Cover Image

Efficient Representations for High-Cardinality Categorical Variables in Machine Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Liang, Zixuan
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

SMKC: Sketch Based Kernel Correlation Images for Variable Cardinality Time Series Anomaly Detection
by: Zhou, Haokun
Published: (2026)

A Lightweight Learned Cardinality Estimation Model
by: Zhu, Yaoyu, et al.
Published: (2025)

General Machine Learning: Theory for Learning Under Variable Regimes
by: Osmani, Aomar
Published: (2026)

Hierarchy Representation of Data in Machine Learnings
by: Yegang, Han, et al.
Published: (2023)

Generating Counterfactual Explanations Using Cardinality Constraints
by: Ruiz-Torrubiano, Rubén
Published: (2024)

Constrained Machine Learning Through Hyperspherical Representation
by: Signorelli, Gaetano, et al.
Published: (2025)

Cardinality-Regularized Hawkes-Granger Model
by: Idé, Tsuyoshi, et al.
Published: (2022)

Categorical Policies: Multimodal Policy Learning and Exploration in Continuous Control
by: Islam, SM Mazharul, et al.
Published: (2025)

Efficient Representations are Controllable Representations
by: Ye, Charles, et al.
Published: (2026)

Cardinality-Preserving Attention Channels for Graph Transformers in Molecular Property Prediction
by: Gupta, Abhijit
Published: (2026)

Efficient Reinforcement Learning in Probabilistic Reward Machines
by: Lin, Xiaofeng, et al.
Published: (2024)

Reinforcement Learning for Machine Learning Engineering Agents
by: Yang, Sherry, et al.
Published: (2025)

Forgetting by Pruning: Data Deletion in Join Cardinality Estimation
by: He, Chaowei, et al.
Published: (2025)

Break the Tie: Learning Cluster-Customized Category Relationships for Categorical Data Clustering
by: Zhao, Mingjie, et al.
Published: (2025)

K-Score: Kalman Filter as a Principled Alternative to Reward Normalization in Reinforcement Learning
by: Xia, Zixuan, et al.
Published: (2026)

Categorical Data Clustering via Value Order Estimated Distance Metric Learning
by: Zhang, Yiqun, et al.
Published: (2024)

GAIA: Categorical Foundations of Generative AI
by: Mahadevan, Sridhar
Published: (2024)

E-CGL: An Efficient Continual Graph Learner
by: Guo, Jianhao, et al.
Published: (2024)

Provable Representation with Efficient Planning for Partial Observable Reinforcement Learning
by: Zhang, Hongming, et al.
Published: (2023)

High-order Regularization for Machine Learning and Learning-based Control
by: Liu, Xinghua, et al.
Published: (2025)

Multimodal Representation Learning Conditioned on Semantic Relations
by: Qiao, Yang, et al.
Published: (2025)

Robustness of AutoML on Dirty Categorical Data
by: Bueno, Marcos L. P., et al.
Published: (2026)

Is Optimal Transport Necessary for Inverse Reinforcement Learning?
by: Dong, Zixuan, et al.
Published: (2025)

Auxiliary Reward Generation with Transition Distance Representation Learning
by: Li, Siyuan, et al.
Published: (2024)

Bridging Philosophy and Machine Learning: A Structuralist Framework for Classifying Neural Network Representations
by: Culcu, Yildiz
Published: (2025)

Time-varying Interaction Graph ODE for Dynamic Graph Representation Learning
by: Wang, Xiaoyi, et al.
Published: (2026)

Should We Ever Prefer Decision Transformer for Offline Reinforcement Learning?
by: Omori, Yumi, et al.
Published: (2025)

Cardinality Estimation over Knowledge Graphs with Embeddings and Graph Neural Networks
by: Schwabe, Tim, et al.
Published: (2023)

DEER: A Delay-Resilient Framework for Reinforcement Learning with Variable Delays
by: Xia, Bo, et al.
Published: (2024)

Reinfier and Reintrainer: Verification and Interpretation-Driven Safe Deep Reinforcement Learning Frameworks
by: Yang, Zixuan, et al.
Published: (2024)

A Rational Model of Dimension-reduced Human Categorization
by: Hong, Yifan, et al.
Published: (2023)

Feature-Selective Representation Misdirection for Machine Unlearning
by: Chen, Taozhao, et al.
Published: (2025)

RL-BioAug: Label-Efficient Reinforcement Learning for Self-Supervised EEG Representation Learning
by: Lee, Cheol-Hui, et al.
Published: (2026)

Linear $Q$-Learning Does Not Diverge in $L^2$: Convergence Rates to a Bounded Set
by: Liu, Xinyu, et al.
Published: (2025)

TGM: a Modular and Efficient Library for Machine Learning on Temporal Graphs
by: Chmura, Jacob, et al.
Published: (2025)

MLAgentBench: Evaluating Language Agents on Machine Learning Experimentation
by: Huang, Qian, et al.
Published: (2023)

Transformers Learn to Implement Multi-step Gradient Descent with Chain of Thought
by: Huang, Jianhao, et al.
Published: (2025)

Harvesting Efficient On-Demand Order Pooling from Skilled Couriers: Enhancing Graph Representation Learning for Refining Real-time Many-to-One Assignments
by: Liang, Yile, et al.
Published: (2024)

Do Diffusion Models Learn Semantically Meaningful and Efficient Representations?
by: Liang, Qiyao, et al.
Published: (2024)

Explaining Categorical Feature Interactions Using Graph Covariance and LLMs
by: Shen, Cencheng, et al.
Published: (2025)