Saved in:
| Main Authors: | DePavia, Adela, Charisopoulos, Vasileios, Willett, Rebecca |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.23804 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Faster Adaptive Optimization via Expected Gradient Outer Product Reparameterization
by: DePavia, Adela, et al.
Published: (2025)
by: DePavia, Adela, et al.
Published: (2025)
Learning-Based Algorithms for Graph Searching Problems
by: DePavia, Adela Frances, et al.
Published: (2024)
by: DePavia, Adela Frances, et al.
Published: (2024)
Solving Inverse Problems with Deep Linear Neural Networks: Global Convergence Guarantees for Gradient Descent with Weight Decay
by: Laus, Hannah, et al.
Published: (2025)
by: Laus, Hannah, et al.
Published: (2025)
Nonlinear tomographic reconstruction via nonsmooth optimization
by: Charisopoulos, Vasileios, et al.
Published: (2024)
by: Charisopoulos, Vasileios, et al.
Published: (2024)
Causal Discovery over High-Dimensional Structured Hypothesis Spaces with Causal Graph Partitioning
by: Shah, Ashka, et al.
Published: (2024)
by: Shah, Ashka, et al.
Published: (2024)
A Model-Guided Neural Network Method for the Inverse Scattering Problem
by: Tsang, Olivia, et al.
Published: (2025)
by: Tsang, Olivia, et al.
Published: (2025)
Local linear convergence of gradient methods for overparameterized Gaussian mixtures
by: Wang, Jingxing, et al.
Published: (2026)
by: Wang, Jingxing, et al.
Published: (2026)
Quantifying the Importance of Data Alignment in Downstream Model Performance
by: Chawla, Krrish, et al.
Published: (2025)
by: Chawla, Krrish, et al.
Published: (2025)
How does the optimizer implicitly bias the model merging loss landscape?
by: Zhang, Chenxiang, et al.
Published: (2025)
by: Zhang, Chenxiang, et al.
Published: (2025)
Multi-Frequency Progressive Refinement for Learned Inverse Scattering
by: Melia, Owen, et al.
Published: (2024)
by: Melia, Owen, et al.
Published: (2024)
ReLU Neural Networks with Linear Layers are Biased Towards Single- and Multi-Index Models
by: Parkinson, Suzanna, et al.
Published: (2023)
by: Parkinson, Suzanna, et al.
Published: (2023)
Embed and Emulate: Contrastive representations for simulation-based inference
by: Jiang, Ruoxi, et al.
Published: (2024)
by: Jiang, Ruoxi, et al.
Published: (2024)
Integrating Uncertainty Awareness into Conformalized Quantile Regression
by: Rossellini, Raphael, et al.
Published: (2023)
by: Rossellini, Raphael, et al.
Published: (2023)
Stabilizing black-box model selection with the inflated argmax
by: Adrian, Melissa, et al.
Published: (2024)
by: Adrian, Melissa, et al.
Published: (2024)
Detecting clinician implicit biases in diagnoses using proximal causal inference
by: Liu, Kara, et al.
Published: (2025)
by: Liu, Kara, et al.
Published: (2025)
Understanding and inverse design of implicit bias in stochastic learning: a geometric perspective
by: Aladrah, Nicola, et al.
Published: (2026)
by: Aladrah, Nicola, et al.
Published: (2026)
Auto-differentiable data assimilation: Co-learning of states, dynamics, and filtering algorithms
by: Adrian, Melissa, et al.
Published: (2026)
by: Adrian, Melissa, et al.
Published: (2026)
Building a stable classifier with the inflated argmax
by: Soloff, Jake A., et al.
Published: (2024)
by: Soloff, Jake A., et al.
Published: (2024)
Bagging Provides Assumption-free Stability
by: Soloff, Jake A., et al.
Published: (2023)
by: Soloff, Jake A., et al.
Published: (2023)
Sketch-Augmented Features Improve Learning Long-Range Dependencies in Graph Neural Networks
by: Hosseini, Ryien, et al.
Published: (2025)
by: Hosseini, Ryien, et al.
Published: (2025)
Quality Measures for Dynamic Graph Generative Models
by: Hosseini, Ryien, et al.
Published: (2025)
by: Hosseini, Ryien, et al.
Published: (2025)
Depth Separation in Norm-Bounded Infinite-Width Neural Networks
by: Parkinson, Suzanna, et al.
