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557fb052f39dfd29455d9979b010168b1ae03dde8a459ea44012ba14923b1608
2026-01-13T00:00:00-05:00
A Kernelization-Based Approach to Nonparametric Binary Choice Models
arXiv:2410.15734v2 Announce Type: replace Abstract: We propose a new estimator for nonparametric binary choice models that does not impose a parametric structure on either the systematic function of covariates or the distribution of the error term. A key advantage of our approach is its computational scalability in the...
https://arxiv.org/abs/2410.15734
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a749ca5747d20421d181c38d46cbfc5d5d2e5d11cb342b544e64a7ef6d5e1110
2026-01-13T00:00:00-05:00
Floods do not sink prices, historical memory does: How flood risk impacts the Italian housing market
arXiv:2502.12116v3 Announce Type: replace Abstract: Do home prices incorporate flood risk in the immediate aftermath of specific flood events, or is it the repeated exposure over the years that plays a more significant role? We address this question through the first systematic study of the Italian housing market, whic...
https://arxiv.org/abs/2502.12116
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01f161fae9e8b404de97f78cd0945bc96b4801e73e65714376b1c943d946f610
2026-01-13T00:00:00-05:00
Does Ideological Polarization Lead to Policy Polarization?
arXiv:2502.14712v5 Announce Type: replace Abstract: I study an election between two ideologically polarized parties that are both office- and policy-motivated. The parties compete by proposing policies on a single issue. The analysis uncovers a non-monotonic relationship between ideological and policy polarization. Whe...
https://arxiv.org/abs/2502.14712
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f490d215783e9a0e9c8b92dde28cf7b217720794fc7d63789b614f4c1a4ffdcb
2026-01-13T00:00:00-05:00
The heterogeneous causal effects of the EU's Cohesion Fund
arXiv:2504.13223v2 Announce Type: replace Abstract: This paper estimates the causal effect of EU cohesion policy on regional output and investment, focusing on the Cohesion Fund (CF), a comparatively understudied instrument. Departing from standard approaches such as regression discontinuity (RDD) and instrumental vari...
https://arxiv.org/abs/2504.13223
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7b87f0196021cdbe142890f97c937eccd276b302b62bb784f2e125e76b05fc7c
2026-01-13T00:00:00-05:00
Causal Inference for Experiments with Latent Outcomes: Key Results and Their Implications for Design and Analysis
arXiv:2505.21909v3 Announce Type: replace Abstract: How should researchers analyze randomized experiments in which the main outcome is latent and measured in multiple ways but each measure contains some degree of error? We first identify a critical study-specific noncomparability problem in existing methods for handlin...
https://arxiv.org/abs/2505.21909
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3a9571bc47e3c34d196ce162eafad4f205b811d2fd694d232d86a7db5bf6d9c4
2026-01-13T00:00:00-05:00
Making Interpretable Discoveries from Unstructured Data: A High-Dimensional Multiple Hypothesis Testing Approach
arXiv:2511.01680v2 Announce Type: replace Abstract: Social scientists are increasingly turning to unstructured datasets to unlock new empirical insights, e.g., estimating descriptive statistics of or causal effects on quantitative measures derived from text, audio, or video data. In many such settings, unsupervised ana...
https://arxiv.org/abs/2511.01680
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2ba79e2f9117a4ae04a369b5e9b7504b223f4540413f0bb544dcce3c0098094b
2026-01-13T00:00:00-05:00
Quantile Selection in the Gender Pay Gap
arXiv:2511.16187v2 Announce Type: replace Abstract: We propose a new approach to estimate selection-corrected quantiles of the gender wage gap. Our method employs instrumental variables that explain variation in the latent variable but, conditional on the latent process, do not directly affect selection. We provide sem...
https://arxiv.org/abs/2511.16187
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e8d2e8555163f6559438c771bfcd048c4a82d87c5f8eaaea3f5986b364b73da0
2026-01-13T00:00:00-05:00
Graph structure learning for stable processes
arXiv:2601.06264v1 Announce Type: new Abstract: We introduce Ising-H\"usler-Reiss processes, a new class of multivariate L\'evy processes that allows for sparse modeling of the path-wise conditional independence structure between marginal stable processes with different stability indices. The underlying conditional ind...
https://arxiv.org/abs/2601.06264
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6f698fdd61f3708973380a82e7606f039a7286d66e53a929374fb64340e53792
2026-01-13T00:00:00-05:00
A Framework for Estimating Restricted Mean Survival Time Difference using Pseudo-observations
arXiv:2601.06296v1 Announce Type: new Abstract: A targeted learning (TL) framework is developed to estimate the difference in the restricted mean survival time (RMST) for a clinical trial with time-to-event outcomes. The approach starts by defining the target estimand as the RMST difference between investigational and ...
https://arxiv.org/abs/2601.06296
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2648c2125bb2e947465bacc9041bdc983b5be3aa7ca08423d33600e1e86bfb61
2026-01-13T00:00:00-05:00
Efficient Data Reduction Via PCA-Guided Quantile Based Sampling
arXiv:2601.06375v1 Announce Type: new Abstract: In large-scale statistical modeling, reducing data size through subsampling is essential for balancing computational efficiency and statistical accuracy. We propose a new method, Principal Component Analysis guided Quantile Sampling (PCA-QS), which projects data onto prin...
https://arxiv.org/abs/2601.06375
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caeff30a93f7dc5e26bd5e53aa18957ce6f3822382bae1c7a6991124a0b4a1fa
2026-01-13T00:00:00-05:00
Empirical Likelihood Test for Common Invariant Subspace of Multilayer Networks based on Monte Carlo Approximation
arXiv:2601.06390v1 Announce Type: new Abstract: Multilayer (or multiple) networks are widely used to represent diverse patterns of relationships among objects in increasingly complex real-world systems. Identifying a common invariant subspace across network layers has become an active area of research, as such a subspa...
https://arxiv.org/abs/2601.06390
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70daaa3461d1cae08959879c9438641e84bfcde6807f037072f32eb4a0c21c2f
2026-01-13T00:00:00-05:00
Triple-dyad ratio estimation for the $p_1$ model
arXiv:2601.06481v1 Announce Type: new Abstract: Although the $p_1$ model was proposed 40 years ago, little progress has been made to address asymptotic theories in this model, that is, neither consistency of the maximum likelihood estimator (MLE) nor other parameter estimation with statistical guarantees is understood....
https://arxiv.org/abs/2601.06481
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f9b978997f72e5b2bae0fa2b8167b4e6614ffda0b1d30cc2cd9a65339463a6a5
2026-01-13T00:00:00-05:00
Bayesian Optimization of Noisy Log-Likelihoods Evaluated by Particle Filters -- One Parameter Case --
arXiv:2601.06545v1 Announce Type: new Abstract: Likelihood functions evaluated using particle filters are typically noisy, computationally expensive, and non-differentiable due to Monte Carlo variability. These characteristics make conventional optimization methods difficult to apply directly or potentially unreliable....
