Saved in:
| Main Authors: | Zhao, Peiqi, Rodríguez, Carlos E., Mena, Ramsés H., Walker, Stephen G. |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.21255 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
On Foundation Models for Temporal Point Processes to Accelerate Scientific Discovery
by: Berghaus, David, et al.
Published: (2025)
by: Berghaus, David, et al.
Published: (2025)
In-Context Learning of Temporal Point Processes with Foundation Inference Models
by: Berghaus, David, et al.
Published: (2025)
by: Berghaus, David, et al.
Published: (2025)
Weighted Low-rank Approximation via Stochastic Gradient Descent on Manifolds
by: Xu, Conglong, et al.
Published: (2025)
by: Xu, Conglong, et al.
Published: (2025)
COVID-19 Clinical footprint to infer about mortality
by: Rodríguez, Carlos E., et al.
Published: (2021)
by: Rodríguez, Carlos E., et al.
Published: (2021)
Interpretable Model-Aware Counterfactual Explanations for Random Forest
by: Harvey, Joshua S., et al.
Published: (2025)
by: Harvey, Joshua S., et al.
Published: (2025)
Dual-Domain Deep Learning Method to Accelerate Local Basis Functions Computation for Reservoir Simulation in High-Contrast Porous Media
by: Li, Peiqi, et al.
Published: (2025)
by: Li, Peiqi, et al.
Published: (2025)
Efficient Alternating Minimization with Applications to Weighted Low Rank Approximation
by: Song, Zhao, et al.
Published: (2023)
by: Song, Zhao, et al.
Published: (2023)
Revisiting Random Weight Perturbation for Efficiently Improving Generalization
by: Li, Tao, et al.
Published: (2024)
by: Li, Tao, et al.
Published: (2024)
Adaptive Stochastic Gradient Descents on Manifolds with an Application on Weighted Low-Rank Approximation
by: Yang, Peiqi, et al.
Published: (2025)
by: Yang, Peiqi, et al.
Published: (2025)
Foundation Inference Models for Markov Jump Processes
by: Berghaus, David, et al.
Published: (2024)
by: Berghaus, David, et al.
Published: (2024)
Towards Foundation Inference Models that Learn ODEs In-Context
by: Mauel, Maximilian, et al.
Published: (2025)
by: Mauel, Maximilian, et al.
Published: (2025)
Zero-shot Imputation with Foundation Inference Models for Dynamical Systems
by: Seifner, Patrick, et al.
Published: (2024)
by: Seifner, Patrick, et al.
Published: (2024)
In-Context Learning of Stochastic Differential Equations with Foundation Inference Models
by: Seifner, Patrick, et al.
Published: (2025)
by: Seifner, Patrick, et al.
Published: (2025)
Diversity Measurement and Subset Selection for Instruction Tuning Datasets
by: Wang, Peiqi, et al.
Published: (2024)
by: Wang, Peiqi, et al.
Published: (2024)
Integrating Random Forests and Generalized Linear Models for Improved Accuracy and Interpretability
by: Agarwal, Abhineet, et al.
Published: (2023)
by: Agarwal, Abhineet, et al.
Published: (2023)
Hybrid Two-Stage Reconstruction of Multiscale Subsurface Flow with Physics-informed Residual Connected Neural Operator
by: Li, Peiqi, et al.
Published: (2025)
by: Li, Peiqi, et al.
Published: (2025)
Out-of-Support Generalisation via Weight-Space Sequence Modelling
by: Nzoyem, Roussel Desmond
Published: (2026)
by: Nzoyem, Roussel Desmond
Published: (2026)
Foundation Inference Models for Ordinary Differential Equations
by: Mauel, Maximilian, et al.
Published: (2026)
by: Mauel, Maximilian, et al.
Published: (2026)
Learning to Interpret Weight Differences in Language Models
by: Goel, Avichal, et al.
Published: (2025)
by: Goel, Avichal, et al.
Published: (2025)
Generalized Random Forests using Fixed-Point Trees
by: Fleischer, David, et al.
Published: (2023)
by: Fleischer, David, et al.
Published: (2023)
Less Discriminatory Alternative and Interpretable XGBoost Framework for Binary Classification
by: Pangia, Andrew, et al.
Published: (2024)
by: Pangia, Andrew, et al.
Published: (2024)
CellStream: Dynamical Optimal Transport Informed Embeddings for Reconstructing Cellular Trajectories from Snapshots Data
by: Ling, Yue, et al.
