arXiv preprints from January 1, 2026 through September 10, 2026 — 05:52:53 EST
Recipe data arises in domains such as materials synthesis, pharmaceutical formulation, and industrial manufacturing, where procedures are represented as ordered sequences of steps containing heterogeneous structured fields. Existing tabular learning methods typically flatten this structure into fixed-schema representations, limiting...
This paper introduces a dynamic role discovery technique in temporal dynamic networks, utilizing temporally regularized Non-negative Matrix Factorization (NMF). Our technique differs from existing dynamic role analysis techniques by creating a consistent set of roles across all time periods, as well as a universal transition matrix...
Free energies govern solid-state phase stability, yet computational materials discovery still relies largely on ground-state energies because free energy calculations require ensemble averages. We introduce the thermodynamic interatomic potential (TIP), which extends an interatomic potential from its static energy to a...
In their seminal work, Goldreich, Goldwasser, and Micali [CRYPTO 1984] constructed a pseudorandom function (PRF) using a black-box access to a pseudorandom generator (PRG). When combined with Levin's domain extension technique, the GGM construction invokes the PRG $ω(\log n)$ times, where $n$ denotes the input length to the PRG. To...
We study the facility location mechanism design problem where $n$ strategic agents report locations in Euclidean space and the mechanism outputs a single facility location. Each agent's cost is its distance from the facility, and our objective is to minimize the egalitarian cost, i.e., the maximum agent cost, in a strategyproof way. ...
Vision-language models (VLMs) enable embodied agents to reason and act from visual observations and language instructions. Reinforcement learning (RL) post-training enhances these capabilities using task feedback, but current on-policy RL runtimes execute rollout, reference scoring, and actor training in strict serial phases. While...
Cross-Tabular Data Generation (CTDG) seeks to learn a generative model from multiple heterogeneous tables and produce new synthetic tabular datasets. However, existing synthetic tabular data generation methods are largely restricted to single-input-table scenarios and struggle to effectively handle multiple heterogeneous tables with...
We study sparse regression codes (SPARCs) for lossy compression under simple greedy encoding rules, including both correlation-based and distance-based methods. We generalize the SPARC construction, and consider the class of \emph{additive orthogonal} regression codes, of which standard SPARCs are a special case. For this class of...
Reinforcement learning (RL) post-training provides a direct way to align diffusion models with human preferences and task-specific rewards. However, current RL algorithms for diffusion models remain fragmented: reverse-trajectory methods rely on discretized likelihood ratios, whereas forward-matching methods train on reward-labeled...
We study online statistical inference for functionals of the return distribution under a fixed policy. The return distribution is estimated by nonparametric distributional temporal-difference learning from a single Markov trajectory. For the Polyak--Ruppert averaged estimator, we prove that its root-$T$ error converges weakly to a...
Whittle-Matérn (WM) Gaussian random fields (GRFs) are defined as solutions of stochastic partial differential equations (SPDEs) and provide a natural analog of Matérn GRFs on non-Euclidean geometry where the Matérn covariance function is not valid. In particular, WM GRFs on metric graphs have been an active area of research motivated...
This paper studies two mechanistic explanations for human mortality by examining reliability theory and vitality modelling through a unified actuarial perspective. While the two approaches arise from different ageing mechanisms, we show that both can naturally generate the Gompertz law under suitable assumptions and can be extended to...
In offline RL, estimating the optimal action-value function $Q^*$ can be formulated as solving the optimal Bellman equation based solely on offline observations. A fundamental challenge is that the reward function and transition kernel are unknown, so the optimal Bellman operator is not directly observable from data. To address this...
We present new inference tools for change point detection in high-dimensional time series. We discuss two distinct statistical applications: First, sequential change point testing in an incoming data-stream. Second, retrospective localization of multiple changes, with confidence intervals at a globally controlled error level. Test...
Ensuring food security across all regions of a country requires continuous monitoring, yet household surveys often leave significant spatio-temporal gaps due to resource constraints and operational priorities. In this paper, we propose a spatio-temporal additive Gaussian process model to estimate sub-national food security time series...
Regional wet-bulb temperature means omit the spatial distribution of humid heat. We compare upper-quartile and middle-half days of the monthly regional mean at 121 sites in a specified eastern-China domain. A prespecified multiscale architecture combines Gaussian-weighted semivariances at five bandwidths with equal-month, equal-scale...
Occam's inversion is a robust algorithm to perform nonlinear geophysical inversion. It provides the smoothest model within observation noise, thereby discouraging geological overinterpretation. While Occam originally penalised l2 model roughness, l1 can be used to provide models that are visually sharp. However, l1 regularised...
Tabular anomaly detection is dominated by classical density-proxy methods (Isolation Forest, OCSVM, LOF), reconstruction-based detectors (Autoencoders, VAEs), and modern non-parametric scorers (COPOD, ECOD, Deep SVDD), all of which approximate the inlier distribution only indirectly; explicit energy-based models are largely absent....
Spillover of volatility shocks across borders during turbulent periods makes accurate equity market volatility forecasts especially critical for risk management, derivatives pricing, and regulatory capital. In this paper, we examine whether volatility forecasts improve when models incorporate information on how markets are connected,...
Network data obtained through link-tracing designs, such as snowball sampling, are collected through a mechanism that depends on the very structure the analysis seeks to estimate. Ignoring this dependence and treating the observed sample as though it were itself a complete network can lead to substantially biased inference. While the...
Joint models provide a flexible framework for characterizing the association between longitudinal and time-to-event processes and have been widely applied in biomedical research. However, fitting joint models can be computationally challenging for large-scale and complex biomedical data. This paper introduces the \proglang{R} package...
When forecasting hourly returns for 1,000 US equities, we observe an unexpected phenomenon: predictions become nearly flat and show poor stock ranking, as measured by cross-sectional correlation. We call this forecast collapse. Surprisingly, the phenomenon largely disappears when forecasting trading volume under the same setting. We...
We propose a novel methodology for fitting decision trees to latent classes. The latent class analysis methodological literature has previously been focusing on logistic models of class membership given covariates, which has important drawbacks in the presence of complex interactions between covariates: logistic models are easily...
Accurate small-area estimation of voter turnout for demographic subgroups is crucial for political analysis but methodologically challenging. Survey data suffer from over-reporting, non-representativeness, and non-ignorable non-response, while ecological inference (EI) from aggregate data is vulnerable to the ecological fallacy. We...
Distributional mismatch between the data used to construct a statistical procedure and the population to which it is ultimately applied is pervasive in modern data analysis. We study covariate shift, a fundamental instance of this problem, and develop an adaptive importance-weighted model averaging method for prediction when labeled...