Tootfinder

CINDES: Classification induced neural density estimator and simulator
Dehao Dai, Jianqing Fan, Yihong Gu, Debarghya Mukherjee
https://arxiv.org/abs/2510.00367 https://

CINDES: Classification induced neural density estimator and simulator
Neural network-based methods for (un)conditional density estimation have recently gained substantial attention, as various neural density estimators have outperformed classical approaches in real-data experiments. Despite these empirical successes, implementation can be challenging due to the need to ensure non-negativity and unit-mass constraints, and theoretical understanding remains limited. In particular, it is unclear whether such estimators can adaptively achieve faster convergence rates …

Hybrid Delta Tracking Schemes Using a Track-Length Estimator
Joanna Piper Morgan, Ilham Variansyah, Kayla B. Clements, Todd S. Palmer, Kyle E. Niemeyer
https://arxiv.org/abs/2510.00152

Hybrid Delta Tracking Schemes Using a Track-Length Estimator
In Monte Carlo radiation transport calculations, Woodcock-delta tracking is a common alternative to the more popular surface tracking technique. In this work we introduce a delta-tracking algorithm that tallies fluxes to a structured rectilinear mesh using the track-length estimator. This development also enables hybrid surface-delta tracking algorithms, because the track-length tally can be used everywhere for scalar flux estimation regardless of which tracking algorithm is employed. We use th…

A posteriori error estimation for weak Galerkin method of the fourth-order singularly perturbed problem
Shicheng Liu, Qilong Zhai
https://arxiv.org/abs/2510.00354 https://

A posteriori error estimation for weak Galerkin method of the fourth-order singularly perturbed problem
In this paper, we present a posteriori error estimation for weak Galerkin method applied to fourth order singularly perturbed problem. The weak Galerkin discretization space and numerical scheme are first described. A fully computable residual type error estimator is then constructed. Both the reliability and efficiency of the proposed estimator are rigorously demonstrated. Numerical experiments are provided to validate the theoretical findings.

Privately Estimating Black-Box Statistics
G\"unter F. Steinke, Thomas Steinke
https://arxiv.org/abs/2510.00322 https://arxiv.org/pdf/2510.00322…

Privately Estimating Black-Box Statistics
Standard techniques for differentially private estimation, such as Laplace or Gaussian noise addition, require guaranteed bounds on the sensitivity of the estimator in question. But such sensitivity bounds are often large or simply unknown. Thus we seek differentially private methods that can be applied to arbitrary black-box functions. A handful of such techniques exist, but all are either inefficient in their use of data or require evaluating the function on exponentially many inputs. In this…

An alternative bootstrap procedure for factor-augmented regression models
Peiyun Jiang, Takashi Yamagata
https://arxiv.org/abs/2510.00947 https://arxiv.org…

An alternative bootstrap procedure for factor-augmented regression models
In this paper, we propose a novel bootstrap algorithm that is more efficient than existing methods for approximating the distribution of the factor-augmented regression estimator for a rotated parameter vector. The regression is augmented by $r$ factors extracted from a large panel of $N$ variables observed over $T$ time periods. We consider general weak factor (WF) models with $r$ signal eigenvalues that may diverge at different rates, $N^{α_{k}}$, where $0<α_{k}\leq 1$ for $k=1,2,...,r$. We…

Differentially Private Learning of Exponential Distributions: Adaptive Algorithms and Tight Bounds
Bar Mahpud, Or Sheffet
https://arxiv.org/abs/2510.00790 https://

Differentially Private Learning of Exponential Distributions: Adaptive Algorithms and Tight Bounds
We study the problem of learning exponential distributions under differential privacy. Given $n$ i.i.d.\ samples from $\mathrm{Exp}(λ)$, the goal is to privately estimate $λ$ so that the learned distribution is close in total variation distance to the truth. We present two complementary pure DP algorithms: one adapts the classical maximum likelihood estimator via clipping and Laplace noise, while the other leverages the fact that the $(1-1/e)$-quantile equals $1/λ$. Each method excels in a d…

ReSWD: ReSTIR'd, not shaken. Combining Reservoir Sampling and Sliced Wasserstein Distance for Variance Reduction
Mark Boss, Andreas Engelhardt, Simon Donn\'e, Varun Jampani
https://arxiv.org/abs/2510.01061

