:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Zhang, Jifan, Li, Michelle M., Zheleva, Elena
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2509.01916
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

TriOpt: A Scalable Algorithm for Linear Causal Discovery
by: Joy, Rafat Ashraf, et al.
Published: (2026)

Inferring Individual Direct Causal Effects Under Heterogeneous Peer Influence
by: Adhikari, Shishir, et al.
Published: (2023)

See Me and Believe Me: Causality and Intersectionality in Testimonial Injustice in Healthcare
by: Andrews, Kenya S., et al.
Published: (2024)

Estimating Causal Effects in Networks with Cluster-Based Bandits
by: Faruk, Ahmed Sayeed, et al.
Published: (2025)

Cascade-based Randomization for Inferring Causal Effects under Diffusion Interference
by: Fatemi, Zahra, et al.
Published: (2024)

Relational Causal Discovery with Latent Confounders
by: Negro, Matteo, et al.
Published: (2025)

Leveraging heterogeneous spillover in maximizing contextual bandit rewards
by: Faruk, Ahmed Sayeed, et al.
Published: (2023)

MCCE: Missingness-aware Causal Concept Explainer
by: Gao, Jifan, et al.
Published: (2024)

Correcting for Position Bias in Learning to Rank: A Control Function Approach
by: Islam, Md Aminul, et al.
Published: (2025)

Learning Exposure Mapping Functions for Inferring Heterogeneous Peer Effects
by: Adhikari, Shishir, et al.
Published: (2025)

Learning Peer Influence Probabilities with Linear Contextual Bandits
by: Faruk, Ahmed Sayeed, et al.
Published: (2025)

Data-Driven Estimation of Heterogeneous Treatment Effects
by: Tran, Christopher, et al.
Published: (2023)

Debiasing Message Passing to Mitigate Popularity Bias in GNN-based Collaborative Filtering
by: Islam, Md Aminul, et al.
Published: (2026)

Causality and Bayesian network PDEs for multiscale representations of porous media
by: Um, Kimoon, et al.
Published: (2019)

Causal invariant geographic network representations with feature and structural distribution shifts
by: Wang, Yuhan, et al.
Published: (2025)

Learning to Actively Learn: A Robust Approach
by: Zhang, Jifan, et al.
Published: (2020)

Unknown Aware AI-Generated Content Attribution
by: Thieu, Ellie, et al.
Published: (2026)

AHA: Human-Assisted Out-of-Distribution Generalization and Detection
by: Bai, Haoyue, et al.
Published: (2024)

Biology-informed neural networks learn nonlinear representations from omics data to improve genomic prediction and interpretability
by: Kontolati, Katiana, et al.
Published: (2025)

Scalable spectral representations for multi-agent reinforcement learning in network MDPs
by: Ren, Zhaolin, et al.
Published: (2024)

Utilizing Causal Network Markers to Identify Tipping Points ahead of Critical Transition
by: Bian, Shirui, et al.
Published: (2024)

PLS-based approach for fair representation learning
by: De-Diego, Elena M., et al.
Published: (2025)

Fine-grained graph representation learning for heterogeneous mobile networks with attentive fusion and contrastive learning
by: Liu, Shengheng, et al.
Published: (2024)

Causal-learn: Causal Discovery in Python
by: Zheng, Yujia, et al.
Published: (2023)

Transformers learn factored representations
by: Shai, Adam, et al.
Published: (2026)

Causal inference through multi-stage learning and doubly robust deep neural networks
by: Zhang, Yuqian, et al.
Published: (2024)

Transformer representation learning is necessary for dynamic multi-modal physiological data on small-cohort patients
by: Wang, Bingxu, et al.
Published: (2025)

Learning representations of learning representations
by: González-Márquez, Rita, et al.
Published: (2024)

Curriculum reinforcement learning with measurable task representation learning
by: Wen, Yongyan, et al.
Published: (2026)

Harnessing Multiple Correlated Networks for Exact Community Recovery
by: Rácz, Miklós Z., et al.
Published: (2024)

Statistical and structural identifiability in representation learning
by: Nelson, Walter, et al.
Published: (2026)

Category learning in deep neural networks: Information content and geometry of internal representations
by: Bonnasse-Gahot, Laurent, et al.
Published: (2025)

Evaluating alignment between humans and neural network representations in image-based learning tasks
by: Demircan, Can, et al.
Published: (2023)

The CausalBench challenge: A machine learning contest for gene network inference from single-cell perturbation data
by: Chevalley, Mathieu, et al.
Published: (2023)

Tensor network to learn the wavefunction of data
by: Dymarsky, Anatoly, et al.
Published: (2021)

Evaluating representation learning on the protein structure universe
by: Jamasb, Arian R., et al.
Published: (2024)

SE3Set: Harnessing equivariant hypergraph neural networks for molecular representation learning
by: Wu, Hongfei, et al.
Published: (2024)

Causal machine learning for heterogeneous treatment effects in the presence of missing outcome data
by: Pryce, Matthew, et al.
Published: (2024)

A transformer-BiGRU-based framework with data augmentation and confident learning for network intrusion detection
by: Zhang, Jiale, et al.
Published: (2025)

GPT-4o as the Gold Standard: A Scalable and General Purpose Approach to Filter Language Model Pretraining Data
by: Zhang, Jifan, et al.
Published: (2024)