Published: (2025)
by: Ling, Yue, et al.
Published: (2025)
Random Forest Weighted Local Fréchet Regression with Random Objects
by: Qiu, Rui, et al.
Published: (2022)
by: Qiu, Rui, et al.
Published: (2022)
An Interpretable Alternative to Neural Representation Learning for Rating Prediction -- Transparent Latent Class Modeling of User Reviews
by: Serra, Giuseppe, et al.
Published: (2024)
by: Serra, Giuseppe, et al.
Published: (2024)
An Interpretable Measure for Quantifying Predictive Dependence between Continuous Random Variables -- Extended Version
by: Assunção, Renato, et al.
Published: (2025)
by: Assunção, Renato, et al.
Published: (2025)
Meta Additive Model: Interpretable Sparse Learning With Auto Weighting
by: Zhang, Xuelin, et al.
Published: (2026)
by: Zhang, Xuelin, et al.
Published: (2026)
Beyond Message Passing: A Symbolic Alternative for Expressive and Interpretable Graph Learning
by: Geng, Chuqin, et al.
Published: (2026)
by: Geng, Chuqin, et al.
Published: (2026)
Prior-Fitted Functional Flow: In-Context Generative Models for Pharmacokinetics
by: Ojeda, César, et al.
Published: (2026)
by: Ojeda, César, et al.
Published: (2026)
QuickBind: A Light-Weight And Interpretable Molecular Docking Model
by: Treyde, Wojtek, et al.
Published: (2024)
by: Treyde, Wojtek, et al.
Published: (2024)
Interpreting the Weight Space of Customized Diffusion Models
by: Dravid, Amil, et al.
Published: (2024)
by: Dravid, Amil, et al.
Published: (2024)
The Exponentially Weighted Signature
by: Bloch, Alexandre, et al.
Published: (2026)
by: Bloch, Alexandre, et al.
Published: (2026)
GraphWeave: Interpretable and Robust Graph Generation via Random Walk Trajectories
by: Nandakumar, Rahul, et al.
Published: (2025)
by: Nandakumar, Rahul, et al.
Published: (2025)
Towards Fast Coarse-graining and Equation Discovery with Foundation Inference Models
by: Hinz, Manuel, et al.
Published: (2025)
by: Hinz, Manuel, et al.
Published: (2025)
On Model-Based Clustering With Entropic Optimal Transport
by: Mena, Gonzalo
Published: (2026)
by: Mena, Gonzalo
Published: (2026)
Spectral Graph Sample Weighting for Interpretable Sub-cohort Analysis in Predictive Models for Neuroimaging
by: Paschali, Magdalini, et al.
Published: (2024)
by: Paschali, Magdalini, et al.
Published: (2024)
Hyper Hawkes Processes: Interpretable Models of Marked Temporal Point Processes
by: Boyd, Alex, et al.
Published: (2025)
by: Boyd, Alex, et al.
Published: (2025)
Interpretable Neural Temporal Point Processes for Modelling Electronic Health Records
by: Liu, Bingqing
Published: (2024)
by: Liu, Bingqing
Published: (2024)
Amortized In-Context Mixed Effect Transformer Models: A Zero-Shot Approach for Pharmacokinetics
by: Marin, César Ali Ojeda, et al.
Published: (2025)
by: Marin, César Ali Ojeda, et al.
Published: (2025)
Path-Weighted Integrated Gradients for Interpretable Dementia Classification
by: Kamalov, Firuz, et al.
Published: (2025)
by: Kamalov, Firuz, et al.
Published: (2025)
SeedLM: Compressing LLM Weights into Seeds of Pseudo-Random Generators
by: Shafipour, Rasoul, et al.
Published: (2024)
by: Shafipour, Rasoul, et al.
Published: (2024)
Similar Items
-
On Foundation Models for Temporal Point Processes to Accelerate Scientific Discovery
by: Berghaus, David, et al.
Published: (2025) -
In-Context Learning of Temporal Point Processes with Foundation Inference Models
by: Berghaus, David, et al.
Published: (2025) -
Weighted Low-rank Approximation via Stochastic Gradient Descent on Manifolds
by: Xu, Conglong, et al.
Published: (2025) -
COVID-19 Clinical footprint to infer about mortality
by: Rodríguez, Carlos E., et al.
Published: (2021) -
Interpretable Model-Aware Counterfactual Explanations for Random Forest
by: Harvey, Joshua S., et al.
Published: (2025)