arXiv preprints from January 1, 2026 through September 10, 2026 — 19:37:43 EST
Multi-step rollouts are essential for model-based reinforcement learning (RL) and predictive control, yet learned dynamics models often become unstable when recursively applied, leading to divergence and unreliable policy updates. This paper proposes a model-agnostic hybrid dynamics framework that blends a provably contracting nominal...
The stringent energy-efficiency requirements of future Integrated Sensing and Communications (ISAC) systems are fundamentally challenged. Unlike conventional communication systems, ISAC transmitters must radiate significantly higher power to ensure reliable target detection, forcing the High-Power Amplifier (HPA) to operate closer to...
Learning-based Model Predictive Control (MPC) using Gaussian processes (GPs) is an effective approach for safe control in the presence of model mismatch. High-probability safety guarantees typically require uncertainty bounds that hold uniformly over the entire state--input domain, but existing bounds are available only for full GP...
Direct Device-to-Satellite (D2S) communications promise global connectivity to unmodified user equipment (UE), extending coverage beyond terrestrial networks. Realizing this promise is fundamentally challenging: severe path loss and limited UE transmit power push uplink SNRs far below terrestrial norms, while suitable spectrum remains...
Reconstructing heard speech from non-invasive electroencephalography (EEG) is challenging due to a low signal-to-noise ratio (SNR) and inter-session variability. While trial averaging improves the SNR, it is difficult to apply to continuous speech. We instead use repeated EEG responses to the same stimulus across different sessions as...
Allocation schemes that greedily maximize a readiness metric over the actuator fiber bundle of an overactuated multirotor produce commands that jump between disconnected optimal strata, demanding actuator rates no motor can deliver; effort-minimizing schemes are continuous but cannot guarantee that wrench-rate authority stays above...
Shared autonomy requires principled mechanisms for allocating and transferring control between a human and an autonomous agent. Existing approaches often rely on blending control inputs or heuristic switching rules, which lack theoretical guarantees and fail to account for the dynamics of authority transfer. This paper develops a...
We study an Estimated Time of Arrival (ETA)-based traffic-coordination framework for Urban Air Mobility corridors with merging at constrained waypoints (CWPs), where approved ETAs at CWPs serve as Required Times of Arrival (RTAs). Vehicle operators submit ETA plans at the merging point for approval by corridor-management authorities...
In survival analysis the way covariates act on the risk of an event often differs between early and late failure times, yet hazard- and mean-based summaries collapse this variation into a single number. Quantile-based modeling instead describes the full conditional distribution on the original time scale, but existing censored-data...
Under the ICH E9 (R1) addendum, treatment policy strategies for intercurrent events target the treatment effect regardless of treatment discontinuation. Sequential multiple imputation (MI) models that condition each visit's imputation on discontinuation status or pattern reduce bias relative to mixed models and standard MI, but...
Instance-wise feature selection is a valuable tool for interpreting labeled data and the predictions of black-box models. In contrast to global feature selection techniques, instance-wise methods dynamically identify important features for each instance. A growing number of methods learn a selector, which identifies important...
Bayesian dynamic borrowing (BDB) methods leverage historical data to reduce treatment effect uncertainty, yet existing approaches rely on parametric outcome models susceptible to misspecification. We propose the nonparametric latent exchangeability prior (NP-LEAP), an outcome-agnostic, assumption-lean framework to borrow information...
Generalized Bayesian inference uses weights of the form $\exp\{-β_n\ell_{n}(θ)\}$, even when the loss is available only through simulation, numerical integration, or subsampling. Exponentiating an unbiased loss estimate changes the target, and when $β_n\asymp n$ an ordinary Monte Carlo (MC) loss estimate with $M^{-1}$ variance needs a...
Seasonal infectious-disease interventions are commonly evaluated with interrupted time-series or pre--post designs that align epidemics by calendar week. When epidemic onset, speed or peak timing differs between seasons, such comparisons confound a shift in epidemic phase with a change in disease burden. We propose a Bayesian causal...
The log-rank test and Kaplan--Meier plot are standard tools for analyzing time-to-event data in randomized clinical trials, yet neither provides a summary of the magnitude of the treatment effect. Practitioners typically fill this gap by reporting a hazard ratio from a Cox proportional-hazards model or an acceleration factor from an...
Dataset alignment is a central step in data analysis across science and engineering, where the goal is to match observations between datasets. Entropic Optimal Transport (EOT) offers a computationally tractable framework for this task by encoding cross-dataset affinities in a transport plan. However, when two datasets are sampled from...
This paper studies the regret analysis for parallel Gaussian process (GP) bandit optimization. The known regret upper bounds for the widely used GP batched upper confidence bound and GP batched Thompson sampling (GP-BTS) suffer from a multiplicative factor with respect to the batch size $Q$. To avoid this degradation, existing...
Bayesian optimal experimental design (BOED) aims to collect informative data by optimizing an expected utility reflecting the goals of an experiment. However, this optimization is computationally challenging for common utilities and complex models. This is especially so for sequential or adaptive designs, where design and data...
Problem-solving log process data from computer-based assessments provide detailed information about how respondents approach and complete tasks. However, the resulting action sequences are complex and noisy, making it difficult to identify specific behaviors associated with successful performance. This paper proposes a...
Statistical inverse problems have garnered significant attention in recent years due to the growing importance of statistical learning theory and functional analytic approaches in the fields of machine learning and artificial intelligence. In this paper, we investigate the stable approximation of the element $u^{\dagger}$ that...
Ornstein-Uhlenbeck processes with flexible multivariate drift matrices are powerful models for capturing coupled, asymmetric, and damped-oscillatory mean reversion. However, likelihood-based inference is challenging because the drift matrix must remain Hurwitz stable, while likelihood and gradient evaluations require repeated, costly...
Interference occurs when one individual's treatment or exposure affects another individual's outcome. In particular, we assume partial interference, where individuals are divided into groups such that there is no interference between individuals in different groups. In observational studies, inverse probability weighting (IPW) based...
The combination of traditional statistical models and neural network (NN) components into semi-structured hybrid models is an intriguing approach to construct models that, ideally, combine traditional interpretability with the unprecedented flexibility of NNs. In order to preserve interpretability, it is usually necessary to restrict...
In multi-institutional studies, different parties hold distinct feature blocks for partially overlapping sets of individuals. Responses may also be missing for some records. In such settings, we propose Assisted Learning with Block-Missing Data (ALB) for sparse high-dimensional linear estimation and coordinatewise inference without...
LLM judges are often evaluated with a single prompt and only a few repeated calls. When their verdicts vary, it remains unclear whether the variation comes from sampling noise within a prompt or systematic differences across prompts. We formalize this distinction using a second-order response law: the distribution of...