Hierarchical Spatio-Temporal Uncertainty Quantification for Distributed Energy Adoption

The rapid deployment of distributed energy resources (DER) has introduced significant spatio-temporal uncertainties in power grid management, necessitating accurate multilevel forecasting methods. However, existing approaches often produce overly conservative uncertainty intervals at individual spatial units and fail to properly capture uncertainties when aggregating predictions across different spatial scales. This paper presents a novel hierarchical spatio-temporal model based on the conformal prediction framework to address these challenges. Our approach generates circuit-level DER growth predictions and efficiently aggregates them to the substation level while maintaining statistical validity through a tailored non-conformity score. Applied to a decade of DER installation data from a local utility network, our method demonstrates superior performance over existing approaches, particularly in reducing prediction interval widths while maintaining coverage.

Recurrent Neural Goodness-of-Fit Test for Time Series

Time series data are crucial across diverse domains such as finance and healthcare, where accurate forecasting and decision-making rely on advanced modeling techniques. While generative models have shown great promise in capturing the intricate dynamics inherent in time series, evaluating their performance remains a major challenge. Traditional evaluation metrics fall short due to the temporal dependencies and potential high dimensionality of the features. In this paper, we propose the REcurrent NeurAL (RENAL) Goodness-of-Fit test, a novel and statistically rigorous framework for evaluating generative time series models. By leveraging recurrent neural networks, we transform the time series into conditionally independent data pairs, enabling the application of a chi-square-based goodness-of-fit test to the temporal dependencies within the data. This approach offers a robust, theoretically grounded solution for assessing the quality of generative models, particularly in settings with limited time sequences. We demonstrate the efficacy of our method across both synthetic and real-world datasets, outperforming existing methods in terms of reliability and accuracy. Our method fills a critical gap in the evaluation of time series generative models, offering a tool that is both practical and adaptable to high-stakes applications.

Distance-Preserving Generative Modeling of Spatial Transcriptomics

Spatial transcriptomics data is invaluable for understanding the spatial organization of gene expression in tissues. There have been consistent efforts in studying how to effectively utilize the associated spatial information for refining gene expression modeling. We introduce a class of distance-preserving generative models for spatial transcriptomics, which utilizes the provided spatial information to regularize the learned representation space of gene expressions to have a similar pair-wise distance structure. This helps the latent space to capture meaningful encodings of genes in spatial proximity. We carry out theoretical analysis over a tractable loss function for this purpose and formalize the overall learning objective as a regularized evidence lower bound. Our framework grants compatibility with any variational-inference-based generative models for gene expression modeling. Empirically, we validate our proposed method on the mouse brain tissues Visium dataset and observe improved performance with variational autoencoders and scVI used as backbone models.

Counterfactual Generative Models for Time-Varying Treatments

Estimating average causal effects is a common practice to test new treatments. However, the average effect ''masks'' important individual characteristics in the counterfactual distribution, which may lead to safety, fairness, and ethical concerns. This issue is exacerbated in the temporal setting, where the treatment is sequential and time-varying, leading to an intricate influence on the counterfactual distribution. In this paper, we propose a novel conditional generative modeling approach to capture the whole counterfactual distribution, allowing efficient inference on certain statistics of the counterfactual distribution. This makes the proposed approach particularly suitable for healthcare and public policy making. Our generative modeling approach carefully tackles the distribution mismatch in the observed data and the targeted counterfactual distribution via a marginal structural model. Our method outperforms state-of-the-art baselines on both synthetic and real data.