Probabilistic prediction aims to compute predictive distributions rather than
single-point predictions. These distributions enable practitioners to quantify
uncertainty, compute risk, and detect outliers. However, most probabilistic
methods assume parametric responses, such as Gaussian or Poisson distributions.
When these assumptions fail, such models lead to bad predictions and poorly
calibrated uncertainty. In this paper, we propose Treeffuser, an easy-to-use
method for probabilistic prediction on tabular data. The idea is to learn a
conditional diffusion model where the score function is estimated using
gradient-boosted trees. The conditional diffusion model makes Treeffuser
flexible and non-parametric, while the gradient-boosted trees make it robust
and easy to train on CPUs. Treeffuser learns well-calibrated predictive
distributions and can handle a wide range of regression tasks -- including
those with multivariate, multimodal, and skewed responses. % , as well as
categorical predictors and missing data We study Treeffuser on synthetic and
real data and show that it outperforms existing methods, providing
better-calibrated probabilistic predictions. We further demonstrate its
versatility with an application to inventory allocation under uncertainty using
sales data from Walmart. We implement Treeffuser in
\href{https://github.com/blei-lab/treeffuser}{this https URL}.

Treeffuser 是一种易于使用的基于梯度提升树的条件扩散模型，用于概率预测，具备良好的预测分布校准能力，适用于多元、多峰、偏态响应的各种回归任务。

Treeffuser：基于梯度提升树的条件扩散概率预测

Treeffuser: Probabilistic Predictions via Conditional Diffusions with  Gradient-Boosted Trees

Driven by steady progress in generative modeling, simulation-based inference
(SBI) has enabled inference over stochastic simulators. However, recent work
has demonstrated that model misspecification can harm SBI's reliability. This
work introduces robust posterior estimation (ROPE), a framework that overcomes
model misspecification with a small real-world calibration set of ground truth
parameter measurements. We formalize the misspecification gap as the solution
of an optimal transport problem between learned representations of real-world
and simulated observations. Assuming the prior distribution over the parameters
of interest is known and well-specified, our method offers a controllable
balance between calibrated uncertainty and informative inference under all
possible misspecifications of the simulator. Our empirical results on four
synthetic tasks and two real-world problems demonstrate that ROPE outperforms
baselines and consistently returns informative and calibrated credible
intervals.

通过引入鲁棒后验估计 (ROPE)，克服了模型错误配置，ROPE 在真实世界校准集的基础上提供了具有可控平衡的校准不确定性和信息丰富的推断，通过解决真实观测和模拟观测之间的最优运输问题确保了模拟基于推理的可靠性。

通过数据驱动校准解决模拟推断中的错误规范

Addressing Misspecification in Simulation-based Inference through  Data-driven Calibration

Bayesian deep learning approaches that allow uncertainty estimation for
regression problems often converge slowly and yield poorly calibrated
uncertainty estimates that can not be effectively used for quantification.
Recently proposed post hoc calibration techniques are seldom applicable to
regression problems and often add overhead to an already slow model training
phase. This work presents a fast calibrated uncertainty estimation method for
regression tasks, called posterior annealing, that consistently improves the
convergence of deep regression models and yields calibrated uncertainty without
any post hoc calibration phase. Unlike previous methods for calibrated
uncertainty in regression that focus only on low-dimensional regression
problems, our method works well on a wide spectrum of regression problems. Our
empirical analysis shows that our approach is generalizable to various network
architectures including, multilayer perceptrons, 1D/2D convolutional networks,
and graph neural networks, on five vastly diverse tasks, i.e., chaotic particle
trajectory denoising, physical property prediction of molecules using 3D
atomistic representation, natural image super-resolution, and medical image
translation using MRI images.

本研究提出了一种名为后验退火的快速校准不确定性估计方法，适用于多种深度回归模型和任务，并在各种网络结构和不同领域的任务上进行了实证分析。