:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Zhou, Hang, Mueller, Jonas, Kumar, Mayank, Wang, Jane-Ling, Lei, Jing
Format:	Preprint
Published:	2023
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2305.16583
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Beyond the Norms: Detecting Prediction Errors in Regression Models
by: Altieri, Andres, et al.
Published: (2024)

Uniform Error Bounds for Gaussian Process Regression with Application to Safe Control
by: Lederer, Armin, et al.
Published: (2019)

any4: Learned 4-bit Numeric Representation for LLMs
by: Elhoushi, Mostafa, et al.
Published: (2025)

On the Interpolation Error of Nonlinear Attention versus Linear Regression
by: Liao, Zhenyu, et al.
Published: (2025)

ActiveLab: Active Learning with Re-Labeling by Multiple Annotators
by: Goh, Hui Wen, et al.
Published: (2023)

Error Taxonomy-Guided Prompt Optimization
by: Singh, Mayank, et al.
Published: (2026)

Numerical Error Analysis of Large Language Models
by: Budzinskiy, Stanislav, et al.
Published: (2025)

Distributional Regression with Tabular Foundation Models: Evaluating Probabilistic Predictions via Proper Scoring Rules
by: Landsgesell, Jonas, et al.
Published: (2026)

DLBacktrace: A Model Agnostic Explainability for any Deep Learning Models
by: Sankarapu, Vinay Kumar, et al.
Published: (2024)

Bayesian Multiple Multivariate Density-Density Regression
by: Nguyen, Khai, et al.
Published: (2026)

Error Reduction from Stacked Regressions
by: Chen, Xin, et al.
Published: (2023)

Generalized Resubstitution for Regression Error Estimation
by: Marcondes, Diego, et al.
Published: (2024)

Bayesian Multivariate Density-Density Regression
by: Nguyen, Khai, et al.
Published: (2025)

Real-Time Trustworthiness Scoring for LLM Structured Outputs and Data Extraction
by: Goh, Hui Wen, et al.
Published: (2026)

Open Challenges in Time Series Anomaly Detection: An Industry Perspective
by: Mueller, Andreas
Published: (2025)

Mahalanobis++: Improving OOD Detection via Feature Normalization
by: Mueller, Maximilian, et al.
Published: (2025)

Dac-Fake: A Divide and Conquer Framework for Detecting Fake News on Social Media
by: Jain, Mayank Kumar, et al.
Published: (2025)

Omni-Mol: Multitask Molecular Model for Any-to-any Modalities
by: Hu, Chengxin, et al.
Published: (2025)

Inference for Large Scale Regression Models with Dependent Errors
by: Voirol, Lionel, et al.
Published: (2024)

Intrinsic Gaussian Process Regression Modeling for Manifold-valued Response Variable
by: Wang, Zhanfeng, et al.
Published: (2024)

Reducing Variance in Meta-Learning via Laplace Approximation for Regression Tasks
by: Reichlin, Alfredo, et al.
Published: (2024)

Shuffled Linear Regression via Spectral Matching
by: Liu, Hang, et al.
Published: (2024)

Error Distribution Smoothing:Advancing Low-Dimensional Imbalanced Regression
by: Chen, Donghe, et al.
Published: (2025)

Tensor Hypercontraction Error Correction Using Regression
by: Satyarth, Ishna, et al.
Published: (2026)

MeshFleet: Filtered and Annotated 3D Vehicle Dataset for Domain Specific Generative Modeling
by: Boborzi, Damian, et al.
Published: (2025)

Heavy-Tailed Linear Bandits: Huber Regression with One-Pass Update
by: Wang, Jing, et al.
Published: (2025)

Numerical Pruning for Efficient Autoregressive Models
by: Shen, Xuan, et al.
Published: (2024)

Understanding When Poisson Log-Normal Models Outperform Penalized Poisson Regression for Microbiome Count Data
by: Agyapong, Daniel, et al.
Published: (2026)

Coefficient Shape Alignment in Multiple Functional Linear Regression
by: Jiao, Shuhao, et al.
Published: (2023)

A Theoretical Study of Neural Network Expressive Power via Manifold Topology
by: Yao, Jiachen, et al.
Published: (2024)

Throughput Optimization as a Strategic Lever in Large-Scale AI Systems: Evidence from Dataloader and Memory Profiling Innovations
by: Jha, Mayank
Published: (2026)

On Uniform Error Bounds for Kernel Regression under Non-Gaussian Noise
by: Teutsch, Johannes, et al.
Published: (2026)

High-Dimensional Tail Index Regression
by: Sasaki, Yuya, et al.
Published: (2024)

Sparse Techniques for Regression in Deep Gaussian Processes
by: Latz, Jonas, et al.
Published: (2025)

Practical Deep Heteroskedastic Regression
by: Jordahn, Mikkel, et al.
Published: (2026)

Generalization Error Matters in Decentralized Learning Under Byzantine Attacks
by: Ye, Haoxiang, et al.
Published: (2024)

Diffusion LLMs are Natural Adversaries for any LLM
by: Lüdke, David, et al.
Published: (2025)

Deep Knowledge-Infusion For Explainable Depression Detection
by: Dalal, Sumit, et al.
Published: (2024)

Conditional Rank-Rank Regression via Deep Conditional Transformation Models
by: Wang, Xiaoyi, et al.
Published: (2026)

Scalable Spatiotemporally Varying Coefficient Modelling with Bayesian Kernelized Tensor Regression
by: Lei, Mengying, et al.
Published: (2021)