a scalable version of the VI-based approach by leveraging variational inference (SVI) Experiments involving both synthetic data and real movie recommendation data illustrate the advantages of our VI and SVI-based methods in terms of estimation accuracy, automatic rank detection, and computational efficiency.
  arXiv  Detail & Related papers  (2024-10-08T20:07:49Z)
• OPUS: Occupancy Prediction Using a Sparse Set [64.60854562502523]
  We present a framework to simultaneously predict occupied locations and classes using a set of learnable queries. OPUS incorporates a suite of non-trivial strategies to enhance model performance. Our lightest model achieves superior RayIoU on the Occ3D-nuScenes dataset at near 2x FPS, while our heaviest model surpasses previous best results by 6.1 RayIoU.
  arXiv  Detail & Related papers  (2024-09-14T07:44:22Z)
• Rejection via Learning Density Ratios [50.91522897152437]
  Classification with rejection emerges as a learning paradigm which allows models to abstain from making predictions. We propose a different distributional perspective, where we seek to find an idealized data distribution which maximizes a pretrained model's performance. Our framework is tested empirically over clean and noisy datasets.
  arXiv  Detail & Related papers  (2024-05-29T01:32:17Z)
• Exploiting Diffusion Prior for Generalizable Dense Prediction [85.4563592053464]
  Recent advanced Text-to-Image (T2I) diffusion models are sometimes too imaginative for existing off-the-shelf dense predictors to estimate. We introduce DMP, a pipeline utilizing pre-trained T2I models as a prior for dense prediction tasks. Despite limited-domain training data, the approach yields faithful estimations for arbitrary images, surpassing existing state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2023-11-30T18:59:44Z)
• Diffusion models for probabilistic programming [56.47577824219207]
  Diffusion Model Variational Inference (DMVI) is a novel method for automated approximate inference in probabilistic programming languages (PPLs) DMVI is easy to implement, allows hassle-free inference in PPLs without the drawbacks of, e.g., variational inference using normalizing flows, and does not make any constraints on the underlying neural network model.
  arXiv  Detail & Related papers  (2023-11-01T12:17:05Z)
• Learning Unnormalized Statistical Models via Compositional Optimization [73.30514599338407]
  Noise-contrastive estimation(NCE) has been proposed by formulating the objective as the logistic loss of the real data and the artificial noise. In this paper, we study it a direct approach for optimizing the negative log-likelihood of unnormalized models.
  arXiv  Detail & Related papers  (2023-06-13T01:18:16Z)
• Adversarial Adaptive Sampling: Unify PINN and Optimal Transport for the Approximation of PDEs [2.526490864645154]
  We propose a new minmax formulation to optimize simultaneously the approximate solution, given by a neural network model, and the random samples in the training set. The key idea is to use a deep generative model to adjust random samples in the training set such that the residual induced by the approximate PDE solution can maintain a smooth profile.
  arXiv  Detail & Related papers  (2023-05-30T02:59:18Z)
• Error Bounds for Flow Matching Methods [38.9898500163582]
  Flow matching methods approximate a flow between two arbitrary probability distributions. We present error bounds for the flow matching procedure using fully deterministic sampling, assuming an $L2$ bound on the approximation error and a certain regularity on the data distributions.
  arXiv  Detail & Related papers  (2023-05-26T12:13:53Z)
• Performative Prediction with Bandit Feedback: Learning through Reparameterization [23.039885534575966]
  performative prediction is a framework for studying social prediction in which the data distribution itself changes in response to the deployment of a model. We develop a reparametization that reparametrizes the performative prediction objective as a function of induced data distribution.
  arXiv  Detail & Related papers  (2023-05-01T21:31:29Z)
• Leveraging Unlabeled Data to Predict Out-of-Distribution Performance [63.740181251997306]
  Real-world machine learning deployments are characterized by mismatches between the source (training) and target (test) distributions. In this work, we investigate methods for predicting the target domain accuracy using only labeled source data and unlabeled target data. We propose Average Thresholded Confidence (ATC), a practical method that learns a threshold on the model's confidence, predicting accuracy as the fraction of unlabeled examples.
  arXiv  Detail & Related papers  (2022-01-11T23:01:12Z)
• Cross-Entropy Method Variants for Optimization [0.0]
  Cross-entropy (CE) method is a popular method for optimization due to its simplicity and effectiveness. Certain objective functions may be computationally expensive to evaluate, and the CE-method could potentially get stuck in local minima. We introduce novel variants of the CE-method to address these concerns.
  arXiv  Detail & Related papers  (2020-09-18T19:51:30Z)
• SODEN: A Scalable Continuous-Time Survival Model through Ordinary Differential Equation Networks [14.564168076456822]
  We propose a flexible model for survival analysis using neural networks along with scalable optimization algorithms. We demonstrate the effectiveness of the proposed method in comparison to existing state-of-the-art deep learning survival analysis models.
  arXiv  Detail & Related papers  (2020-08-19T19:11:25Z)
• Uncertainty Estimation Using a Single Deep Deterministic Neural Network [66.26231423824089]
  We propose a method for training a deterministic deep model that can find and reject out of distribution data points at test time with a single forward pass. We scale training in these with a novel loss function and centroid updating scheme and match the accuracy of softmax models.
  arXiv  Detail & Related papers  (2020-03-04T12:27:36Z)

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.