Saved in:
| Main Authors: | Elkady, Mai, Bui, Thu, Ribeiro, Bruno, Inouye, David I. |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.13358 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Decoupled Vertical Federated Learning for Practical Training on Vertically Partitioned Data
by: Amalanshu, Avi, et al.
Published: (2024)
by: Amalanshu, Avi, et al.
Published: (2024)
On the Effectiveness of Random Weights in Graph Neural Networks
by: Bui, Thu, et al.
Published: (2025)
by: Bui, Thu, et al.
Published: (2025)
Flow-based Generative Modeling of Potential Outcomes and Counterfactuals
by: Wu, Dongze, et al.
Published: (2025)
by: Wu, Dongze, et al.
Published: (2025)
Robust Collaborative Inference with Vertically Split Data Over Dynamic Device Environments
by: Ganguli, Surojit, et al.
Published: (2023)
by: Ganguli, Surojit, et al.
Published: (2023)
Benchmarking Algorithms for Federated Domain Generalization
by: Bai, Ruqi, et al.
Published: (2023)
by: Bai, Ruqi, et al.
Published: (2023)
Your VAR Model is Secretly an Efficient and Explainable Generative Classifier
by: Chen, Yi-Chung, et al.
Published: (2025)
by: Chen, Yi-Chung, et al.
Published: (2025)
Expressive Score-Based Priors for Distribution Matching with Geometry-Preserving Regularization
by: Gong, Ziyu, et al.
Published: (2025)
by: Gong, Ziyu, et al.
Published: (2025)
Towards Practical Non-Adversarial Distribution Matching
by: Gong, Ziyu, et al.
Published: (2023)
by: Gong, Ziyu, et al.
Published: (2023)
From Invariant Representations to Invariant Data: Provable Robustness to Spurious Correlations via Noisy Counterfactual Matching
by: Bai, Ruqi, et al.
Published: (2025)
by: Bai, Ruqi, et al.
Published: (2025)
Towards Characterizing Domain Counterfactuals For Invertible Latent Causal Models
by: Zhou, Zeyu, et al.
Published: (2023)
by: Zhou, Zeyu, et al.
Published: (2023)
Implicit Maximum Likelihood Estimation for Real-time Generative Model Predictive Control
by: Lee, Grayson, et al.
Published: (2026)
by: Lee, Grayson, et al.
Published: (2026)
Counterfactual Fairness by Combining Factual and Counterfactual Predictions
by: Zhou, Zeyu, et al.
Published: (2024)
by: Zhou, Zeyu, et al.
Published: (2024)
TRIX: A More Expressive Model for Zero-shot Domain Transfer in Knowledge Graphs
by: Zhang, Yucheng, et al.
Published: (2025)
by: Zhang, Yucheng, et al.
Published: (2025)
Correctness Assessment of Code Generated by Large Language Models Using Internal Representations
by: Bui, Tuan-Dung, et al.
Published: (2025)
by: Bui, Tuan-Dung, et al.
Published: (2025)
Implicit vs Unfolded Graph Neural Networks
by: Yang, Yongyi, et al.
Published: (2021)
by: Yang, Yongyi, et al.
Published: (2021)
ComaDICE: Offline Cooperative Multi-Agent Reinforcement Learning with Stationary Distribution Shift Regularization
by: Bui, The Viet, et al.
Published: (2024)
by: Bui, The Viet, et al.
Published: (2024)
Preference-Guided Learning for Sparse-Reward Multi-Agent Reinforcement Learning
by: Bui, The Viet, et al.
Published: (2025)
by: Bui, The Viet, et al.
Published: (2025)
O-MAPL: Offline Multi-agent Preference Learning
by: Bui, The Viet, et al.
Published: (2025)
by: Bui, The Viet, et al.
Published: (2025)
GraphMETRO: Mitigating Complex Graph Distribution Shifts via Mixture of Aligned Experts
by: Wu, Shirley, et al.
Published: (2023)
by: Wu, Shirley, et al.
Published: (2023)
Bridging Input Feature Spaces Towards Graph Foundation Models
by: Eliasof, Moshe, et al.
Published: (2026)
by: Eliasof, Moshe, et al.
Published: (2026)
Generalization of Graph Neural Networks is Robust to Model Mismatch
by: Wang, Zhiyang, et al.
Published: (2024)
by: Wang, Zhiyang, et al.
