:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Białek, Jakub, Kivimäki, Juhani, Kuberski, Wojtek, Perrakis, Nikolaos
Format:	Preprint
Published:	2024
Subjects:	Machine Learning 62G05
Online Access:	https://arxiv.org/abs/2401.08348
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Confidence-based Estimators for Predictive Performance in Model Monitoring
by: Kivimäki, Juhani, et al.
Published: (2024)

Performance Estimation in Binary Classification Using Calibrated Confidence
by: Kivimäki, Juhani, et al.
Published: (2025)

Estimating Unbounded Density Ratios: Applications in Error Control under Covariate Shift
by: Xu, Shuntuo, et al.
Published: (2025)

Pseudo-Labeling for Kernel Ridge Regression under Covariate Shift
by: Wang, Kaizheng
Published: (2023)

A Minimax Theory of Nonparametric Regression Under Covariate Shift
by: Zamolodtchikov, Petr
Published: (2026)

Batched Single-Index Global Multi-Armed Bandits with Covariates
by: Arya, Sakshi, et al.
Published: (2025)

Kernel $ε$-Greedy for Multi-Armed Bandits with Covariates
by: Arya, Sakshi, et al.
Published: (2023)

Variance-Reduced Manifold Sampling via Polynomial-Maximization Density Estimation
by: Zabolotnii, Serhii
Published: (2026)

Data-driven Error Estimation: Excess Risk Bounds without Class Complexity as Input
by: Krishnamurthy, Sanath Kumar, et al.
Published: (2024)

When to Trust Confidence Thresholding: Calibration Diagnostics for Pseudo-Labelled Regression
by: Kurbucz, Marcell T.
Published: (2026)

Adversarial Robust Low Rank Matrix Estimation: Compressed Sensing and Matrix Completion
by: Sasai, Takeyuki, et al.
Published: (2020)

Joint Estimation of Conditional Mean and Covariance for Unbalanced Panels
by: Filipovic, Damir, et al.
Published: (2024)

Semi-Supervised Deep Sobolev Regression: Estimation and Variable Selection by ReQU Neural Network
by: Ding, Zhao, et al.
Published: (2024)

Jacobian-Velocity Bounds for Deployment Risk Under Covariate Drift
by: Landers, Jonathan R.
Published: (2026)

Efficient Estimation of the Central Mean Subspace via Smoothed Gradient Outer Products
by: Yuan, Gan, et al.
Published: (2023)

Kernel Single-Index Bandits: Estimation, Inference, and Learning
by: Arya, Sakshi, et al.
Published: (2026)

Model Assessment and Selection under Temporal Distribution Shift
by: Han, Elise, et al.
Published: (2024)

Model-Agnostic Covariate-Assisted Inference on Partially Identified Causal Effects
by: Ji, Wenlong, et al.
Published: (2023)

Convolutional neural networks for valid and efficient causal inference
by: Ghasempour, Mohammad, et al.
Published: (2023)

Functional Partial Least-Squares: Adaptive Estimation and Inference
by: Babii, Andrii, et al.
Published: (2024)

Estimating the size of a set using cascading exclusion
by: Chatterjee, Sourav, et al.
Published: (2025)

When Marginals Match but Structure Fails: Covariance Fidelity in Generative Models
by: Riasat, Nazia
Published: (2026)

Statistical Multicriteria Benchmarking via the GSD-Front
by: Jansen, Christoph, et al.
Published: (2024)

Calibration Bands for Mean Estimates within the Exponential Dispersion Family
by: Delong, Łukasz, et al.
Published: (2025)

Adversarial robust weighted Huber regression
by: Sasai, Takeyuki, et al.
Published: (2021)

Wasserstein Distributionally Robust Nonparametric Regression
by: Liu, Changyu, et al.
Published: (2025)

Alignment-Sensitive Minimax Rates for Spectral Algorithms with Learned Kernels
by: Huang, Dongming, et al.
Published: (2025)

Minimax Rates of Estimation for Optimal Transport Map between Infinite-Dimensional Spaces
by: Ponnoprat, Donlapark, et al.
Published: (2025)

Transcendental Regularization of Finite Mixtures:Theoretical Guarantees and Practical Limitations
by: Fokoué, Ernest
Published: (2026)

Phase Transition in Nonparametric Minimax Rates for Covariate Shifts on Approximate Manifolds
by: Wang, Yuyao, et al.
Published: (2025)

Efficient adjustment for complex covariates: Gaining efficiency with DOPE
by: Christgau, Alexander Mangulad, et al.
Published: (2024)

From Conditional to Unconditional Independence: Testing Conditional Independence via Transport Maps
by: He, Chenxuan, et al.
Published: (2025)

Clustering risk in Non-parametric Hidden Markov and I.I.D. Models
by: Gassiat, Elisabeth, et al.
Published: (2023)

Throwing Vines at the Wall: Structure Learning via Random Search
by: Vatter, Thibault, et al.
Published: (2025)

Direct Estimation of Schrödinger Bridge Time-Series Drifts: Finite-Sample, Asymptotic, and Adaptive Guarantees
by: Mazhar, Othmane, et al.
Published: (2026)

Enhanced Survival Trees
by: Zhou, Ruiwen, et al.
Published: (2025)

Why Empirical p-Values Are Not Uniform: Reference Samples, Dependence, and PIT Backtesting
by: Lis, Jakub
Published: (2026)

Bayesian Power Steering: An Effective Approach for Domain Adaptation of Diffusion Models
by: Huang, Ding, et al.
Published: (2024)

Minimax Optimality of Classical Scaling Under General Noise Conditions
by: Vishwanath, Siddharth, et al.
Published: (2025)

ALMAB-DC: Active Learning, Multi-Armed Bandits, and Distributed Computing for Sequential Experimental Design and Black-Box Optimization
by: Hui-Mean, Foo, et al.
Published: (2026)