ReSWD: ReSTIR'd, not shaken. Combining Reservoir Sampling and Sliced Wasserstein Distance for Variance Reduction
Distribution matching is central to many vision and graphics tasks, where the widely used Wasserstein distance is too costly to compute for high dimensional distributions. The Sliced Wasserstein Distance (SWD) offers a scalable alternative, yet its Monte Carlo estimator suffers from high variance, resulting in noisy gradients and slow convergence. We introduce Reservoir SWD (ReSWD), which integrates Weighted Reservoir Sampling into SWD to adaptively retain informative projection directions in o…

CLEAR: A Closed-Form Minimal-Sensor TDOA/FDOA Estimator for Moving-Source IoT Localization
Mohammad Kazzazi, Mohammad Morsali, Rouhollah Amiri
https://arxiv.org/abs/2510.04160 h…

CLEAR: A Closed-Form Minimal-Sensor TDOA/FDOA Estimator for Moving-Source IoT Localization
This paper presents CLEAR -- a closed-form localization estimator with a reduced sensor network. The proposed method is a computationally efficient, two-stage estimator that fuses time-difference-of-arrival (TDOA) and frequency-difference-of-arrival (FDOA) measurements with a minimal number of sensors. CLEAR localizes a moving source in N-dimensional space using only N+1 sensors, achieving the theoretical minimum sensor count. The first stage introduces auxiliary range and range-rate parameters…

Nonparametric Estimation of Self- and Cross-Impact
Natascha Hey, Eyal Neuman, Sturmius Tuschmann
https://arxiv.org/abs/2510.06879 https://arxiv.org/pdf/251…

Nonparametric Estimation of Self- and Cross-Impact
We introduce an offline nonparametric estimator for concave multi-asset propagator models based on a dataset of correlated price trajectories and metaorders. Compared to parametric models, our framework avoids parameter explosion in the multi-asset case and yields confidence bounds for the estimator. We implement the estimator using both proprietary metaorder data from Capital Fund Management (CFM) and publicly available S&P order flow data, where we augment the former dataset using a metaorder…

On the Optimality of the Median-of-Means Estimator under Adversarial Contamination
Xabier de Juan, Santiago Mazuelas
https://arxiv.org/abs/2510.07867 https://

On the Optimality of the Median-of-Means Estimator under Adversarial Contamination
The Median-of-Means (MoM) is a robust estimator widely used in machine learning that is known to be (minimax) optimal in scenarios where samples are i.i.d. In more grave scenarios, samples are contaminated by an adversary that can inspect and modify the data. Previous work has theoretically shown the suitability of the MoM estimator in certain contaminated settings. However, the (minimax) optimality of MoM and its limitations under adversarial contamination remain unknown beyond the Gaussian ca…

QML-FAST - A Fast Code for low-$\ell$ Tomographic Maximum Likelihood Power Spectrum Estimation
Yurii Kvasiuk, Anderson Lai, Moritz M\"unchmeyer, Kendrick M. Smith
https://arxiv.org/abs/2510.05215 …

QML-FAST - A Fast Code for low-$\ell$ Tomographic Maximum Likelihood Power Spectrum Estimation
We present a novel implementation for the quadratic maximum likelihood (QML) power spectrum estimator for multiple correlated scalar fields on the sphere. Our estimator supports arbitrary binning in redshift and multipoles $\ell$ and includes cross-correlations of redshift bins. It implements a fully optimal analysis with a pixel-wise covariance model. We implement a number of optimizations which make the estimator and associated covariance matrix computationally tractable for a low-$\ell$ anal…

Beyond the Oracle Property: Adaptive LASSO in Cointegrating Regressions
Karsten Reichold, Ulrike Schneider
https://arxiv.org/abs/2510.07204 https://arxiv.o…

Beyond the Oracle Property: Adaptive LASSO in Cointegrating Regressions
This paper establishes new asymptotic results for the adaptive LASSO estimator in cointegrating regression models. We study model selection probabilities, estimator consistency, and limiting distributions under both standard and moving-parameter asymptotics. We also derive uniform convergence rates and the fastest local-to-zero rates that can still be detected by the estimator, complementing and extending the results of Lee, Shi, and Gao (2022, Journal of Econometrics, 229, 322--349). Our main …

Causality-aware Graph Aggregation Weight Estimator for Popularity Debiasing in Top-K Recommendation
Yue Que, Yingyi Zhang, Xiangyu Zhao, Chen Ma
https://arxiv.org/abs/2510.04502