https://arxiv.org/abs/2601.06545
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8e4ee34a41e334d16c009ee5d18e72664e0a583c364e3702fef7c9f07e9ec460
2026-01-13T00:00:00-05:00
Mittag Leffler Distributions Estimation and Autoregressive Framework
arXiv:2601.06610v1 Announce Type: new Abstract: This work deals with the estimation of parameters of Mittag-Leffler (ML($\alpha, \sigma$)) distribution. We estimate the parameters of ML($\alpha, \sigma$) using empirical Laplace transform method. The simulation study indicates that the proposed method provides satisfact...
https://arxiv.org/abs/2601.06610
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fd5ae007c4a6b26f0a23d638badfe641bd3345a021d997a93a7059d837a49f5a
2026-01-13T00:00:00-05:00
R-Estimation with Right-Censored Data
arXiv:2601.06685v1 Announce Type: new Abstract: This paper considers the problem of directly generalizing the R-estimator under a linear model formulation with right-censored outcomes. We propose a natural generalization of the rank and corresponding estimating equation for the R-estimator in the case of the Wilcoxon (...
https://arxiv.org/abs/2601.06685
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a6f7b24c01ad89128a1b8bad423442a197bc403eeeb25068ca499e5a56bf7776
2026-01-13T00:00:00-05:00
Nonparametric contaminated Gaussian mixture of regressions
arXiv:2601.06695v1 Announce Type: new Abstract: Semi- and non-parametric mixture of regressions are a very useful flexible class of mixture of regressions in which some or all of the parameters are non-parametric functions of the covariates. These models are, however, based on the Gaussian assumption of the component e...
https://arxiv.org/abs/2601.06695
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b7c0377f81f72ff023ac08cffe9c405b91a963ca83bc478fa2a71e3eec354bff
2026-01-13T00:00:00-05:00
Adversarially Perturbed Precision Matrix Estimation
arXiv:2601.06807v1 Announce Type: new Abstract: Precision matrix estimation is a fundamental topic in multivariate statistics and modern machine learning. This paper proposes an adversarially perturbed precision matrix estimation framework, motivated by recent developments in adversarial training. The proposed framewor...
https://arxiv.org/abs/2601.06807
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10f070a56ff053d13261a9f93f2d235253a7962cf7f38ae1e306dbc1eebc212e
2026-01-13T00:00:00-05:00
Likelihood-Based Regression for Weibull Accelerated Life Testing Model Under Censored Data
arXiv:2601.06890v1 Announce Type: new Abstract: In this paper, we investigate accelerated life testing (ALT) models based on the Weibull distribution with stress-dependent shape and scale parameters. Temperature and voltage are treated as stress variables influencing the lifetime distribution. Data are assumed to be co...
https://arxiv.org/abs/2601.06890
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114438407329a887811e63308e3d4859df88169383aa8df13e4d9db0b2dfb0a9
2026-01-13T00:00:00-05:00
Minimum information Markov model
arXiv:2601.06900v1 Announce Type: new Abstract: The analysis of high-dimensional time series data has become increasingly important across a wide range of fields. Recently, a method for constructing the minimum information Markov kernel on finite state spaces was established. In this study, we propose a statistical mod...
https://arxiv.org/abs/2601.06900
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22b05f47945dc288361a6dffd8fb2b8684f2d32b8c9ed8173809c1d370fe39e6
2026-01-13T00:00:00-05:00
Localization Estimator for High Dimensional Tensor Covariance Matrices
arXiv:2601.06989v1 Announce Type: new Abstract: This paper considers covariance matrix estimation of tensor data under high dimensionality. A multi-bandable covariance class is established to accommodate the need for complex covariance structures of multi-layer lattices and general covariance decay patterns. We propose...
https://arxiv.org/abs/2601.06989
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27bcc009c8a12a48ea40962f78bc6d6b7686a755448f6f62aa2973e14243ec32
2026-01-13T00:00:00-05:00
Semiparametric Analysis of Interval-Censored Data Subject to Inaccurate Diagnoses with A Terminal Event
arXiv:2601.07044v1 Announce Type: new Abstract: Interval-censoring frequently occurs in studies of chronic diseases where disease status is inferred from intermittently collected biomarkers. Although many methods have been developed to analyze such data, they typically assume perfect disease diagnosis, which often does...
https://arxiv.org/abs/2601.07044
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2cbd32261d8494fe8256e4983b79456a6d1798119318db95c098a31e58a6298b
2026-01-13T00:00:00-05:00
FormulaCompiler.jl and Margins.jl: Efficient Marginal Effects in Julia
arXiv:2601.07065v1 Announce Type: new Abstract: Marginal effects analysis is fundamental to interpreting statistical models, yet existing implementations face computational constraints that limit analysis at scale. We introduce two Julia packages that address this gap. Margins.jl provides a clean two-function API organ...
https://arxiv.org/abs/2601.07065
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3bc414259bfb559b59e81b2ec8139e240687f71818b5e41774947e0065abdda2
2026-01-13T00:00:00-05:00
The Bayesian Intransitive Bradley-Terry Model via Combinatorial Hodge Theory
arXiv:2601.07158v1 Announce Type: new Abstract: Pairwise comparison data are widely used to infer latent rankings in areas such as sports, social choice, and machine learning. The Bradley-Terry model provides a foundational probabilistic framework but inherently assumes transitive preferences, explaining all comparison...
https://arxiv.org/abs/2601.07158
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d9e4005ddb97950c00c7b38fdc754469ac87fd45e288a35b13ac0460c7c8c170
2026-01-13T00:00:00-05:00
Principal component-guided sparse reduced-rank regression
arXiv:2601.07202v1 Announce Type: new Abstract: Reduced-rank regression estimates regression coefficients by imposing a low-rank constraint on the matrix of regression coefficients, thereby accounting for correlations among response variables. To further improve predictive accuracy and model interpretability, several r...
https://arxiv.org/abs/2601.07202
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41c7b39a8d0aa0ee79e8817bab373e7ad76f4cb68a60808186673d6488d0793e
2026-01-13T00:00:00-05:00
Compounded Linear Failure Rate Distribution: Properties, Simulation and Analysis
arXiv:2601.07249v1 Announce Type: new Abstract: This paper proposes a new extension of the linear failure rate (LFR) model to better capture real-world lifetime data. The model incorporates an additional shape parameter to increase flexibility. It helps model the minimum survival time from a set of LFR distributed vari...
https://arxiv.org/abs/2601.07249
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8e7f00f4c86dc52835bc906c33de6fcd07174ad158a7e5124bae9de57a95c74d
2026-01-13T00:00:00-05:00
Connections as treatment: causal inference with edge interventions in networks
arXiv:2601.07267v1 Announce Type: new Abstract: Causal inference has traditionally focused on interventions at the unit level. In many applications, however, the central question concerns the causal effects of connections between units, such as transportation links, social relationships, or collaborative ties. We devel...
