Multivariate conformal prediction requires nonconformity scores that compress residual vectors into scalars while preserving certain implicit geometric structure of the residual distribution. We introduce a Multivariate Kernel Score (MKS) that produces prediction regions that explicitly adapt to this geometry. We show that the proposed score resembles the Gaussian process posterior variance, unifying Bayesian uncertainty quantification with the coverage guarantees of frequentist-type. Moreover, the MKS can be decomposed into an anisotropic Maximum Mean Discrepancy (MMD) that interpolates between kernel density estimation and covariance-weighted distance. We prove finite-sample coverage guarantees and establish convergence rates that depend on the effective rank of the kernel-based covariance operator rather than the ambient dimension, enabling dimension-free adaptation. On regression tasks, the MKS reduces the volume of prediction region significantly, compared to ellipsoidal baselines while maintaining nominal coverage, with larger gains at higher dimensions and tighter coverage levels.

We re-weight the discriminator loss of GAILfO and do ablation study on it. GAN does not converge in most cases. Previous results on GAIL also confirm such an observation.

Large language models are increasingly used as judges (LLM-as-a-judge) to evaluate model outputs at scale, but their assessments often diverge systematically from human judgments. We present Bridge, a unified statistical framework that explicitly bridges human and LLM evaluations under both absolute scoring and pairwise comparison paradigms. Bridge posits a latent human preference score for each prompt-response pair and models LLM deviations as linear transformations of covariates that capture sources of discrepancies. This offers a simple and principled framework for refining LLM ratings and characterizing systematic discrepancies between humans and LLMs. We provide an efficient fitting algorithm with asymptotic guarantees for statistical inference. Using six LLM judges and two benchmarks (BigGen Bench and Chatbot Arena), Bridge achieves higher agreement with human ratings (accuracy, calibration, and KL divergence) and exposes systematic human-LLM gaps.

Graph Neural Networks (GNNs) are increasingly used in conjunction with unsupervised learning techniques to learn powerful node representations, but their deployment is hindered by their high sensitivity to hyperparameter tuning and the absence of established methodologies for selecting the optimal models. To address these challenges, we propose LOBSTUR-GNN ({\bf Lo}cal {\bf B}oot{\bf s}trap for {\bf T}uning {\bf U}nsupervised {\bf R}epresentations in GNNs) i), a novel framework designed to adapt bootstrapping techniques for unsupervised graph representation learning. LOBSTUR-GNN tackles two main challenges: (a) adapting the bootstrap edge and feature resampling process to account for local graph dependencies in creating alternative versions of the same graph, and (b) establishing robust metrics for evaluating learned representations without ground-truth labels. Using locally bootstrapped resampling and leveraging Canonical Correlation Analysis (CCA) to assess embedding consistency, LOBSTUR provides a principled approach for hyperparameter tuning in unsupervised GNNs. We validate the effectiveness and efficiency of our proposed method through extensive experiments on established academic datasets, showing an 65.9\% improvement in the classification accuracy compared to an uninformed selection of hyperparameters. Finally, we deploy our framework on a real-world application, thereby demonstrating its validity and practical utility in various settings. \footnote{The code is available at \href{https://github.com/sowonjeong/lobstur-graph-bootstrap}{github.com/sowonjeong/lobstur-graph-bootstrap}.}

Text and audio simplification to increase information comprehension are important in healthcare. With the introduction of ChatGPT, an evaluation of its simplification performance is needed. We provide a systematic comparison of human and ChatGPT simplified texts using fourteen metrics indicative of text difficulty. We briefly introduce our online editor where these simplification tools, including ChatGPT, are available. We scored twelve corpora using our metrics: six text, one audio, and five ChatGPT simplified corpora. We then compare these corpora with texts simplified and verified in a prior user study. Finally, a medical domain expert evaluated these texts and five, new ChatGPT simplified versions. We found that simple corpora show higher similarity with the human simplified texts. ChatGPT simplification moves metrics in the right direction. The medical domain expert evaluation showed a preference for the ChatGPT style, but the text itself was rated lower for content retention.

