Saved in:
| Main Authors: | Yao, Dan, McLaughlin, Steve, Altmann, Yoann |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.23757 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Quantization Error Propagation: Revisiting Layer-Wise Post-Training Quantization
by: Arai, Yamato, et al.
Published: (2025)
by: Arai, Yamato, et al.
Published: (2025)
Using LLMs to Directly Guess Conditional Expectations Can Improve Efficiency in Causal Estimation
by: Engh, Chris, et al.
Published: (2025)
by: Engh, Chris, et al.
Published: (2025)
Spike-and-slab shrinkage priors for structurally sparse Bayesian neural networks
by: Jantre, Sanket, et al.
Published: (2023)
by: Jantre, Sanket, et al.
Published: (2023)
Bagged Polynomial Regression and Neural Networks
by: Klosin, Sylvia, et al.
Published: (2022)
by: Klosin, Sylvia, et al.
Published: (2022)
Adaptive Physics-Guided Neural Network
by: Shulman, David, et al.
Published: (2024)
by: Shulman, David, et al.
Published: (2024)
Neural Bayes Estimators for Irregular Spatial Data using Graph Neural Networks
by: Sainsbury-Dale, Matthew, et al.
Published: (2023)
by: Sainsbury-Dale, Matthew, et al.
Published: (2023)
Covariate-Elaborated Robust Partial Information Transfer with Conditional Spike-and-Slab Prior
by: Zhang, Ruqian, et al.
Published: (2024)
by: Zhang, Ruqian, et al.
Published: (2024)
Factor Augmented Tensor-on-Tensor Neural Networks
by: Zhou, Guanhao, et al.
Published: (2024)
by: Zhou, Guanhao, et al.
Published: (2024)
Causal Estimation of Exposure Shifts with Neural Networks
by: Tec, Mauricio, et al.
Published: (2023)
by: Tec, Mauricio, et al.
Published: (2023)
Sparsity-Induced Global Matrix Autoregressive Model with Auxiliary Network Data
by: Wu, Sanyou, et al.
Published: (2025)
by: Wu, Sanyou, et al.
Published: (2025)
Neural network-based CUSUM for online change-point detection
by: Gong, Tingnan, et al.
Published: (2022)
by: Gong, Tingnan, et al.
Published: (2022)
Calibration Prediction Interval for Non-parametric Regression and Neural Networks
by: Wu, Kejin, et al.
Published: (2025)
by: Wu, Kejin, et al.
Published: (2025)
Structure Maintained Representation Learning Neural Network for Causal Inference
by: Sun, Yang, et al.
Published: (2025)
by: Sun, Yang, et al.
Published: (2025)
Doubly Robust Conditional Independence Testing with Generative Neural Networks
by: Zhang, Yi, et al.
Published: (2024)
by: Zhang, Yi, et al.
Published: (2024)
Minimal Sufficient Representations for Self-interpretable Deep Neural Networks
by: Tan, Zhiyao, et al.
Published: (2026)
by: Tan, Zhiyao, et al.
Published: (2026)
Efficient Neural Network Training via Subset Pretraining
by: Spörer, Jan, et al.
Published: (2024)
by: Spörer, Jan, et al.
Published: (2024)
Integrating Causal Inference with Graph Neural Networks for Alzheimer's Disease Analysis
by: Peddi, Pranay Kumar, et al.
Published: (2025)
by: Peddi, Pranay Kumar, et al.
Published: (2025)
Constructive Universal Approximation and Sure Convergence for Multi-Layer Neural Networks
by: Chi, Chien-Ming
Published: (2025)
by: Chi, Chien-Ming
Published: (2025)
The Contextual Lasso: Sparse Linear Models via Deep Neural Networks
by: Thompson, Ryan, et al.
Published: (2023)
by: Thompson, Ryan, et al.
Published: (2023)
Regret Distribution in Stochastic Bandits: Optimal Trade-off between Expectation and Tail Risk
by: Simchi-Levi, David, et al.
Published: (2023)
by: Simchi-Levi, David, et al.
Published: (2023)
Approximate Maximum Likelihood Inference for Acoustic Spatial Capture-Recapture with Unknown Identities, Using Monte Carlo Expectation Maximization
by: Wang, Yuheng, et al.
Published: (2024)
by: Wang, Yuheng, et al.
Published: (2024)
Spiking the training data to correct for test set contamination
by: Wei, Johnny Tian-Zheng, et al.
Published: (2026)
by: Wei, Johnny Tian-Zheng, et al.