https://arxiv.org/abs/2601.07267
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b8ee56d4eb328c10a2dc97045b8485ac90981ec7533c6f9c5352c08eeb3fdb52
2026-01-13T00:00:00-05:00
Minimum Wasserstein distance estimator under covariate shift: closed-form, super-efficiency and irregularity
arXiv:2601.07282v1 Announce Type: new Abstract: Covariate shift arises when covariate distributions differ between source and target populations while the conditional distribution of the response remains invariant, and it underlies problems in missing data and causal inference. We propose a minimum Wasserstein distance...
https://arxiv.org/abs/2601.07282
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cadd67651edafb9b7cd1f00ad9f0066112a3c97875f48305e7bd66f3d7b72baf
2026-01-13T00:00:00-05:00
Cauchy-Gaussian Overbound for Heavy-tailed GNSS Measurement Errors
arXiv:2601.07299v1 Announce Type: new Abstract: Overbounds of heavy-tailed measurement errors are essential to meet stringent navigation requirements in integrity monitoring applications. This paper proposes to leverage the bounding sharpness of the Cauchy distribution in the core and the Gaussian distribution in the t...
https://arxiv.org/abs/2601.07299
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f91f85a5b78f19bfcba65608d02bb90cae6a74cee64a90ba9fe4bf3c07186924
2026-01-13T00:00:00-05:00
Inference for Multiple Change-points in Piecewise Locally Stationary Time Series
arXiv:2601.07400v1 Announce Type: new Abstract: Change-point detection and locally stationary time series modeling are two major approaches for the analysis of non-stationary data. The former aims to identify stationary phases by detecting abrupt changes in the dynamics of a time series model, while the latter employs ...
https://arxiv.org/abs/2601.07400
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c95743a0f27a797823c720b3aad87c34d752abfe8e3d3e65c0a362a0f1518c6f
2026-01-13T00:00:00-05:00
Penalized Likelihood Optimization for Adaptive Neighborhood Clustering in Time-to-Event Data with Group-Level Heterogeneity
arXiv:2601.07446v1 Announce Type: new Abstract: The identification of patient subgroups with comparable event-risk dynamics plays a key role in supporting informed decision-making in clinical research. In such settings, it is important to account for the inherent dependence that arises when individuals are nested withi...
https://arxiv.org/abs/2601.07446
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1ec248051438164093ad1d83253890f7c7476b1df8fb38a51a1a50fb826d31c9
2026-01-13T00:00:00-05:00
Ridge-penalised spectral least-squares estimation for point processes
arXiv:2601.07490v1 Announce Type: new Abstract: Penalised estimation methods for point processes usually rely on a large amount of independent repetitions for cross-validation purposes. However, in the case of a single realisation of the process, existing cross-validation methods may be impractical depending on the cho...
https://arxiv.org/abs/2601.07490
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10db849439c83d611fea7e505e1cc6508ce84b28116fbdab475a40f3b45b4131
2026-01-13T00:00:00-05:00
Population-Adjusted Indirect Treatment Comparison with the outstandR Package in R
arXiv:2601.07532v1 Announce Type: new Abstract: Indirect treatment comparisons (ITCs) are essential in Health Technology Assessment (HTA) when head-to-head clinical trials are absent. A common challenge arises when attempting to compare a treatment with available individual patient data (IPD) against a competitor with ...
https://arxiv.org/abs/2601.07532
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0366b513250ff8903b1ab12c2acea376ecde9cd107111edc9dcdf82868224827
2026-01-13T00:00:00-05:00
Bayesian Handwriting Evidence Evaluation using MANOVA via Fourier-Based Extracted Features
arXiv:2601.07534v1 Announce Type: new Abstract: This paper proposes a novel statistical approach that aims at the identification of valid and useful patterns in handwriting examination via Bayesian modeling. Starting from a sample of characters selected among 13 French native writers, an accurate loop reconstruction ca...
https://arxiv.org/abs/2601.07534
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138e8ad011966ad83c2d4b1423d48ad011b0aea7fd8466c345c3aad9164d3199
2026-01-13T00:00:00-05:00
Functional Synthetic Control Methods for Metric Space-Valued Outcomes
arXiv:2601.07539v1 Announce Type: new Abstract: The synthetic control method (SCM) is a widely used tool for evaluating causal effects of policy changes in panel data settings. Recent studies have extended its framework to accommodate complex outcomes that take values in metric spaces, such as distributions, functions,...
https://arxiv.org/abs/2601.07539
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137e3f3b34b3a49fb1f390493d6a935ea223b41fa869e68b35f6fae9c4271211
2026-01-13T00:00:00-05:00
An evaluation of empirical equations for assessing local scour around bridge piers using global sensitivity analysis
arXiv:2601.07594v1 Announce Type: new Abstract: Bridge scour is a complex phenomenon combining hydrological, geotechnical and structural processes. Bridge scour is the leading cause of bridge collapse, which can bring catastrophic consequences including the loss of life. Estimating scour on bridges is an important task...
https://arxiv.org/abs/2601.07594
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4feb8f04f594e78b64f87c90a38179f6c2003a1e4dcf7016d2735e00b658a851
2026-01-13T00:00:00-05:00
Omitted covariates bias and finite mixtures of regression models for longitudinal responses
arXiv:2601.07609v1 Announce Type: new Abstract: Individual-specific, time-constant, random effects are often used to model dependence and/or to account for omitted covariates in regression models for longitudinal responses. Longitudinal studies have known a huge and widespread use in the last few years as they allow to...
https://arxiv.org/abs/2601.07609
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798540811e2aacd1206848c88ec36991b27dacb0e0b87afd01e8622831b2883e
2026-01-13T00:00:00-05:00
Dual-Level Models for Physics-Informed Multi-Step Time Series Forecasting
arXiv:2601.07640v1 Announce Type: new Abstract: This paper develops an approach for multi-step forecasting of dynamical systems by integrating probabilistic input forecasting with physics-informed output prediction. Accurate multi-step forecasting of time series systems is important for the automatic control and optimi...
https://arxiv.org/abs/2601.07640
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55c79ababdd3d91e63d1c9ab3fed96e9a6a00a4d7cfa4e1c8309ddaf50153b20
2026-01-13T00:00:00-05:00
The Role of Confounders and Linearity in Ecological Inference: A Reassessment
arXiv:2601.07668v1 Announce Type: new Abstract: Estimating conditional means using only the marginal means available from aggregate data is commonly known as the ecological inference problem (EI). We provide a reassessment of EI, including a new formalization of identification conditions and a demonstration of how thes...