To deduce new facts on a knowledge graph (KG), a link predictor learns from the graph structure and collects local evidence to find the answer to a given query. However, existing methods suffer from a severe scalability problem due to the utilization of the whole KG for prediction, which hinders their promise on large scale KGs and cannot be directly addressed by vanilla sampling methods. In this work, we propose the one-shot-subgraph link prediction to achieve efficient and adaptive prediction. The design principle is that, instead of directly acting on the whole KG, the prediction procedure is decoupled into two steps, i.e., (i) extracting only one subgraph according to the query and (ii) predicting on this single, query dependent subgraph. We reveal that the non-parametric and computation-efficient heuristics Personalized PageRank (PPR) can effectively identify the potential answers and supporting evidence. With efficient subgraph-based prediction, we further introduce the automated searching of the optimal configurations in both data and model spaces. Empirically, we achieve promoted efficiency and leading performances on five large-scale benchmarks. The code is publicly available at: https://github.com/tmlr-group/one-shot-subgraph.

The principal benefit of unsupervised graph representation learning is that a pre-trained model can be fine-tuned where data or labels are scarce. Existing approaches are domain specific, maintaining consistent node and edge attributes across the pre-training and target datasets. This precludes transfer to other domains. A model capable of positive transfer on arbitrary tasks and domains would represent the first foundation graph model. In this work we use adversarial contrastive learning to present FoToM, a graph pre-training method based on node and edge feature exclusion. We use FoToM to pre-train models over multiple graph domains, producing the first foundation graph models. We demonstrate positive transfer on evaluation datasets from multiple domains, including domains not present in pre-training data. On all datasets performance is at worst on-par and on 76% significantly better than a supervised baseline ($P \leq 0.01$), with an 8 to 40% reduction in error at 95% confidence. Contrary to other research, pre-training on a dataset with the target domain excluded leads us to better performance than pre-training on a dataset from only the target domain. The multi-domain model at worst, matches, and on 56% of tasks, significantly outperforms single-domain ($P \leq 0.01$). These results include when node labels are used in evaluation, where performance is consistently superior to single-domain or non-pre-trained models. Notably, FoToM benefits scenarios in both large or scarce data regimes for the target domains.

Neural network forms the foundation of deep learning and numerous AI applications. Classical neural networks are fully connected, expensive to train and prone to overfitting. Sparse networks tend to have convoluted structure search, suboptimal performance and limited usage. We proposed the novel uniform sparse network (USN) with even and sparse connectivity within each layer. USN has one striking property that its performance is independent of the substantial topology variation and enormous model space, thus offers a search-free solution to all above mentioned issues of neural networks. USN consistently and substantially outperforms the state-of-the-art sparse network models in prediction accuracy, speed and robustness. It even achieves higher prediction accuracy than the fully connected network with only 0.55% parameters and 1/4 computing time and resources. Importantly, USN is conceptually simple as a natural generalization of fully connected network with multiple improvements in accuracy, robustness and scalability. USN can replace the latter in a range of applications, data types and deep learning architectures. We have made USN open source at https://github.com/datapplab/sparsenet.

Human susceptibility to epidemics of misinformation and disease has grown manyfold as the world we inhabit keeps getting smaller due to increased access to online information and soaring global mobility. Social media platforms have changed the way we consume information [Schmidt et al., 2017], and more and more people find their news through social media [Newman et al., 2015]. Following the 2016 presidential election in the United States, there have been investigations into the spread of false stories, or "fake news" on social media, and based on web browsing data, archives of fact-checking websites, and results from an online survey, a recent study found that social media were an important source of election news [Allcott and Gentzkow, 2017]. While ascertainment of social network structures is generally difficult using traditional survey-based approaches, such as name generators, which are survey questions designed to solicit information about friends and acquaintances of a subject, online platforms readily capture the structure of large-scale social networks, therefore making them well suited to study spread of information whether accurate or not. Further, although the transmission mechanisms are very different, the spread of information in online systems has many similarities to the spread of infectious diseases among hosts in a population. From a mathematical and statistical point of view, one can therefore investigate the spread of pathogens and the spread of information in the same framework as long as the network structure accurately captures the transmission pathways and the spreading process is parametrized appropriately. In this paper, we consider a simple susceptible-infected (SI) process and a more complex spreading process on a fixed and known network structure. This spreading process may be conceptualized as propagating either a pathogen or a piece of information. We focus on addressing two distinct questions that are relevant in both settings: (1) How to infer the unknown parameters associated with the spreading process?

An implementation of a nonparametric Bayesian approach to solving binary classification problems on graphs is described. A hierarchical Bayesian approach with a randomly scaled Gaussian prior is considered. The prior uses the graph Laplacian to take into account the underlying geometry of the graph. A method based on a theoretically optimal prior and a more flexible variant using partial conjugacy are proposed. Two simulated data examples and two examples using real data are used in order to illustrate the proposed methods.