Published: (2024)
Implicit Graph Neural Diffusion Networks: Convergence, Generalization, and Over-Smoothing
by: Fu, Guoji, et al.
Published: (2023)
by: Fu, Guoji, et al.
Published: (2023)
MisoDICE: Multi-Agent Imitation from Unlabeled Mixed-Quality Demonstrations
by: Bui, The Viet, et al.
Published: (2025)
by: Bui, The Viet, et al.
Published: (2025)
The Score-Difference Flow for Implicit Generative Modeling
by: Weber, Romann M.
Published: (2023)
by: Weber, Romann M.
Published: (2023)
Trust Region Continual Learning as an Implicit Meta-Learner
by: Wang, Zekun, et al.
Published: (2026)
by: Wang, Zekun, et al.
Published: (2026)
Graph Generative Models Evaluation with Masked Autoencoder
by: Wang, Chengen, et al.
Published: (2025)
by: Wang, Chengen, et al.
Published: (2025)
GOLD: Graph Out-of-Distribution Detection via Implicit Adversarial Latent Generation
by: Wang, Danny, et al.
Published: (2025)
by: Wang, Danny, et al.
Published: (2025)
Graph Signal Generative Diffusion Models
by: Uslu, Yigit Berkay, et al.
Published: (2025)
by: Uslu, Yigit Berkay, et al.
Published: (2025)
Coverage-Validity-Aware Algorithmic Recourse
by: Bui, Ngoc, et al.
Published: (2023)
by: Bui, Ngoc, et al.
Published: (2023)
SWING: Unlocking Implicit Graph Representations for Graph Random Features
by: Manenti, Alessandro, et al.
Published: (2026)
by: Manenti, Alessandro, et al.
Published: (2026)
Implicit Dynamical Flow Fusion (IDFF) for Generative Modeling
by: Rezaei, Mohammad R., et al.
Published: (2024)
by: Rezaei, Mohammad R., et al.
Published: (2024)
Enhancing Explainability of Graph Neural Networks Through Conceptual and Structural Analyses and Their Extensions
by: Bui, Tien Cuong
Published: (2025)
by: Bui, Tien Cuong
Published: (2025)
A Manifold Perspective on the Statistical Generalization of Graph Neural Networks
by: Wang, Zhiyang, et al.
Published: (2024)
by: Wang, Zhiyang, et al.
Published: (2024)
Understanding Nonlinear Implicit Bias via Region Counts in Input Space
by: Li, Jingwei, et al.
Published: (2025)
by: Li, Jingwei, et al.
Published: (2025)
Anytime-Valid Conformal Risk Control
by: Hultberg, Bror, et al.
Published: (2026)
by: Hultberg, Bror, et al.
Published: (2026)
Efficient and Effective Implicit Dynamic Graph Neural Network
by: Zhong, Yongjian, et al.
Published: (2024)
by: Zhong, Yongjian, et al.
Published: (2024)
Implicit meta-learning may lead language models to trust more reliable sources
by: Krasheninnikov, Dmitrii, et al.
Published: (2023)
by: Krasheninnikov, Dmitrii, et al.
Published: (2023)
IGNN-Solver: A Graph Neural Solver for Implicit Graph Neural Networks
by: Lin, Junchao, et al.
Published: (2024)
by: Lin, Junchao, et al.
Published: (2024)
Explicit Preference Optimization: No Need for an Implicit Reward Model
by: Hu, Xiangkun, et al.
Published: (2025)
by: Hu, Xiangkun, et al.
Published: (2025)
Survey-aware Machine Learning: A Guideline for Valid Population Health Inference based on Scoping Review
by: Oh, YongKyung, et al.
Published: (2026)
by: Oh, YongKyung, et al.
Published: (2026)
Similar Items
-
Decoupled Vertical Federated Learning for Practical Training on Vertically Partitioned Data
by: Amalanshu, Avi, et al.
Published: (2024) -
On the Effectiveness of Random Weights in Graph Neural Networks
by: Bui, Thu, et al.
Published: (2025) -
Flow-based Generative Modeling of Potential Outcomes and Counterfactuals
by: Wu, Dongze, et al.
Published: (2025) -
Robust Collaborative Inference with Vertically Split Data Over Dynamic Device Environments
by: Ganguli, Surojit, et al.
Published: (2023) -
Benchmarking Algorithms for Federated Domain Generalization
by: Bai, Ruqi, et al.
Published: (2023)