https://arxiv.org/abs/2601.07668
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ac25d9917d80099a9793d029bc1964e41784ce0dbd1cb473cc0df68da4b78630
2026-01-13T00:00:00-05:00
Cluster-based name embeddings reduce ethnic disparities in record linkage quality under realistic name corruption: evidence from the North Carolina Voter Registry
arXiv:2601.07693v1 Announce Type: new Abstract: Differential ethnic-based record linkage errors can bias epidemiologic estimates. Prior evidence often conflates heterogeneity in error mechanisms with unequal exposure to error. Using snapshots of the North Carolina Voter Registry (Oct 2011-Oct 2022), we derived empirica...
https://arxiv.org/abs/2601.07693
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287728d7d7b6598f3abd12f7ca2bee5630769979ad0f720bb9e8c007ba54c194
2026-01-13T00:00:00-05:00
Reinforcement Learning for Micro-Level Claims Reserving
arXiv:2601.07637v1 Announce Type: cross Abstract: Outstanding claim liabilities are revised repeatedly as claims develop, yet most modern reserving models are trained as one-shot predictors and typically learn only from settled claims. We formulate individual claims reserving as a claim-level Markov decision process in...
https://arxiv.org/abs/2601.07637
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6160995bfc566234925f78ceceba9b5a9f966e3c2d241ef6a565a531b11df0cc
2026-01-13T00:00:00-05:00
Physics-Informed Singular-Value Learning for Cross-Covariances Forecasting in Financial Markets
arXiv:2601.07687v1 Announce Type: cross Abstract: A new wave of work on covariance cleaning and nonlinear shrinkage has delivered asymptotically optimal analytical solutions for large covariance matrices. Building on this progress, these ideas have been generalized to empirical cross-covariance matrices, whose singular...
https://arxiv.org/abs/2601.07687
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de76722fe0b76fc14c4280a67c02f004d73ce9c41797f44b88c54a47900958fc
2026-01-13T00:00:00-05:00
Non-Convex Portfolio Optimization via Energy-Based Models: A Comparative Analysis Using the Thermodynamic HypergRaphical Model Library (THRML) for Index Tracking
arXiv:2601.07792v1 Announce Type: cross Abstract: Portfolio optimization under cardinality constraints transforms the classical Markowitz mean-variance problem from a convex quadratic problem into an NP-hard combinatorial optimization problem. This paper introduces a novel approach using THRML (Thermodynamic HypergRaph...
https://arxiv.org/abs/2601.07792
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705bb0a28be78e55f3abafd31d7a118db14371b66d352ce2d406c74bfe723da2
2026-01-13T00:00:00-05:00
Accumulation of Sub-Sampling Matrices with Applications to Statistical Computation
arXiv:2103.04031v2 Announce Type: replace Abstract: With appropriately chosen sampling probabilities, sampling-based random projection can be used to implement large-scale statistical methods, substantially reducing computational cost while maintaining low statistical error. However, computing optimal sampling probabil...
https://arxiv.org/abs/2103.04031
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20f8d2cb6e09378a6bdf5a47474f5d8a41764f73c1faa690a30f73bb4dd0de77
2026-01-13T00:00:00-05:00
Relaxed Gaussian process interpolation: a goal-oriented approach to Bayesian optimization
arXiv:2206.03034v4 Announce Type: replace Abstract: This work presents a new procedure for obtaining predictive distributions in the context of Gaussian process (GP) modeling, with a relaxation of the interpolation constraints outside ranges of interest: the mean of the predictive distributions no longer necessarily in...
https://arxiv.org/abs/2206.03034
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87ca7cad113528cb5d1631b504f27acfa11d21ffa6d27c354cfa8b03840c1b5b
2026-01-13T00:00:00-05:00
Experiment-selector cross-validated targeted maximum likelihood estimator for hybrid RCT-external data studies
arXiv:2210.05802v4 Announce Type: replace Abstract: Augmenting a randomized controlled trial (RCT) with external data may increase power at the risk of introducing bias. To select and analyze the experiment (RCT alone or combined with external data) with the optimal bias-variance tradeoff, we develop a novel experiment...
https://arxiv.org/abs/2210.05802
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8d48b7aea56b13826744ec300ebdc9b2b17c5bfb3b8a692c3671631aa33f4cba
2026-01-13T00:00:00-05:00
The Interpolating Information Criterion for Overparameterized Models
arXiv:2307.07785v2 Announce Type: replace Abstract: The problem of model selection is considered for the setting of interpolating estimators, where the number of model parameters exceeds the size of the dataset. Classical information criteria typically consider the large-data limit, penalizing model size. However, thes...
https://arxiv.org/abs/2307.07785
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70ff429a3355298d7703387871dccf269ce6683b3be71e0e923dce590f1df5eb
2026-01-13T00:00:00-05:00
A Convex Framework for Confounding Robust Inference
arXiv:2309.12450v3 Announce Type: replace Abstract: We study policy evaluation of offline contextual bandits subject to unobserved confounders. Sensitivity analysis methods are commonly used to estimate the policy value under the worst-case confounding over a given uncertainty set. However, existing work often resorts ...
https://arxiv.org/abs/2309.12450
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2202caf973b6b46f00403e3ec8ee957ccc238f0303e633b2ad160410733eb72d
2026-01-13T00:00:00-05:00
Expectile Periodograms
arXiv:2403.02060v4 Announce Type: replace Abstract: This paper introduces a novel periodogram-like function, called the expectile periodogram, for modeling spectral features of time series and detecting hidden periodicities. The expectile periodogram is constructed from trigonometric expectile regression, in which a sp...
https://arxiv.org/abs/2403.02060
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64f7675ee779a82c25e75a4d80d24e61de6e5f89a7f940e37df8137efbdb2315
2026-01-13T00:00:00-05:00
Data-Driven Strategies for Detecting and Sampling Misrepresented Subgroups
arXiv:2405.01342v2 Announce Type: replace Abstract: Economic policy research frequently examines population well-being, with a particular focus on the relationships between unequal living conditions, low educational attainment, and social exclusion. Sample surveys, such as EU-SILC, are widely used for this purpose and ...
https://arxiv.org/abs/2405.01342
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3abc34c92634777517ae6d1deb2ed7e5c4fd9b412920f06026140c6534260b24
2026-01-13T00:00:00-05:00
Iterative Methods for Full-Scale Gaussian Process Approximations for Large Spatial Data
arXiv:2405.14492v5 Announce Type: replace Abstract: Gaussian processes are flexible probabilistic regression models which are widely used in statistics and machine learning. However, a drawback is their limited scalability to large data sets. To alleviate this, full-scale approximations (FSAs) combine predictive proces...
https://arxiv.org/abs/2405.14492
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709f2018ee74475c50fff0a24453296eb762f7910bfee8eef8f881c21d9059e2
2026-01-13T00:00:00-05:00
Berezinskii--Kosterlitz--Thouless transition in a context-sensitive random language model
arXiv:2412.01212v2 Announce Type: replace Abstract: Several power-law critical properties involving different statistics in natural languages -- reminiscent of scaling properties of physical systems at or near phase transitions -- have been documented for decades. The recent rise of large language models has added furt...