Causality-aware Graph Aggregation Weight Estimator for Popularity Debiasing in Top-K Recommendation
Graph-based recommender systems leverage neighborhood aggregation to generate node representations, which is highly sensitive to popularity bias, resulting in an echo effect during information propagation. Existing graph-based debiasing solutions refine the aggregation process with attempts such as edge reconstruction or weight adjustment. However, these methods remain inadequate in fully alleviating popularity bias. Specifically, this is because 1) they provide no insights into graph aggregati…

Robustness of Covariance Estimators with Application in Activity Detection
Hendrik Bernd Zarucha, Peter Jung, Giuseppe Caire
https://arxiv.org/abs/2510.07044 https://

Robustness of Covariance Estimators with Application in Activity Detection
The first part of this work considers a general class of covariance estimators. Each estimator of that class is generated by a real-valued function $g$ and a set of model covariance matrices $H$. If $\bf{W}$ is a potentially perturbed observation of a searched covariance matrix, then the estimator is the minimizer of the sum of $g$ applied to each eigenvalue of $\bf{W}^\frac{1}{2}\bf{Z}^{-1}\bf{W}^\frac{1}{2}$ under the constraint that $\bf{Z}$ is from $H$. It is shown that under mild condition…

New M-estimator of the leading principal component
Joni Virta, Una Radojicic, Marko Voutilainen
https://arxiv.org/abs/2510.02799 https://arxiv.org/pdf/2510…

New M-estimator of the leading principal component
We study the minimization of the non-convex and non-differentiable objective function $v \mapsto \mathrm{E} ( \| X - v \| \| X + v \| - \| X \|^2 )$ in $\mathbb{R}^p$. In particular, we show that its minimizers recover the first principal component direction of elliptically symmetric $X$ under specific conditions. The stringency of these conditions is studied in various scenarios, including a diverging number of variables $p$. We establish the consistency and asymp…

Rank Aggregation under Weak Stochastic Transitivity via a Maximum Score Estimator
Haoran Zhang, Yunxiao Chen
https://arxiv.org/abs/2510.06789 https://arxiv…

Rank Aggregation under Weak Stochastic Transitivity via a Maximum Score Estimator
Stochastic transitivity is central for rank aggregation based on pairwise comparison data. The existing models, including the Thurstone, Bradley-Terry (BT), and nonparametric BT models, adopt a strong notion of stochastic transitivity, known as strong stochastic transitivity (SST). This assumption imposes restrictive monotonicity constraints on the pairwise comparison probabilities, which is often unrealistic for real-world applications. This paper introduces a maximum score estimator for aggre…

Simultaneous Calibration of Noise Covariance and Kinematics for State Estimation of Legged Robots via Bi-level Optimization
Denglin Cheng, Jiarong Kang, Xiaobin Xiong
https://arxiv.org/abs/2510.11539

Simultaneous Calibration of Noise Covariance and Kinematics for State Estimation of Legged Robots via Bi-level Optimization
Accurate state estimation is critical for legged and aerial robots operating in dynamic, uncertain environments. A key challenge lies in specifying process and measurement noise covariances, which are typically unknown or manually tuned. In this work, we introduce a bi-level optimization framework that jointly calibrates covariance matrices and kinematic parameters in an estimator-in-the-loop manner. The upper level treats noise covariances and model parameters as optimization variables, while …

Non-Stationary Online Structured Prediction with Surrogate Losses
Shinsaku Sakaue, Han Bao, Yuzhou Cao
https://arxiv.org/abs/2510.07086 https://arxiv.org/p…

Non-Stationary Online Structured Prediction with Surrogate Losses
Online structured prediction, including online classification as a special case, is the task of sequentially predicting labels from input features. Therein the surrogate regret -- the cumulative excess of the target loss (e.g., 0-1 loss) over the surrogate loss (e.g., logistic loss) of the fixed best estimator -- has gained attention, particularly because it often admits a finite bound independent of the time horizon $T$. However, such guarantees break down in non-stationary environments, where…

A debiased Bernoulli factory and unbiased estimation of a probability
Jere Koskela, Toni Karvonen, Krzysztof {\L}atuszy\'nski, Dario Span\`o
https://arxiv.org/abs/2510.01941

A debiased Bernoulli factory and unbiased estimation of a probability
Given a known function $f : [0, 1] \mapsto (0, 1)$ and a random but almost surely finite number of independent, Ber$(x)$-distributed random variables with unknown $x \in [0, 1]$, we construct an unbiased, $[0, 1]$-valued estimator of the probability $f(x) \in (0, 1)$. Our estimator is based on so-called debiasing, or randomly truncating a telescopic series of consistent estimators. Constructing these consistent estimators from the coefficients of a particular Bernoulli factory for $f$ yields pr…

