Tootfinder

A nonparametric Bayesian analysis of independent and identically distributed observations of covariate-driven Poisson processes
Patric Dolmeta, Matteo Giordano
https://arxiv.org/abs/2509.02299

A nonparametric Bayesian analysis of independent and identically distributed observations of covariate-driven Poisson processes
An important task in the statistical analysis of inhomogeneous point processes is to investigate the influence of a set of covariates on the point-generating mechanism. In this article, we consider the nonparametric Bayesian approach to this problem, assuming that $n$ independent and identically distributed realizations of the point pattern and the covariate random field are available. In many applications, different covariates are often vastly diverse in physical nature, resulting in anisotrop…

When the Past Misleads: Rethinking Training Data Expansion Under Temporal Distribution Shifts
Chengyuan Yao, Yunxuan Tang, Christopher Brooks, Rene F. Kizilcec, Renzhe Yu
https://arxiv.org/abs/2509.01060

When the Past Misleads: Rethinking Training Data Expansion Under Temporal Distribution Shifts
Predictive models are typically trained on historical data to predict future outcomes. While it is commonly assumed that training on more historical data would improve model performance and robustness, data distribution shifts over time may undermine these benefits. This study examines how expanding historical data training windows under covariate shifts (changes in feature distributions) and concept shifts (changes in feature-outcome relationships) affects the performance and algorithmic fairn…

Graph Representation-based Model Poisoning on Federated LLMs in CyberEdge Networks
Hanlin Cai, Haofan Dong, Houtianfu Wang, Kai Li, Ozgur B. Akan
https://arxiv.org/abs/2507.01694 …

Graph Representation-based Model Poisoning on Federated LLMs in CyberEdge Networks
Federated large language models (FedLLMs) provide powerful generative capabilities in CyberEdge networks while protecting data privacy. However, FedLLMs remains highly vulnerable to model poisoning attacks. This article first reviews recent model poisoning techniques and existing defense mechanisms for FedLLMs, highlighting critical limitations, particularly under non-IID text distributions. In particular, current defenses primarily utilize distance-based outlier detection or norm constraints, …

Distributionally Robust Optimization for Aerial Multi-access Edge Computing via Cooperation of UAVs and HAPs
Ziye Jia, Can Cui, Chao Dong, Qihui Wu, Zhuang Ling, Dusit Niyato, Zhu Han
https://arxiv.org/abs/2506.01972

Distributionally Robust Optimization for Aerial Multi-access Edge Computing via Cooperation of UAVs and HAPs
With an extensive increment of computation demands, the aerial multi-access edge computing (MEC), mainly based on unmanned aerial vehicles (UAVs) and high altitude platforms (HAPs), plays significant roles in future network scenarios. In detail, UAVs can be flexibly deployed, while HAPs are characterized with large capacity and stability. Hence, in this paper, we provide a hierarchical model composed of an HAP and multi-UAVs, to provide aerial MEC services. Moreover, considering the errors of c…

Dynamic real-time multi-UAV cooperative mission planning method under multiple constraints
Chenglou Liu, Yufeng Lu, Fangfang Xie, Tingwei Ji, Yao Zheng
https://arxiv.org/abs/2506.02365

Dynamic real-time multi-UAV cooperative mission planning method under multiple constraints
As UAV popularity soars, so does the mission planning associated with it. The classical approaches suffer from the triple problems of decoupled of task assignment and path planning, poor real-time performance and limited adaptability. Aiming at these challenges, this paper proposes a dynamic real-time multi-UAV collaborative mission planning algorithm based on Dubins paths under a distributed formation structure. Dubins path with multiple advantages bridges the gap between task assignment and p…

Robustness Enhancement for Multi-Quadrotor Centralized Transportation System via Online Tuning and Learning
Tianhua Gao, Kohji Tomita, Akiya Kamimura
https://arxiv.org/abs/2509.01952

Robustness Enhancement for Multi-Quadrotor Centralized Transportation System via Online Tuning and Learning
This paper introduces an adaptive-neuro geometric control for a centralized multi-quadrotor cooperative transportation system, which enhances both adaptivity and disturbance rejection. Our strategy is to coactively tune the model parameters and learn the external disturbances in real-time. To realize this, we augmented the existing geometric control with multiple neural networks and adaptive laws, where the estimated model parameters and the weights of the neural networks are simultaneously tun…

On the role of the design phase in a linear regression
Junho Choi
https://arxiv.org/abs/2509.01861 https://arxiv.org/pdf/2509.01861

On the role of the design phase in a linear regression
The "design phase" refers to a stage in observational studies, during which a researcher constructs a subsample that achieves a better balance in covariate distributions between the treated and untreated units. In this paper, we study the role of this preliminary phase in the context of linear regression, offering a justification for its utility. To that end, we first formalize the design phase as a process of estimand adjustment via selecting a subsample. Then, we show that covariate balance o…

Minimax Optimal Two-Stage Algorithm For Moment Estimation Under Covariate Shift
Zhen Zhang, Xin Liu, Shaoli Wang, Jiaye Teng
https://arxiv.org/abs/2506.23453

Minimax Optimal Two-Stage Algorithm For Moment Estimation Under Covariate Shift
Covariate shift occurs when the distribution of input features differs between the training and testing phases. In covariate shift, estimating an unknown function's moment is a classical problem that remains under-explored, despite its common occurrence in real-world scenarios. In this paper, we investigate the minimax lower bound of the problem when the source and target distributions are known. To achieve the minimax optimal bound (up to a logarithmic factor), we propose a two-stage algorithm…

Fail2Progress: Learning from Real-World Robot Failures with Stein Variational Inference
Yixuan Huang, Novella Alvina, Mohanraj Devendran Shanthi, Tucker Hermans
https://arxiv.org/abs/2509.01746

Fail2Progress: Learning from Real-World Robot Failures with Stein Variational Inference
Skill effect models for long-horizon manipulation tasks are prone to failures in conditions not covered by training data distributions. Therefore, enabling robots to reason about and learn from failures is necessary. We investigate the problem of efficiently generating a dataset targeted to observed failures. After fine-tuning a skill effect model on this dataset, we evaluate the extent to which the model can recover from failures and minimize future failures. We propose Fail2Progress, an appro…

Evaluation of Surrogate Endpoints Based on Meta-Analysis with Surrogate Indices
Florian Stijven, Peter B. Gilbert
https://arxiv.org/abs/2509.01737 https://…

Evaluation of Surrogate Endpoints Based on Meta-Analysis with Surrogate Indices
We introduce in this paper an extension of the meta-analytic (MA) framework for evaluating surrogate endpoints. While the MA framework is regarded as the gold standard for surrogate endpoint evaluation, it is limited in its ability to handle complex surrogates and does not take into account possible differences in the distribution of baseline covariates across trials. By contrast, in the context of data fusion, the surrogate-index (SI) framework accommodates complex surrogates and allows for co…