https://arxiv.org/abs/2412.01212
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f380525f586684ac188c3653a17bc4feb85f3bf8c8697449324c654c07da660b
2026-01-13T00:00:00-05:00
Asymptotics of Non-Convex Generalized Linear Models in High-Dimensions: A proof of the replica formula
arXiv:2502.20003v2 Announce Type: replace Abstract: The analytic characterization of the high-dimensional behavior of optimization for Generalized Linear Models (GLMs) with Gaussian data has been a central focus in statistics and probability in recent years. While convex cases, such as the LASSO, ridge regression, and ...
https://arxiv.org/abs/2502.20003
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e339a8ed154bd0ee0011505adbe90a74810aca45dec74be8f97da3e8e52bb30e
2026-01-13T00:00:00-05:00
A Spatiotemporal, Quasi-experimental Causal Inference Approach to Characterize the Effects of Global Plastic Waste Export and Burning on Air Quality Using Remotely Sensed Data
arXiv:2503.04491v3 Announce Type: replace Abstract: Open burning of plastic waste may pose a significant threat to global health by degrading air quality, but quantitative research on this problem -- crucial for policy making -- has been stunted by lack of data. Many low- and middle-income countries, where open burning...
https://arxiv.org/abs/2503.04491
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7d3a01bb42183e51f26225be1178d449a74600b0be85ce67f4e2f5f7d6b1945f
2026-01-13T00:00:00-05:00
Simulation of Multivariate Extremes: a Wasserstein-Aitchison GAN approach
arXiv:2504.21438v3 Announce Type: replace Abstract: Economically responsible mitigation of multivariate extreme risks-such as extreme rainfall over large areas, large simultaneous variations in many stock prices, or widespread breakdowns in transportation systems-requires assessing the resilience of the systems under p...
https://arxiv.org/abs/2504.21438
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5a6190dcd3a6ba15947aaeaab095ebb32179ae46518f72820a6308a68e5c0be2
2026-01-13T00:00:00-05:00
Convergence Rates of Constrained Expected Improvement
arXiv:2505.11323v2 Announce Type: replace Abstract: Constrained Bayesian optimization (CBO) methods have seen significant success in black-box optimization with constraints. One of the most commonly used CBO methods is the constrained expected improvement (CEI) algorithm. CEI is a natural extension of expected improvem...
https://arxiv.org/abs/2505.11323
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782540336aed474bef10e9c0e52d639c3efa8ab77dbc688d602aeb45866bdc12
2026-01-13T00:00:00-05:00
Model-X Change-Point Detection of Conditional Distribution
arXiv:2505.12023v3 Announce Type: replace Abstract: The dynamic nature of many real-world systems can lead to temporal outcome model shifts, causing a deterioration in model accuracy and reliability over time. This requires change-point detection on the outcome models to guide model retraining and adjustments. However,...
https://arxiv.org/abs/2505.12023
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e4e120fd7710f43b2b9c8a0503169ce7c2bd0187a3ab43a774a625f3e9ec7b0c
2026-01-13T00:00:00-05:00
Testing for sufficient follow-up in cure models with categorical covariates
arXiv:2505.13128v2 Announce Type: replace Abstract: In survival analysis, estimating the fraction of 'immune' or 'cured' subjects who will never experience the event of interest, requires a sufficiently long follow-up period. A few statistical tests have been proposed to test the assumption of sufficient follow-up, i.e...
https://arxiv.org/abs/2505.13128
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cd99aab4d2ab71f4e0197537a0314b5072d2e8519651612d759eceae35994800
2026-01-13T00:00:00-05:00
Data-Adaptive Automatic Threshold Calibration for Stability Selection
arXiv:2505.22012v2 Announce Type: replace Abstract: Stability selection has gained popularity as a method for enhancing the performance of variable selection algorithms while controlling false discovery rates. However, achieving these desirable properties depends on correctly specifying the stable threshold parameter, ...
https://arxiv.org/abs/2505.22012
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a620a2b632d25361480ff3c0234ff967f14596b7b090a65503ccbad48ec92c82
2026-01-13T00:00:00-05:00
PCA-Guided Quantile Sampling: Preserving Data Structure in Large-Scale Subsampling
arXiv:2506.18249v2 Announce Type: replace Abstract: We introduce Principal Component Analysis guided Quantile Sampling (PCA QS), a novel sampling framework designed to preserve both the statistical and geometric structure of large scale datasets. Unlike conventional PCA, which reduces dimensionality at the cost of inte...
https://arxiv.org/abs/2506.18249
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761555f337378d67769f62749b3f799b89b7547a46b91e50284291cd51c65aca
2026-01-13T00:00:00-05:00
Stable Minima of ReLU Neural Networks Suffer from the Curse of Dimensionality: The Neural Shattering Phenomenon
arXiv:2506.20779v4 Announce Type: replace Abstract: We study the implicit bias of flatness / low (loss) curvature and its effects on generalization in two-layer overparameterized ReLU networks with multivariate inputs -- a problem well motivated by the minima stability and edge-of-stability phenomena in gradient-descen...
https://arxiv.org/abs/2506.20779
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378f84a7799b0d60ad0e4ac22e1a6020b0dba2c95fb7b167e1de0a9557ba22f1
2026-01-13T00:00:00-05:00
When Less Is More: Binary Feedback Can Outperform Ordinal Comparisons in Ranking Recovery
arXiv:2507.01613v4 Announce Type: replace Abstract: Paired comparison data, where users evaluate items in pairs, play a central role in ranking and preference learning tasks. While ordinal comparison data intuitively offer richer information than binary comparisons, this paper challenges that conventional wisdom. We pr...
https://arxiv.org/abs/2507.01613
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32d79a3a9f80c9aadace154024ce0709e47afe3c21456e73ab2ac216cc8bb8f1
2026-01-13T00:00:00-05:00
Bootstrapped Control Limits for Score-Based Concept Drift Control Charts
arXiv:2507.16749v2 Announce Type: replace Abstract: Monitoring for changes in a predictive relationship represented by a fitted supervised learning model (i.e., concept drift detection) is a widespread problem in modern data-driven applications. A general and powerful Fisher score-based concept drift approach was recen...
https://arxiv.org/abs/2507.16749
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59a0bed9581195feaec82badd92bcbe19c97fa49a22fc08d470c6851d6793191
2026-01-13T00:00:00-05:00
Bag of Coins: A Statistical Probe into Neural Confidence Structures
arXiv:2507.19774v2 Announce Type: replace Abstract: Modern neural networks often produce miscalibrated confidence scores and struggle to detect out-of-distribution (OOD) inputs, while most existing methods post-process outputs without testing internal consistency. We introduce the Bag-of-Coins (BoC) probe, a non-parame...