Learning-based model predictive control with moving horizon state estimation for autonomous racing
Yassine Kebbati, Andreas Rauh, Naima Ait-Oufroukh, Dalil Ichalal, Vincent Vigneron
https://arxiv.org/abs/2510.05366

Learning-based model predictive control with moving horizon state estimation for autonomous racing
This paper addresses autonomous racing by introducing a real-time nonlinear model predictive controller (NMPC) coupled with a moving horizon estimator (MHE). The racing problem is solved by an NMPC-based off-line trajectory planner that computes the best trajectory while considering the physical limits of the vehicle and circuit constraints. The developed controller is further enhanced with a learning extension based on Gaussian process regression that improves model predictions. The proposed c…

A Honest Cross-Validation Estimator for Prediction Performance
Tianyu Pan, Vincent Z. Yu, Viswanath Devanarayan, Lu Tian
https://arxiv.org/abs/2510.07649 https://

A Honest Cross-Validation Estimator for Prediction Performance
Cross-validation is a standard tool for obtaining a honest assessment of the performance of a prediction model. The commonly used version repeatedly splits data, trains the prediction model on the training set, evaluates the model performance on the test set, and averages the model performance across different data splits. A well-known criticism is that such cross-validation procedure does not directly estimate the performance of the particular model recommended for future use. In this paper, w…

Some Remarks on Commuting Probability
Alexander Kushkuley
https://arxiv.org/abs/2510.04458 https://arxiv.org/pdf/2510.04458

Some Remarks on Commuting Probability
We introduce a weighted sum of irreducible character ratios as an estimator for commutator probabilities. The estimator yields Frobenius formula when applied to a regular representation

Robust Cislunar Navigation via LFT-Based $\mathcal{H}_\infty$ Filtering with Bearing-Only Measurements
Raktim Bhattacharya
https://arxiv.org/abs/2510.04942 https://

Robust Cislunar Navigation via LFT-Based $\mathcal{H}_\infty$ Filtering with Bearing-Only Measurements
This paper develops a robust estimation framework for cislunar navigation that embeds the Circular Restricted Three-Body Problem (CR3BP) dynamics and bearing-only optical measurements within a Linear Fractional Transformation (LFT) representation. A full-order $\mathcal{H}_\infty$ observer is synthesized with explicit $\mathcal{L}_2$ performance bounds. The formulation yields a nonlinear estimator that operates directly on the governing equations and avoids reliance on local linearizations. Dom…

Replaced article(s) found for q-fin.RM. https://arxiv.org/list/q-fin.RM/new
[1/1]:
- Empirical estimator of diversification quotient
Xia Han, Liyuan Lin, Mengshi Zhao

A Bernstein polynomial approach for the estimation of cumulative distribution functions in the presence of missing data
Rihab Gharbi, Wissem Jedidi, Salah Khardani, Fr\'ed\'eric Ouimet
https://arxiv.org/abs/2510.07235

A Bernstein polynomial approach for the estimation of cumulative distribution functions in the presence of missing data
We study nonparametric estimation of univariate cumulative distribution functions (CDFs) pertaining to data missing at random. The proposed estimators smooth the inverse probability weighted (IPW) empirical CDF with the Bernstein operator, yielding monotone, $[0,1]$-valued curves that automatically adapt to bounded supports. We analyze two versions: a pseudo estimator that uses known propensities and a feasible estimator that uses propensities estimated nonparametrically from discrete auxiliary…

Inference in pseudo-observation-based regression using (biased) covariance estimation and naive bootstrapping
Simon Mack, Morten Overgaard, Dennis Dobler
https://arxiv.org/abs/2510.06815

Inference in pseudo-observation-based regression using (biased) covariance estimation and naive bootstrapping
We demonstrate that the usual Huber-White estimator is not consistent for the limiting covariance of parameter estimates in pseudo-observation regression approaches. By confirming that a plug-in estimator can be used instead, we obtain asymptotically exact and consistent tests for general linear hypotheses in the parameters of the model. Additionally, we confirm that naive bootstrapping can not be used for covariance estimation in the pseudo-observation model either. However, it can be used for…

Crosslisted article(s) found for cond-mat.dis-nn. https://arxiv.org/list/cond-mat.dis-nn/new
[1/1]:
- High-Dimensional Learning Dynamics of Quantized Models with Straight-Through Estimator
Yuma Ichikawa, Shuhei Kashiwamura, Ayaka Sakata

