「regression」の検索結果
68 件Deployed language models are updated continually, and regulators increasingly accept changes that were declare
We study the minimization of the average of $n$ convex $(L_0,L_1)$-smooth functions: the Hessian norm of each
Temporal-difference (TD) learning trains neural representations using bootstrapped targets generated by evolvi
Under temporal nonstationarity, model performance rankings reverse across training horizons due to heterogeneo
Protein structure predictors reliably identify native conformations, but do not describe the continuum connect
Can an LLM directly edit the weights of a diffusion-based image-restoration model to \emph{improve} restoratio
Prior-data fitted networks are typically trained using synthetic data generated by a single, manually construc
Quantum reservoir computing (QRC) processes temporal information through quantum dynamics, but it remains uncl
Hyperparameter optimization (HPO) returns a single optimal configuration, systematically discarding the explor
Identifying faulty components from sensor anomalies is difficult because fault effects can propagate through a
We consider nonparametric maximum likelihood estimation of the intensity function of a Poisson process, allowi
For effective resource management, infrastructure planning, and sustainable tourism, it is really important to
Suicide prevention remains a critical public health challenge, and electronic health records (EHRs) provide an
Laminar-turbulent transition governs skin friction, heat transfer and losses in turbomachinery, yet Reynolds-a
The Poll Schumann model requires airframe and engine parameters, but published tables cover only part of the f
Best subset selection is a central problem in statistical regression, drawing increasing attention from the br
Bayesian online changepoint detection provides a natural framework for quantifying uncertainty about whether a
Overall and group-level averages, and ranking metrics such as the AUC, can hide sets of observations for which
Increasingly complex neural models predict blood-oxygen-level-dependent (BOLD) responses to naturalistic stimu
The scarcity of human-annotated literary corpora limits the development of domain-specific sentiment analysis
Feature matching underpins many robotic perception tasks, including visual localization, mapping, SLAM, and au
Video dubbing aims to synthesize lip movements that match target speech while preserving the speaker identity,
Monocular head pose estimation is usually formulated as absolute regression from a single face crop, yet many
Among visual localization strategies, scene coordinate regression (SCR) has emerged as an attractive approach
Understanding how cells respond to perturbations over time is central to cell biology and drug discovery. Live
Control barrier functions (CBFs) are a standard way to keep a planner inside a safe set, but they assume contr
Spectral shrinkage encompasses a broad family of estimators for high-dimensional linear regression that regula
Convex regression provides a flexible nonparametric estimation framework while incorporating appropriate shape
Training with chain-of-thought (CoT) supervision is an effective approach for teaching models multi-step reaso
We study nonparametric regression using an over-parameterized two-layer ReLU neural network with fixed covaria
Selective prediction with split conformal calibration promises a post-hoc fix for molecular property predictio
Uncertainty estimates from fuzzy regression are informative only when the implementation is correct and the co
Verifying that a packaged food is Halal requires reading ingredient declarations and E-number additives before
Concept-based explanations of melanoma classifiers are rarely tested against controls with known targets. We b
Recent advances in generative AI have enabled multiple pathways for high-fidelity video synthesis: text-to-vid
Robotic surface exploration benefits from tactile interfaces that accommodate contact and capture variations i
The Lane-Emden Equation is a class of nonlinear differential equations commonly used to model phenomena in ast
Surprisal correlates closely with reading time and is widely used in the cognitive science of reading as a mea
End-to-end planners decode the ego trajectory either by coordinate regression, which is fast but blends plausi
The absolute trajectory error (ATE) is the standard measure of camera-motion accuracy, from SLAM to feed-forwa
Phase I dose-finding trials must learn from sparse toxicity observations while limiting exposure to overly tox
Estimating the causal effect of a treatment $X$ on an outcome $Y$ from observational data in the presence of u
Concept erasure is a prominent approach to achieving fairness in machine learning through the removal of sensi
Distributionally robust optimization (DRO) minimizes worst-case risk under distributional uncertainty, but its
We study the non-asymptotic convergence of Stochastic Mirror Descent (SMD) algorithm when the noisy first-orde
Autonomous systems that navigate their environment need methods to quickly assess the feasibility and safety o
Robot policy learning is dominated by zeroth-order methods such as PPO, which treat the simulator as a black b
Modern data analysis pipelines increasingly rely on large datasets assembled through retrieval, scraping, auto
We study high-dimensional nonparametric regression under multisource covariate shift, where $\mathrm J$ source
Synthetic data are widely used for training modern AI systems. However, their statistical understanding remain
Recent advances in Time Series Foundation Models (TSFMs) increasingly rely on axial attention mechanisms, but
Tabular foundation models use in-context learning to estimate conditional expected outcomes and average treatm
Parameter estimation and classification in binary logistic regression deteriorate severely when the number of
Learning a shared subspace in multitask learning is an important statistical problem, yet exact rates remain p
Kernel methods face two recurrent bottlenecks: the cubic cost of inverting an $n\times n$ kernel matrix, as in
Ship-to-home fulfillment requires a retailer to select a fulfillment location and carrier before the delivery
We study conformal prediction under covariate shift with a known covariate density ratio, seeking prediction s
Deep neural networks learn data-dependent features, a capacity widely credited for their empirical success. Ho
Artificial intelligence offers statisticians a new paradigm for modelling and analysing complex datasets. Agen
Language model performance depends strongly on the mixture of domains in the pretraining data corpus. Popular
In this paper, we introduce a model-based boosting approach for fitting structured additive distributional cop
Prediction models degrade when the deployment population differs from the one used to train them, and training
Contextual dueling bandits learn from pairwise comparisons, the same feedback used to fine-tune language model
Visual generation requires both high-level semantic knowledge and fine-grained pixel details. We propose a lea
Neural architecture search (NAS) is challenging because of the vast, non-linear space of artificial neural net
Clinical registries and wearable sensors support early health-risk assessment, yet models trained on these rec
Process-induced warpage, ceramic stress and solder strain limit the reliability of insulated-gate bipolar tran
Intrinsic explainability remains a challenging problem, particularly in contexts where multilayer perceptrons