Statistical Methods and Inference

Derniers papiers

• Deep Multitask Learning for Mixed-Type Outcomes with Shared Sparsity

  Huichao Li, Tong Wang, Sanguo Zhang, Shuangge Ma · 2 juillet 2026

  Most existing multitask learning approaches are limited by their reliance on task-specific loss functions tailored to the scale and type of each outcome. When outcomes differ across tasks, these losses are generally not directly comparable, which makes it difficult to formulate a unified objective a…

• Conformalized Regression for Continuous Bounded Outcomes

  Zhanli Wu, Fabrizio Leisen, F. Javier Rubio · 1 juillet 2026

  Regression problems with bounded continuous outcomes frequently arise in statistical and machine learning applications, such as the analysis of rates and proportions. A central challenge in this setting is predicting the response at a new covariate value. Most of the existing literature has focused …

• Doubly Robust Adaptive Conformal Inference for Causal Effects Under Temporal Dependence

  Andreas Koukorinis, Ricardo Silva · 30 juin 2026

  We propose doubly robust adaptive conformal inference (DR-ACI), which constructs prediction intervals for doubly robust pseudo-outcomes under temporal dependence.…

• Overcoming Dependent Censoring in the Evaluation of Survival Models

  Christian Marius Lillelund, Shi-ang Qi, Russell Greiner · 30 juin 2026

  Dependent censoring occurs when the event time and censoring time are not conditionally independent given the observed covariates. This complicates survival model evaluation because widely used metrics, such as the Brier score, typically handle right-censoring using inverse probability of censoring …

• Towards Reliable Recommender Systems for Rating Data

  Aurore Archimbaud, Andreas Alfons, Ines Wilms · 29 juin 2026

  Recommender systems are widely used in the digital landscape to match users with content fitting their preferences. However, growing concerns about fake accounts, strategic manipulation, and other deceptive online behavior place increasing pressure on the reliability of these systems. A common stati…

• Conformal and kNN Predictive Uncertainty Quantification Algorithms in Metric Spaces

  G\'abor Lugosi, Marcos Matabuena · 23 juin 2026

  This paper introduces a framework for uncertainty quantification in regression models defined on metric spaces. Using a proposed notion of homoscedasticity, we define a conformal prediction algorithm that provides finite-sample marginal coverage guarantees and fast convergence rates to the oracle pr…

• Subsampling for supervised learning in reproducing kernel Hilbert spaces

  Eyal Vayness, Maxime Sangnier · 23 juin 2026

  In the era of big data, subsampling became a common practice in statistical learning. By selecting a subgroup of individuals based on which the learner is trained, subsampling aims at reducing the computational cost and time of the estimation step, and ideally leads to a decrease of its energy consu…

• High-Dimensional Differentially Private Quantile Regression: Distributed Estimation and Statistical Inference

  Ziliang Shen, Caixing Wang, Shaoli Wang, Yibo Yan · 23 juin 2026

  With the development of big data and machine learning, privacy concerns have become increasingly critical, especially when handling heterogeneous datasets containing sensitive personal information. Differential privacy provides a rigorous framework for safeguarding individual privacy while enabling …

• Finite-Sample Performance of Gradient Descent in Logistic Regression with Gaussian Design

  Junren Chen, Arya Mazumdar · 23 juin 2026

  We consider the parameter estimation problem in logistic regression with Gaussian design: the estimation of a fixed unknown parameter $\theta^*\in \mathbb{R}^d$ ($\|\theta^*\|_2\ge 1$) from $n$ i.i.d. samples $\{(x_i,y_i)\}_{i=1}^n$, where $x_i\sim N(0,I_d)$ and $y_i|x_i \sim {\rm Bernoulli}(1/(1+\e…

• Generalized nonparametric regression in reproducing kernel Hilbert spaces: Consistency and rates of convergence

  Ioannis Kalogridis · 23 juin 2026

  We develop a comprehensive theory for regularized M-estimation in reproducing kernel Hilbert spaces. Under mild conditions on the loss we establish existence and measurability of the estimator, covering a wide range of convex and non-convex losses, including bounded robust losses. We further prove s…

• On the Variance of Temporal Difference Learning and its Reduction Using Control Variates

  Hsiao-Ru Pan, Bernhard Sch\"olkopf · 19 juin 2026

  We analyze the variance of temporal difference (TD) learning using the phased setting with tabular representation, and show that one of the mechanisms behind its ability to reduce variance is by effectively aggregating over a larger number of independent trajectories. Based on this insight, we demon…

• Discovering Subgroups with Exceptional Survival Characteristics

  Mhd Jawad Al Rahwanji, Sascha Xu, Nils Philipp Walter, Jilles Vreeken · 16 juin 2026

  In many applications, it is important to identify subpopulations that survive longer or shorter than the rest of the population. In medicine, for example, it allows determining which patients benefit from treatment, and in predictive maintenance, which components are more likely to fail. Existing me…

