:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Young, Spencer, Jenkins, Porter, Da, Longchao, Dotson, Jeff, Wei, Hua
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2406.09262
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

RegExplainer: Generating Explanations for Graph Neural Networks in Regression Tasks
by: Zhang, Jiaxing, et al.
Published: (2023)

CityFlowER: An Efficient and Realistic Traffic Simulator with Embedded Machine Learning Models
by: Da, Longchao, et al.
Published: (2024)

Towards Self-Supervised Covariance Estimation in Deep Heteroscedastic Regression
by: Shukla, Megh, et al.
Published: (2025)

Semi-Supervised Regression with Heteroscedastic Pseudo-Labels
by: Sun, Xueqing, et al.
Published: (2025)

Heteroscedastic Double Bayesian Elastic Net
by: Kimura, Masanari
Published: (2025)

Assessing the Probabilistic Fit of Neural Regressors via Conditional Congruence
by: Young, Spencer, et al.
Published: (2024)

TIC-TAC: A Framework for Improved Covariance Estimation in Deep Heteroscedastic Regression
by: Shukla, Megh, et al.
Published: (2023)

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

CAAL: Confidence-Aware Active Learning for Heteroscedastic Atmospheric Regression
by: Jiang, Fei, et al.
Published: (2026)

Position: Uncertainty Quantification in LLMs is Just Unsupervised Clustering
by: Chen, Tiejin, et al.
Published: (2026)

Joint-Local Grounded Action Transformation for Sim-to-Real Transfer in Multi-Agent Traffic Control
by: Turnau, Justin, et al.
Published: (2025)

Uncertainty-Aware Trajectory Prediction via Rule-Regularized Heteroscedastic Deep Classification
by: Manas, Kumar, et al.
Published: (2025)

Levenshtein Distance Embedding with Poisson Regression for DNA Storage
by: Wei, Xiang, et al.
Published: (2023)

A Framework for Variational Inference of Lightweight Bayesian Neural Networks with Heteroscedastic Uncertainties
by: Schodt, David J., et al.
Published: (2024)

Enhancing reliability in prediction intervals using point forecasters: Heteroscedastic Quantile Regression and Width-Adaptive Conformal Inference
by: Sebastián, Carlos, et al.
Published: (2024)

Heteroscedastic Neural Networks for Path Loss Prediction with Link-Specific Uncertainty
by: Ethier, Jonathan
Published: (2025)

ALPCAHUS: Subspace Clustering for Heteroscedastic Data
by: Cavazos, Javier Salazar, et al.
Published: (2025)

Symbolic Regression via Neural Networks
by: Boddupalli, Nibodh, et al.
Published: (2026)

Personalized Product Assortment with Real-time 3D Perception and Bayesian Payoff Estimation
by: Jenkins, Porter, et al.
Published: (2024)

Zero-Truncated Poisson Regression for Sparse Multiway Count Data Corrupted by False Zeros
by: López, Oscar, et al.
Published: (2022)

Anchor-Based Heteroscedastic Noise for Preferential Bayesian Optimization
by: Sinaga, Marshal Arijona, et al.
Published: (2024)

A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models
by: Da, Longchao, et al.
Published: (2025)

SPEED: Experimental Design for Policy Evaluation in Linear Heteroscedastic Bandits
by: Mukherjee, Subhojyoti, et al.
Published: (2023)

Theoretical Analysis of Heteroscedastic Gaussian Processes with Posterior Distributions
by: Ito, Yuji
Published: (2024)

ALPCAH: Subspace Learning for Sample-wise Heteroscedastic Data
by: Cavazos, Javier Salazar, et al.
Published: (2025)

Heteroscedastic Temporal Variational Autoencoder For Irregular Time Series
by: Shukla, Satya Narayan, et al.
Published: (2021)

Deep Submodular Peripteral Networks
by: Bhatt, Gantavya, et al.
Published: (2024)

Heteroscedastic Diffusion for Multi-Agent Trajectory Modeling
by: Capellera, Guillem, et al.
Published: (2026)

On the Lipschitz Constant of Deep Networks and Double Descent
by: Gamba, Matteo, et al.
Published: (2023)

H-AddiVortes: Heteroscedastic (Bayesian) Additive Voronoi Tessellations
by: Stone, Adam J., et al.
Published: (2025)

A Skewness-Based Criterion for Addressing Heteroscedastic Noise in Causal Discovery
by: Lin, Yingyu, et al.
Published: (2024)

Effective Causal Discovery under Identifiable Heteroscedastic Noise Model
by: Yin, Naiyu, et al.
Published: (2023)

Probabilistic Wind Power Modelling via Heteroscedastic Non-Stationary Gaussian Processes
by: Ladopoulou, Domniki, et al.
Published: (2025)

Moment Matters: Mean and Variance Causal Graph Discovery from Heteroscedastic Observational Data
by: Chikahara, Yoichi
Published: (2026)

Uncertainty Regularized Evidential Regression
by: Ye, Kai, et al.
Published: (2024)

A Novel Theoretical Analysis for Clustering Heteroscedastic Gaussian Data without Knowledge of the Number of Clusters
by: Pastor, Dominique, et al.
Published: (2026)

New Approximation Results and Optimal Estimation for Fully Connected Deep Neural Networks
by: Tang, Zhaoji
Published: (2025)

Two-Timescale Linear Stochastic Approximation: Constant Stepsizes Go a Long Way
by: Kwon, Jeongyeol, et al.
Published: (2024)

Expressive Symbolic Regression for Interpretable Models of Discrete-Time Dynamical Systems
by: Iyer, Adarsh, et al.
Published: (2024)

Learning to Simulate: Generative Metamodeling via Quantile Regression
by: Hong, L. Jeff, et al.
Published: (2023)