Tag: distributional
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Distributional Computational Graphs: Error Bounds
Distributional Computational Graphs: Error Bounds arXiv:2601.16250v1 Announce Type: new Abstract: We study a general framework of distributional computational graphs: computational graphs whose inputs are probability distributions rather than point values. We analyze the discretization error that arises when these graphs are evaluated using finite approximations of continuous probability distributions. Such an approximation might be the…
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Distributional Sensitivity Analysis: Enabling Differentiability in Sample-Based Inference
Distributional Sensitivity Analysis: Enabling Differentiability in Sample-Based Inference arXiv:2508.09347v1 Announce Type: new Abstract: We present two analytical formulae for estimating the sensitivity — namely, the gradient or Jacobian — at given realizations of an arbitrary-dimensional random vector with respect to its distributional parameters. The first formula interprets this sensitivity as partial derivatives of the inverse…
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A Principled Path to Fitted Distributional Evaluation
A Principled Path to Fitted Distributional Evaluation arXiv:2506.20048v1 Announce Type: new Abstract: In reinforcement learning, distributional off-policy evaluation (OPE) focuses on estimating the return distribution of a target policy using offline data collected under a different policy. This work focuses on extending the widely used fitted-Q evaluation — developed for expectation-based reinforcement learning — to…
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Distributional encoding for Gaussian process regression with qualitative inputs
Distributional encoding for Gaussian process regression with qualitative inputs arXiv:2506.04813v1 Announce Type: new Abstract: Gaussian Process (GP) regression is a popular and sample-efficient approach for many engineering applications, where observations are expensive to acquire, and is also a central ingredient of Bayesian optimization (BO), a highly prevailing method for the optimization of black-box functions. However,…
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Risk Bounds For Distributional Regression
Risk Bounds For Distributional Regression arXiv:2505.09075v1 Announce Type: new Abstract: This work examines risk bounds for nonparametric distributional regression estimators. For convex-constrained distributional regression, general upper bounds are established for the continuous ranked probability score (CRPS) and the worst-case mean squared error (MSE) across the domain. These theoretical results are applied to isotonic and trend…
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Online Multivariate Regularized Distributional Regression for High-dimensional Probabilistic Electricity Price Forecasting
Online Multivariate Regularized Distributional Regression for High-dimensional Probabilistic Electricity Price Forecasting arXiv:2504.02518v1 Announce Type: new Abstract: Probabilistic electricity price forecasting (PEPF) is a key task for market participants in short-term electricity markets. The increasing availability of high-frequency data and the need for real-time decision-making in energy markets require online estimation methods for efficient model updating.…
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On Generalization and Distributional Update for Mimicking Observations with Adequate Exploration
On Generalization and Distributional Update for Mimicking Observations with Adequate Exploration arXiv:2501.12785v1 Announce Type: new Abstract: This paper tackles the efficiency and stability issues in learning from observations (LfO). We commence by investigating how reward functions and policies generalize in LfO. Subsequently, the built-in reinforcement learning (RL) approach in generative adversarial imitation from observation (GAIfO)…
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A Distributional Evaluation of Generative Image Models
A Distributional Evaluation of Generative Image Models arXiv:2501.00744v1 Announce Type: new Abstract: Generative models are ubiquitous in modern artificial intelligence (AI) applications. Recent advances have led to a variety of generative modeling approaches that are capable of synthesizing highly realistic samples. Despite these developments, evaluating the distributional match between the synthetic samples and the target…