HYPERDOA: Robust and Efficient DoA Estimation using Hyperdimensional Computing
Rajat Bhattacharjya, Woohyeok Park, Arnab Sarkar, Hyunwoo Oh, Mohsen Imani, Nikil Dutt
https://arxiv.org/abs/2510.10718

HYPERDOA: Robust and Efficient DoA Estimation using Hyperdimensional Computing
Direction of Arrival (DoA) estimation techniques face a critical trade-off, as classical methods often lack accuracy in challenging, low signal-to-noise ratio (SNR) conditions, while modern deep learning approaches are too energy-intensive and opaque for resource-constrained, safety-critical systems. We introduce HYPERDOA, a novel estimator leveraging Hyperdimensional Computing (HDC). The framework introduces two distinct feature extraction strategies -- Mean Spatial-Lag Autocorrelation and Spa…

Inverse Portfolio Optimization with Synthetic Investor Data: Recovering Risk Preferences under Uncertainty
Jinho Cha, Long Pham, Thi Le Hoa Vo, Jaeyoung Cho, Jaejin Lee
https://arxiv.org/abs/2510.06986

Inverse Portfolio Optimization with Synthetic Investor Data: Recovering Risk Preferences under Uncertainty
This study develops an inverse portfolio optimization framework for recovering latent investor preferences including risk aversion, transaction cost sensitivity, and ESG orientation from observed portfolio allocations. Using controlled synthetic data, we assess the estimator's statistical properties such as consistency, coverage, and dynamic regret. The model integrates robust optimization and regret-based inference to quantify welfare losses under preference misspecification and market shocks.…

Robust and efficient estimation for the Generalized Extreme-Value distribution with application to flood frequency analysis in the UK
Nathan Huet, Ilaria Prosdocimi
https://arxiv.org/abs/2510.03338

Robust and efficient estimation for the Generalized Extreme-Value distribution with application to flood frequency analysis in the UK
A common approach for modeling extremes, such as peak flow or high temperatures, is the three-parameter Generalized Extreme-Value distribution. This is typically fit to extreme observations, here defined as maxima over disjoint blocks. This results in limited sample sizes and consequently, the use of classic estimators, such as the maximum likelihood estimator, may be inappropriate, as they are highly sensitive to outliers. To address these limitations, we propose a novel robust estimator based…

On Improvement of Control Chart using Repetitive Sampling for Monitoring Process Mean
Fahad Rafique, Saadia Masood, Shabbir Ahmad, Sadaf Amin
https://arxiv.org/abs/2510.05086 ht…

On Improvement of Control Chart using Repetitive Sampling for Monitoring Process Mean
In the practical industry, the most commonly used application of statistical analysis for monitoring the process mean is the control chart. Control charts are generated based on the presumption that we have a sample from a stable process. The control chart then provides a graphical display to test this presumption. In the existing estimator \textcolor{red}{$Mr$}, researchers use a technique involving repetitive sampling along with an auxiliary variable for detecting and monitoring the statistic…

Robustified Gaussian quasi-likelihood inference for volatility
Shoichi Eguchi, Hiroki Masuda
https://arxiv.org/abs/2510.02666 https://arxiv.org/pdf/2510.02…

Robustified Gaussian quasi-likelihood inference for volatility
We consider statistical inference for a class of continuous regression models contaminated by finite-activity jumps and spike noises. We propose an $M$-estimator through some easy-to-implement one-parameter robustifications of the conventional Gaussian quasi-likelihood function, and prove its asymptotic mixed normality at the standard rate $\sqrt{n}$. It is theoretically shown that the estimator is simultaneously robust against the contaminations in both the covariate process and the objective …

A Robust Neural Control Design for Multi-drone Slung Payload Manipulation with Control Contraction Metrics
Xinyuan Liang, Longhao Qian, Yi Lok Lo, Hugh H. T. Liu
https://arxiv.org/abs/2510.01489

A Robust Neural Control Design for Multi-drone Slung Payload Manipulation with Control Contraction Metrics
This paper presents a robust neural control design for a three-drone slung payload transportation system to track a reference path under external disturbances. The control contraction metric (CCM) is used to generate a neural exponentially converging baseline controller while complying with control input saturation constraints. We also incorporate the uncertainty and disturbance estimator (UDE) technique to dynamically compensate for persistent disturbances. The proposed framework yields a modu…

Central limit theorem and Cram\'{e}r-type moderate deviations for Milstein scheme
Peng Chen, Hui Jiang, Jing Wang
https://arxiv.org/abs/2510.03053 https://