Published: (2024)
by: Parkinson, Suzanna, et al.
Published: (2024)
Training neural operators to preserve invariant measures of chaotic attractors
by: Jiang, Ruoxi, et al.
Published: (2023)
by: Jiang, Ruoxi, et al.
Published: (2023)
Quantification of Uncertainties in Probabilistic Deep Neural Network by Implementing Boosting of Variational Inference
by: Bera, Pavia, et al.
Published: (2025)
by: Bera, Pavia, et al.
Published: (2025)
Data Assimilation with Machine Learning Surrogate Models: A Case Study with FourCastNet
by: Adrian, Melissa, et al.
Published: (2024)
by: Adrian, Melissa, et al.
Published: (2024)
Escape dynamics and implicit bias of one-pass SGD in overparameterized quadratic networks
by: Bocchi, Dario, et al.
Published: (2026)
by: Bocchi, Dario, et al.
Published: (2026)
Accelerating PDE Surrogates via RL-Guided Mesh Optimization
by: Meng, Yang, et al.
Published: (2026)
by: Meng, Yang, et al.
Published: (2026)
Assumption-free stability for ranking problems
by: Liang, Ruiting, et al.
Published: (2025)
by: Liang, Ruiting, et al.
Published: (2025)
Deep Stochastic Mechanics
by: Orlova, Elena, et al.
Published: (2023)
by: Orlova, Elena, et al.
Published: (2023)
Can a calibration metric be both testable and actionable?
by: Rossellini, Raphael, et al.
Published: (2025)
by: Rossellini, Raphael, et al.
Published: (2025)
Beyond Ensemble Averages: Leveraging Climate Model Ensembles for Subseasonal Forecasting
by: Orlova, Elena, et al.
Published: (2022)
by: Orlova, Elena, et al.
Published: (2022)
Sample-efficient neural likelihood-free Bayesian inference of implicit HMMs
by: Ghosh, Sanmitra, et al.
Published: (2024)
by: Ghosh, Sanmitra, et al.
Published: (2024)
CaAdam: Improving Adam optimizer using connection aware methods
by: Genet, Remi, et al.
Published: (2024)
by: Genet, Remi, et al.
Published: (2024)
Hierarchical Implicit Neural Emulators
by: Jiang, Ruoxi, et al.
Published: (2025)
by: Jiang, Ruoxi, et al.
Published: (2025)
HomeAdam: Adam and AdamW Algorithms Sometimes Go Home to Obtain Better Provable Generalization
by: Huang, Feihu, et al.
Published: (2026)
by: Huang, Feihu, et al.
Published: (2026)
Progressive distillation induces an implicit curriculum
by: Panigrahi, Abhishek, et al.
Published: (2024)
by: Panigrahi, Abhishek, et al.
Published: (2024)
How to Set $β_1, β_2$ in Adam: An Online Learning Perspective
by: Nguyen, Quan
Published: (2025)
by: Nguyen, Quan
Published: (2025)
Adam-HNAG: A Convergent Reformulation of Adam with Accelerated Rate
by: Yu, Yaxin, et al.
Published: (2026)
by: Yu, Yaxin, et al.
Published: (2026)
Forecasting the Past: Gradient-Based Distribution Shift Detection in Trajectory Prediction
by: De Vita, Michele, et al.
Published: (2026)
by: De Vita, Michele, et al.
Published: (2026)
Tune My Adam, Please!
by: Athanasiadis, Theodoros, et al.
Published: (2025)
by: Athanasiadis, Theodoros, et al.
Published: (2025)
Similar Items
-
Faster Adaptive Optimization via Expected Gradient Outer Product Reparameterization
by: DePavia, Adela, et al.
Published: (2025) -
Learning-Based Algorithms for Graph Searching Problems
by: DePavia, Adela Frances, et al.
Published: (2024) -
Solving Inverse Problems with Deep Linear Neural Networks: Global Convergence Guarantees for Gradient Descent with Weight Decay
by: Laus, Hannah, et al.
Published: (2025) -
Nonlinear tomographic reconstruction via nonsmooth optimization
by: Charisopoulos, Vasileios, et al.
Published: (2024) -
Causal Discovery over High-Dimensional Structured Hypothesis Spaces with Causal Graph Partitioning
by: Shah, Ashka, et al.
Published: (2024)