https://arxiv.org/abs/2507.19774
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da7a8fa61fafc53953473bf7e14c5887e4c5782b7496e0c5b598ab9ec4d1b9df
2026-01-13T00:00:00-05:00
From Sublinear to Linear: Fast Convergence in Deep Networks via Locally Polyak-Lojasiewicz Regions
arXiv:2507.21429v2 Announce Type: replace Abstract: Gradient descent (GD) on deep neural network loss landscapes is non-convex, yet often converges far faster in practice than classical guarantees suggest. Prior work shows that within locally quasi-convex regions (LQCRs), GD converges to stationary points at sublinear ...
https://arxiv.org/abs/2507.21429
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47d20798b6b52f167c0c4eb47e20320f00c4bb23be439e9d79e129411edebb5d
2026-01-13T00:00:00-05:00
Bayesian inference of antibody evolutionary dynamics using multitype branching processes
arXiv:2508.09519v2 Announce Type: replace Abstract: When our immune system encounters foreign antigens (i.e., from pathogens), the B cells that produce our antibodies undergo a cyclic process of proliferation, mutation, and selection, improving their ability to bind to the specific antigen. Immunologists have recently ...
https://arxiv.org/abs/2508.09519
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68718d0e11429812d43675abcfebb914b5a98f6ebbe4f5b86a9e724d203213e1
2026-01-13T00:00:00-05:00
Precision Dose-Finding Design for Phase I Oncology Trials by Integrating Pharmacology Data
arXiv:2509.05120v2 Announce Type: replace Abstract: Phase I oncology trials aim to identify a safe dose - often the maximum tolerated dose (MTD) - for subsequent studies. Conventional designs focus on population-level toxicity modeling, with recent attention on leveraging pharmacokinetic (PK) data to improve dose selec...
https://arxiv.org/abs/2509.05120
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40b4feaa614d707800382efa6f68ad31dd96b9530a8595bb7efb968eb484e27e
2026-01-13T00:00:00-05:00
Copula-Stein Discrepancy: A Generator-Based Stein Operator for Archimedean Dependence
arXiv:2510.24056v2 Announce Type: replace Abstract: Kernel Stein discrepancies (KSDs) are widely used for goodness-of-fit testing, but standard KSDs can be insensitive to higher-order dependence features such as tail dependence. We introduce the Copula-Stein Discrepancy (CSD), which defines a Stein operator directly on...
https://arxiv.org/abs/2510.24056
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c30902bd9f0af1cb3e941905496e90dd4343e931b905a2ea58fd8e57804b8456
2026-01-13T00:00:00-05:00
Discretization approximation: An alternative to Monte Carlo in Bayesian computation
arXiv:2512.11475v2 Announce Type: replace Abstract: In this paper we propose a new deterministic approximation method, called discretization approximation, for Bayesian computation. Discretization approximation is very simple to understand and to implement, It only requires calculating posterior density values as proba...
https://arxiv.org/abs/2512.11475
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4c4c62f7a785e57e67286468d04d148cd39435060f06e4586a9b729fd0bb0782
2026-01-13T00:00:00-05:00
A Concentration Bound for TD(0) with Function Approximation
arXiv:2312.10424v4 Announce Type: replace-cross Abstract: We derive uniform all-time concentration bound of the type 'for all $n \geq n_0$ for some $n_0$' for TD(0) with linear function approximation. We work with online TD learning with samples from a single sample path of the underlying Markov chain. This makes our a...
https://arxiv.org/abs/2312.10424
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e28faff5eb4189b2b19f63ff56e8ac2c4da202c7886b684a40396f583feecb43
2026-01-13T00:00:00-05:00
Reimagining Anomalies: What If Anomalies Were Normal?
arXiv:2402.14469v2 Announce Type: replace-cross Abstract: Deep learning-based methods have achieved a breakthrough in image anomaly detection, but their complexity introduces a considerable challenge to understanding why an instance is predicted to be anomalous. We introduce a novel explanation method that generates mu...
https://arxiv.org/abs/2402.14469
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2fac5c4edcb0442a4c39ac58322daf8667a482fe11f90f5a14bd3d81e96070ad
2026-01-13T00:00:00-05:00
Data-Driven Knowledge Transfer in Batch $Q^*$ Learning
arXiv:2404.15209v3 Announce Type: replace-cross Abstract: In data-driven decision-making in marketing, healthcare, and education, it is desirable to utilize a large amount of data from existing ventures to navigate high-dimensional feature spaces and address data scarcity in new ventures. We explore knowledge transfer ...
https://arxiv.org/abs/2404.15209
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198cd83090655e6118838e24e1ef7b5b9d6b9d0491655da1e9458b55db8f5052
2026-01-13T00:00:00-05:00
Low-Rank Online Dynamic Assortment with Dual Contextual Information
arXiv:2404.17592v2 Announce Type: replace-cross Abstract: As e-commerce expands, delivering real-time personalized recommendations from vast catalogs poses a critical challenge for retail platforms. Maximizing revenue requires careful consideration of both individual customer characteristics and available item features...
https://arxiv.org/abs/2404.17592
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8f85745557598a07814e3403f4d8a6dbfb7cd216f1b046ddd7364af1bbc90f4c
2026-01-13T00:00:00-05:00
Model Privacy: A Unified Framework to Understand Model Stealing Attacks and Defenses
arXiv:2502.15567v2 Announce Type: replace-cross Abstract: The use of machine learning (ML) has become increasingly prevalent in various domains, highlighting the importance of understanding and ensuring its safety. One pressing concern is the vulnerability of ML applications to model stealing attacks. These attacks inv...
https://arxiv.org/abs/2502.15567
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f34c229beaa378af20132d181959e35731df1bb16ac61184ceeaa32f7153b85a
2026-01-13T00:00:00-05:00
The Power of Iterative Filtering for Supervised Learning with (Heavy) Contamination
arXiv:2505.20177v2 Announce Type: replace-cross Abstract: Inspired by recent work on learning with distribution shift, we give a general outlier removal algorithm called iterative polynomial filtering and show a number of striking applications for supervised learning with contamination: (1) We show that any function cl...
https://arxiv.org/abs/2505.20177
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492db51240ec75c238ec129fc5d08881f2f983b6c45dd0e6799b06c191131544
2026-01-13T00:00:00-05:00
ORACLE: Explaining Feature Interactions in Neural Networks with ANOVA
arXiv:2509.10825v4 Announce Type: replace-cross Abstract: We introduce ORACLE, a framework for explaining neural networks on tabular data and scientific factorial designs. ORACLE summarizes a trained network's prediction surface with main effects and pairwise interactions by treating the network as a black-box response...