Central limit theorem and Cramér-type moderate deviations for Milstein scheme
In this paper, we investigate the Milstein numerical scheme with step size $η$ for a stochastic differential equation driven by multiplicative Brownian motion. Under some appropriate coefficient conditions, the continuous-time system and its discrete Milstein scheme approximation each possess unique invariant measures, which we denote by $π$ and $π_η$ respectively. We first establish a central limit theorem for the empirical measure $Π_η$, a statistical consistent estimator of $π_η$. Su…

Evaluating and Learning Optimal Dynamic Treatment Regimes under Truncation by Death
Sihyung Park (North Carolina State University), Wenbin Lu (North Carolina State University), Shu Yang (North Carolina State University)
https://arxiv.org/abs/2510.07501

Evaluating and Learning Optimal Dynamic Treatment Regimes under Truncation by Death
Truncation by death, a prevalent challenge in critical care, renders traditional dynamic treatment regime (DTR) evaluation inapplicable due to ill-defined potential outcomes. We introduce a principal stratification-based method, focusing on the always-survivor value function. We derive a semiparametrically efficient, multiply robust estimator for multi-stage DTRs, demonstrating its robustness and efficiency. Empirical validation and an application to electronic health records showcase its utili…

Replaced article(s) found for hep-lat. https://arxiv.org/list/hep-lat/new
[1/1]:
- Gradient and Hessian-Based Temperature Estimator in Lattice Gauge Theories: A Diagnostic Tool for...
Navdeep Singh Dhindsa, Anosh Joseph, Vamika Longia

Crosslisted article(s) found for stat.ME. https://arxiv.org/list/stat.ME/new
[1/1]:
- New M-estimator of the leading principal component
Joni Virta, Una Radojicic, Marko Voutilainen

Drift estimation for rough processes under small noise asymptotic : QMLE approach
Arnaud Gloter (LaMME), Nakahiro Yoshida
https://arxiv.org/abs/2510.09028 https://

Drift estimation for rough processes under small noise asymptotic : QMLE approach
We consider a process X^$ε$ solution of a stochastic Volterra equation with an unknown parameter $θ$ in the drift function. The Volterra kernel is singular and given by K(u) = cu $α$-1 __u>0 with $α$ $\in$ (1/2, 1) and it is assumed that the diffusion coefficient is proportional to $ε$ $\rightarrow$ 0 Based on the observation of a discrete sampling with mesh h $\rightarrow$ 0 of the Volterra process, we build a Quasi Maximum Likelihood Estimator. The main step is to assess the error arisin…

Navigating Sparsities in High-Dimensional Linear Contextual Bandits
Rui Zhao, Zihan Chen, Zemin Zheng
https://arxiv.org/abs/2510.08435 https://arxiv.org/pd…

Navigating Sparsities in High-Dimensional Linear Contextual Bandits
High-dimensional linear contextual bandit problems remain a significant challenge due to the curse of dimensionality. Existing methods typically consider either the model parameters to be sparse or the eigenvalues of context covariance matrices to be (approximately) sparse, lacking general applicability due to the rigidity of conventional reward estimators. To overcome this limitation, a powerful pointwise estimator is introduced in this work that adaptively navigates both kinds of sparsity. Ba…

General Divergence Regularized Optimal Transport: Sample Complexity and Central Limit Theorems
Jiaping Yang, Yunxin Zhang
https://arxiv.org/abs/2510.02489 https://

General Divergence Regularized Optimal Transport: Sample Complexity and Central Limit Theorems
Optimal transport has emerged as a fundamental methodology with applications spanning multiple research areas in recent years. However, the convergence rate of the empirical estimator to its population counterpart suffers from the curse of dimensionality, which prevents its application in high-dimensional spaces. While entropic regularization has been proven to effectively mitigate the curse of dimensionality and achieve a parametric convergence rate under mild conditions, these statistical gua…

A theoretical framework for M-posteriors: frequentist guarantees and robustness properties
Juraj Marusic, Marco Avella Medina, Cynthia Rush
https://arxiv.org/abs/2510.01358 http…

A theoretical framework for M-posteriors: frequentist guarantees and robustness properties
We provide a theoretical framework for a wide class of generalized posteriors that can be viewed as the natural Bayesian posterior counterpart of the class of M-estimators in the frequentist world. We call the members of this class M-posteriors and show that they are asymptotically normally distributed under mild conditions on the M-estimation loss and the prior. In particular, an M-posterior contracts in probability around a normal distribution centered at an M-estimator, showing frequentist c…