Published: (2026)
Covariate-dependent Graphical Model Estimation via Neural Networks with Statistical Guarantees
by: Lin, Jiahe, et al.
Published: (2025)
by: Lin, Jiahe, et al.
Published: (2025)
Self-Consistent Equation-guided Neural Networks for Censored Time-to-Event Data
by: Kim, Sehwan, et al.
Published: (2025)
by: Kim, Sehwan, et al.
Published: (2025)
Integral Probability Metrics Meet Neural Networks: The Radon-Kolmogorov-Smirnov Test
by: Paik, Seunghoon, et al.
Published: (2023)
by: Paik, Seunghoon, et al.
Published: (2023)
Multidimensional Distributional Neural Network Output Demonstrated in Super-Resolution of Surface Wind Speed
by: Goldwyn, Harrison J., et al.
Published: (2025)
by: Goldwyn, Harrison J., et al.
Published: (2025)
Neural Networks with Causal Graph Constraints: A New Approach for Treatment Effects Estimation
by: Pros, Roger, et al.
Published: (2024)
by: Pros, Roger, et al.
Published: (2024)
Contrasting Global and Patient-Specific Regression Models via a Neural Network Representation
by: Behrens, Max, et al.
Published: (2026)
by: Behrens, Max, et al.
Published: (2026)
Understanding the Trade-offs in Accuracy and Uncertainty Quantification: Architecture and Inference Choices in Bayesian Neural Networks
by: Sheinkman, Alisa, et al.
Published: (2025)
by: Sheinkman, Alisa, et al.
Published: (2025)
DFNN: A Deep Fréchet Neural Network Framework for Learning Metric-Space-Valued Responses
by: Kim, Kyum, et al.
Published: (2025)
by: Kim, Kyum, et al.
Published: (2025)
Screening for Diabetes Mellitus in the U.S. Population Using Neural Network Models and Complex Survey Designs
by: Matabuena, Marcos, et al.
Published: (2024)
by: Matabuena, Marcos, et al.
Published: (2024)
Neural Networks Decoded: Targeted and Robust Analysis of Neural Network Decisions via Causal Explanations and Reasoning
by: Diallo, Alec F., et al.
Published: (2024)
by: Diallo, Alec F., et al.
Published: (2024)
Spike-and-Slab Posterior Sampling in High Dimensions
by: Kumar, Syamantak, et al.
Published: (2025)
by: Kumar, Syamantak, et al.
Published: (2025)
Choosing a Proxy Metric from Past Experiments
by: Tripuraneni, Nilesh, et al.
Published: (2023)
by: Tripuraneni, Nilesh, et al.
Published: (2023)
Probabilistic Graphical Model using Graph Neural Networks for Bayesian Inversion of Discrete Structural Component States
by: Li, Teng, et al.
Published: (2026)
by: Li, Teng, et al.
Published: (2026)
neuralGAM: An R Package for Fitting Generalized Additive Neural Networks
by: Ortega-Fernandez, Ines, et al.
Published: (2025)
by: Ortega-Fernandez, Ines, et al.
Published: (2025)
DiffKnock: Diffusion-based Knockoff Statistics for Neural Networks Inference
by: Ge, Heng, et al.
Published: (2025)
by: Ge, Heng, et al.
Published: (2025)
Neural Networks for Extreme Quantile Regression with an Application to Forecasting of Flood Risk
by: Pasche, Olivier C., et al.
Published: (2022)
by: Pasche, Olivier C., et al.
Published: (2022)
Self-Distillation is Optimal Among Spectral Shrinkage Estimators in Spiked Covariance Models
by: Lecoiu, Radu, et al.
Published: (2026)
by: Lecoiu, Radu, et al.
Published: (2026)
Amortized Inference for Correlated Discrete Choice Models via Equivariant Neural Networks
by: Huch, Easton, et al.
Published: (2026)
by: Huch, Easton, et al.
Published: (2026)
Similar Items
-
Quantization Error Propagation: Revisiting Layer-Wise Post-Training Quantization
by: Arai, Yamato, et al.
Published: (2025) -
Using LLMs to Directly Guess Conditional Expectations Can Improve Efficiency in Causal Estimation
by: Engh, Chris, et al.
Published: (2025) -
Spike-and-slab shrinkage priors for structurally sparse Bayesian neural networks
by: Jantre, Sanket, et al.
Published: (2023) -
Bagged Polynomial Regression and Neural Networks
by: Klosin, Sylvia, et al.
Published: (2022) -
Adaptive Physics-Guided Neural Network
by: Shulman, David, et al.
Published: (2024)