• Spectral Adaptive Conformal Prediction for Structured Non-Exchangeable Data

  Jeffery Opoku, David Banahene · 16 juin 2026

  Conformal prediction gives prediction intervals with finite-sample coverage when the data are exchangeable. Many time-indexed datasets are not exchangeable. They have seasons, recurring regimes, changing frequencies, or other forms of structured dependence. This paper studies a simple way to use tha…

• Anytime-Valid Confirmation of Label-Shift Corrections

  Seungjin Choi · 15 juin 2026

  In small-batch scientific deployments, labeled target outcomes may be too scarce for reliable shift estimation even when unlabeled target inputs are available. We address the complementary setting where the practitioner has a pre-specified label-shift correction from domain knowledge and asks whethe…

• Bidirectional Random Projections

  Chao Lan, Luyuan Yang · 10 juin 2026

  This paper analyzes bidirectional random projections for ordinary least squares (OLS) regression under the fixed design setting. Let $(X,Y) \in \mathbb{R}^{n \times p} \times \mathbb{R}^n$ be a sample and $R \in \mathbb{R}^{n_1 \times n}, W \in \mathbb{R}^{p \times p_1}$ be two properly distributed …

• Generalized Conformal Predictive Systems Under Distributional Shifts

  Jef Jonkers, Johanna Ziegel · 10 juin 2026

  Conformal predictive systems (CPS) output calibrated bands of CDFs under exchangeability. We extend generalized CPS to non-exchangeable settings by encoding distributional shifts through observation-specific permutation weights. This yields shift-aware predictive systems that remain valid whenever t…

• Convergence Rates for Neural-Network Estimation with Current-Status Data

  Yuan Wu, Tianhui Zhou · 10 juin 2026

  Current-status data arise when an event time is observed only through an indicator of whether it occurred before an examination time. This paper studies a nonparametric neural-network sieve maximum likelihood estimator of the conditional cumulative distribution function of the event time. Under H\"o…

• Optimal and Provable Calibration in High-Dimensional Binary Classification: Angular Calibration and Platt Scaling

  Yufan Li, Pragya Sur · 9 juin 2026

  We study the fundamental problem of calibrating a linear binary classifier of the form $\sigma(\hat{w}^\top x)$, where the feature vector $x$ is Gaussian, $\sigma$ is a link function, and $\hat{w}$ is an estimator of the true linear weight $w^\star$. By interpolating with a noninformative $\textit{c…

• Data augmented bootstrap: Unifying confidence interval construction by approximate invariance

  Kevin Han Huang · 9 juin 2026

  We propose the data augmented bootstrap (DAB), a framework for constructing confidence intervals from approximately invariant transformations of the data. As special cases, DAB recovers popular methods that rely on exact group symmetries, such as conformal prediction, wild bootstrap for Maximum Mean…

• Differentially Private Joint Independence Test

  Xingwei Liu, Yuexin Chen, Jin-Ting Zhang, Wangli Xu · 4 juin 2026

  Identification of joint dependence among several random vectors plays an important role in many statistical applications, where the data may contain sensitive or confidential information. In this paper, we consider the $d$-variable Hilbert-Schmidt independence criterion (dHSIC) in the context of dif…

• Set-Preserving Calibration from Conformal P-Values to E-Values

  Nabil Alami, Jad Zakharia, Souhaib Ben Taieb · 3 juin 2026

  Standard conformal prediction (CP) procedures are typically formulated in terms of p-values, but reliance on p-values alone limits flexibility, for example, when combining dependent evidence across models or data splits. Recent work has explored e-value formulations for conformal inference, yet a di…

• Cellwise and Casewise Robust Covariance in High Dimensions

  Fabio Centofanti, Mia Hubert, Peter J. Rousseeuw · 2 juin 2026

  The sample covariance matrix is a cornerstone of multivariate statistics, but it is highly sensitive to outliers. These can be casewise outliers, such as cases belonging to a different population, or cellwise outliers, which are deviating cells (entries) of the data matrix. Recently some robust cova…

• Optimal Regularization for Performative Learning

  Edwige Cyffers, Alireza Mirrokni, Marco Mondelli · 2 juin 2026

  In performative learning, the data distribution reacts to the deployed model - for example, because strategic users adapt their features to game it - which creates a more complex dynamic than in classical supervised learning. One should thus not only optimize the model for the current data but also …

• Dynamical local Fr\'echet curve regression in manifolds

  M. D. Ruiz-Medina, A. Torres-Signes · 1 juin 2026

  Under mild conditions, this paper derives a least-squares local linear Fr\'echet curve predictor for response and regressor evaluated in a separable Hilbert space. We obtain the conditions allowing the implementation of this local linear Fr\'echet functional predictor in the ambient L^{2}-space of v…

• Learning with Importance Weighted Variational Inference

  Kam\'elia Daudel, Fran\c{c}ois Roueff · 28 mai 2026

  Several variational bounds involving importance weighting ideas generalize the Evidence Lower BOund (ELBO) for marginal likelihood optimization, such as the Importance-weighted Auto-Encoder (IWAE), Variational R\'enyi (VR) and VR-IWAE bounds. Yet, it remains unclear how the joint choice of bound and…