https://arxiv.org/abs/2509.10825
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4c62708432c8216ef314e9650c465010f4976d881efcd61e4c850dbbe81812b3
2026-01-13T00:00:00-05:00
Wide Neural Networks as a Baseline for the Computational No-Coincidence Conjecture
arXiv:2510.06527v2 Announce Type: replace-cross Abstract: We establish that randomly initialized neural networks, with large width and a natural choice of hyperparameters, have nearly independent outputs exactly when their activation function is nonlinear with zero mean under the Gaussian measure: $\mathbb{E}_{z \sim \...
https://arxiv.org/abs/2510.06527
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e4059efb0e57de3c553439c6863e631ec3479f26c5e080773ba8f98ea2213f3e
2026-01-13T00:00:00-05:00
SAVeD: Semantic Aware Version Discovery
arXiv:2511.17298v2 Announce Type: replace-cross Abstract: Our work introduces SAVeD (Semantically Aware Version Detection), a contrastive learning-based framework for identifying versions of structured datasets without relying on metadata, labels, or integration-based assumptions. SAVeD addresses a common challenge in ...
https://arxiv.org/abs/2511.17298
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d04da4cd46056f8eb778cc16e5c7b31069c45e0b73c8592387a540cfa6ee6c3d
2026-01-13T00:00:00-05:00
A Regime-Aware Fusion Framework for Time Series Classification
arXiv:2512.15378v2 Announce Type: replace-cross Abstract: Kernel-based methods such as Rocket are among the most effective default approaches for univariate time series classification (TSC), yet they do not perform equally well across all datasets. We revisit the long-standing intuition that different representations c...
https://arxiv.org/abs/2512.15378
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e6cda8b017cc05c2a5d19acf52807d6d66ac2a3ac9684b90c59cc84cfe0839e7
2026-01-13T00:00:00-05:00
The Qutrit Bloch Sphere
arXiv:2601.06240v1 Announce Type: new Abstract: It is very important to understand if a qutrit can be visualized in a 3-dimensional Bloch sphere. In this work, a mathematical model for performing this operation is presented.
https://arxiv.org/abs/2601.06240
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043bf92814358e332f751ee08044f7c9d3f41de6406d5dcfaf431b6e4d11d1c5
2026-01-13T00:00:00-05:00
Universal Predictors for Mixing Time more than Liouvillian Gap
arXiv:2601.06256v1 Announce Type: new Abstract: We analyze the mixing time of open quantum systems governed by the Lindblad master equation, showing it is not only determined by the Liouvillian gap, but also the trace norm of the lowest excited state of Liouvillian superoperator. By utilizing these universal predictors...
https://arxiv.org/abs/2601.06256
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0ac04c8fc8dc54a0a481ca90f74f96c610a3f867e204927ca6b4eb450819cd29
2026-01-13T00:00:00-05:00
Latent splitting as a causal probe
arXiv:2601.06265v1 Announce Type: new Abstract: Generalizations of Bell's framework to causal networks have yielded new foundational insights and applications, including the use of interventions to enhance the detection of nonclassicality in scenarios with communication. Such interventions, however, become uninformativ...
https://arxiv.org/abs/2601.06265
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0f1e34f7f02a88ab3836a32dbc0475199508102d2fc4f3133aa0fca757eccb87
2026-01-13T00:00:00-05:00
Quantum algorithm for dephasing of coupled systems: decoupling and IQP duality
arXiv:2601.06298v1 Announce Type: new Abstract: Noise and decoherence are ubiquitous in the dynamics of quantum systems coupled to an external environment. In the regime where environmental correlations decay rapidly, the evolution of a subsytem is well described by a Lindblad quantum master equation. In this work, we ...
https://arxiv.org/abs/2601.06298
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18fafd03bf78750d3bd0c136dd38dc26325c197f374e7c314d511a8682cc732b
2026-01-13T00:00:00-05:00
The pros and cons of using deep reinforcement learning or genetic algorithms to design control schemes for quantum state transfer on qubit chains
arXiv:2601.06303v1 Announce Type: new Abstract: In recent years, control methods based on different optimization techniques have shed light on the possibilities of processing information in many quantum systems. When exploring the transmission of quantum states, faster transmission times are mandatory to avoid the dele...
https://arxiv.org/abs/2601.06303
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b16a11ad3a404ac8d652cb5d179843c2667b630716d6c01d09efd813fb289399
2026-01-13T00:00:00-05:00
Informationally Complete Distributed Metrology Without a Shared Reference Frame
arXiv:2601.06393v1 Announce Type: new Abstract: In quantum information processing, implementing arbitrary preparations and measurements on qubits necessitates precise information to identify a specific reference frame (RF). In space quantum communication and sensing, where a shared RF is absent, the interplay between l...
https://arxiv.org/abs/2601.06393
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51450995a89d12a1b55a79f90ced57553c6b8d8fdeab10c258b3b7cb3519165e
2026-01-13T00:00:00-05:00
Restoring Locality: The Heisenberg Picture as a Separable Description of Quantum Theory
arXiv:2601.06522v1 Announce Type: new Abstract: Local realism has been the subject of much discussion in modern physics, partly because our deepest theories of physics appear to contradict one another in regard to whether reality is local. According to general relativity, it is, as physical quantities (perceptible or n...
https://arxiv.org/abs/2601.06522
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e2bd41a43ead5bb64f98d583307d3763539e21c4eebacab03ea240e0155a528f
2026-01-13T00:00:00-05:00
Digital Predistortion of Power Amplifiers for Quantum Computing
arXiv:2601.06524v1 Announce Type: new Abstract: Power amplifiers (PA) are essential for microwavecontrolled trapped-ion and semiconductor spin based quantum computers (QC). They adjust the power level of the control signal and therefore the processing time of the QC. Their nonlinearities and memory effects degrade the ...
https://arxiv.org/abs/2601.06524
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d7e49d18f3ee4bbb975edd8140c25ab207236a65087ef44402a80f3c24e1c72a
2026-01-13T00:00:00-05:00
Magnetic levitation and spatial superposition of a nanodiamond with a current-carrying chip
arXiv:2601.06608v1 Announce Type: new Abstract: We propose a current-carrying-chip scheme for generating spatial quantum superpositions using a levitating nanodiamond with a built-in nitrogen-vacancy (NV) centre defect. Our setup is quite versatile and we aim to create the superposition for a mass range of $10^{-19}~{\...
https://arxiv.org/abs/2601.06608
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d0c4e27549ef3c510e02ffc8625d8514ca655ef61e0323ff755faca80c1e9a9b
2026-01-13T00:00:00-05:00
Rydberg atom parity gate based on dark state resonances
arXiv:2601.06665v1 Announce Type: new Abstract: Quantum computation (QC) and digital quantum simulation (DQS) essentially require two- or multi-qubit controlled-NOT or -phase gates. We propose an alternative pathway for QC and DQS using a three-qubit parity gate in a Rydberg atom array. The basic principle of the Rydbe...
https://arxiv.org/abs/2601.06665
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1de61122ac4e592a24ea007972f421fcafad11b38f3b2ebd41e8c61b48a6a5cc
2026-01-13T00:00:00-05:00
A paradigm for universal quantum information processing with integrated acousto-optic frequency beamsplitters
arXiv:2601.06752v1 Announce Type: new Abstract: Frequency-bin encoding offers tremendous potential in quantum photonic information processing, in which a single waveguide can support hundreds of lightpaths in a naturally phase-stable fashion. This stability, however, comes at a cost: arbitrary unitary operations can be...
https://arxiv.org/abs/2601.06752
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b99df70bf023a471d4e4aaebb08fc933ddf13c8362abe5de6189b631b8a9ab97
2026-01-13T00:00:00-05:00
Noise-Resistant Feature-Aware Attack Detection Using Quantum Machine Learning
arXiv:2601.06762v1 Announce Type: new Abstract: Continuous-variable quantum key distribution (CV-QKD) is a quantum communication technology that offers an unconditional security guarantee. However, the practical deployment of CV-QKD systems remains vulnerable to various quantum attacks. In this paper, we propose a quan...
https://arxiv.org/abs/2601.06762
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70301e4793577467bc1fb2313f4ec6b6f56ba0104b5eac7fc284776a6846d380
2026-01-13T00:00:00-05:00
Experimental Coherent One-Way Quantum Key Distribution with Simplicity and Practical Security
arXiv:2601.06772v1 Announce Type: new Abstract: Coherent one-way quantum key distribution (COW-QKD) has been widely investigated, and even been deployed in real-world quantum network. However, the proposal of the zero-error attack has critically undermined its security guarantees, and existing experimental implementati...
https://arxiv.org/abs/2601.06772
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4e2d1c7f9b53ecda88cb907170cecdeff0f14ba63dfebeab352e06e8b50b244e
2026-01-13T00:00:00-05:00
Geometric and Operational Characterization of Two-Qutrit Entanglement
arXiv:2601.06783v1 Announce Type: new Abstract: We investigate the entanglement structure of bipartite two-qutrit pure states from both geometric and operational perspectives.Using the eigenvalues of the reduced density matrix, we analyze how symmetric polynomials characterize pairwise and genuinely three-level correla...
https://arxiv.org/abs/2601.06783
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77029c64afec7fdd17a569d55e45b2a6462070b9f4da046becc9a75328ce3114
2026-01-13T00:00:00-05:00
Cancelling second order frequency shifts in Ge hole spin qubits via bichromatic control
arXiv:2601.06805v1 Announce Type: new Abstract: Germanium quantum dot hole spin qubits are compatible with fully electrical control and are progressing toward multi-qubit operations. However, their coherence is limited by charge noise and driving field induced frequency shifts, and the resulting ensemble $1/f$ dephasin...
https://arxiv.org/abs/2601.06805
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16ffa89b6f66688ae625314b5a811e81cb60886251cadcaa21e36347118d613f
2026-01-13T00:00:00-05:00
Axion Signal Search Using Hybrid Nuclear-Electronic Spin Systems
arXiv:2601.06816v1 Announce Type: new Abstract: Conventional nuclear magnetic resonance searches for the galactic axion wind lose sensitivity at low frequencies due to the unfavourable scaling of inductive readout. Here, we propose a hybrid architecture where the hyperfine interaction transduces axion-driven nuclear pr...
https://arxiv.org/abs/2601.06816
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7845c13bfce3b81ffb60e9e057d98f9561eaacbd8a51d5cdfb9c84855c16c9e7
2026-01-13T00:00:00-05:00
Quantum Circuit-Based Adaptation for Credit Risk Analysis
arXiv:2601.06865v1 Announce Type: new Abstract: Noisy and Intermediate-Scale Quantum, or NISQ, processors are sensitive to noise, prone to quantum decoherence, and are not yet capable of continuous quantum error correction for fault-tolerant quantum computation. Hence, quantum algorithms designed in the pre-fault-toler...
https://arxiv.org/abs/2601.06865
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c4715353e08a23fc2e2f098f496c1b6b899e1bee18742c9d1f9f44605098853e
2026-01-13T00:00:00-05:00
High capacity dual degrees of freedom quantum secret sharing protocol beyond the linear rate-distance bound
arXiv:2601.06919v1 Announce Type: new Abstract: Quantum secret sharing (QSS) is the multipartite cryptographic primitive. Most of existing QSS protocols are limited by the linear rate-distance bound, and cannot realize the long-distance and high-capacity multipartite key distribution. This paper proposes a polarization...
https://arxiv.org/abs/2601.06919
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74b951de757344bfb89b9e53813c389aae84dc1e8a002a1fbd71f6eb098a4a69
2026-01-13T00:00:00-05:00
Extending the Handover-Iterative VQE to Challenging Strongly Correlated Systems: $N_2$ and Fe-S Cluster
arXiv:2601.06935v1 Announce Type: new Abstract: Accurately describing strongly correlated electronic systems remains a central challenge in quantum chemistry, as electron-electron interactions give rise to complex many-body wavefunctions that are difficult to capture with conventional approximations. Classical wavefunc...
https://arxiv.org/abs/2601.06935
Academic Papers
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1ddb57a69872eb03a727b325d8567333f77ecd8ed7e197bb3113efac9be8f252
2026-01-13T00:00:00-05:00
Dynamical Correlation of the Post-quench Non-thermal Equilibrium State
arXiv:2601.06987v1 Announce Type: new Abstract: After a quantum quench, the integrable system is expected to relax to a non-thermal equilibrium state (NTES) whose local properties are believed to be governed by a generalized Gibbs ensemble (GGE). Combining quench action and the form factor approach, we compute the fiel...
https://arxiv.org/abs/2601.06987
Academic Papers
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dc360f31df7352def817e72a216e622e96636f3837716207c935ff7c26b58784
2026-01-13T00:00:00-05:00
Counter-diabatic driving for fast spin control in a two-electron double quantum dot
arXiv:2601.06988v1 Announce Type: new Abstract: The techniques of shortcuts to adiabaticity have been proposed to accelerate the "slow" adiabatic processes in various quantum systems with the applications in quantum information processing. In this paper, we study the counter-diabatic driving for fast adiabatic spin man...
https://arxiv.org/abs/2601.06988
Academic Papers
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66497b0d9bf4148c941382f4ad3ae6f73ac16583e949b17485908abe99ba7963
2026-01-13T00:00:00-05:00
Quantum state engineering of spin-orbit coupled ultracold atoms in a Morse potential
arXiv:2601.06996v1 Announce Type: new Abstract: Achieving full control of a Bose-Einstein condensate can have valuable applications in metrology, quantum information processing, and quantum condensed matter physics. We propose protocols to simultaneously control the internal (related to its pseudospin-1/2) and motional...
https://arxiv.org/abs/2601.06996
Academic Papers
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