An Information-Theoretic Approach to Understanding Transformers' In-Context Learning of Variable-Order Markov Chains
Fuente:
arXiv
Guardado en:
| Autores principales: | Zhou, Ruida, Tian, Chao, Diggavi, Suhas |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Robust Federated Personalised Mean Estimation for the Gaussian Mixture Model
por: Managoli, Malhar A., et al.
Publicado: (2025)
por: Managoli, Malhar A., et al.
Publicado: (2025)
ADEPT: Hierarchical Bayes Approach to Personalized Federated Unsupervised Learning
por: Ozkara, Kaan, et al.
Publicado: (2024)
por: Ozkara, Kaan, et al.
Publicado: (2024)
On the Training Convergence of Transformers for In-Context Classification of Gaussian Mixtures
por: Shen, Wei, et al.
Publicado: (2024)
por: Shen, Wei, et al.
Publicado: (2024)
SPIRE: Conditional Personalization for Federated Diffusion Generative Models
por: Ozkara, Kaan, et al.
Publicado: (2025)
por: Ozkara, Kaan, et al.
Publicado: (2025)
From Markov to Laplace: How Mamba In-Context Learns Markov Chains
por: Bondaschi, Marco, et al.
Publicado: (2025)
por: Bondaschi, Marco, et al.
Publicado: (2025)
An Information-Theoretic Analysis of In-Context Learning
por: Jeon, Hong Jun, et al.
Publicado: (2024)
por: Jeon, Hong Jun, et al.
Publicado: (2024)
Reframing Data Value for Large Language Models Through the Lens of Plausibility
por: Rammal, Mohamad Rida, et al.
Publicado: (2024)
por: Rammal, Mohamad Rida, et al.
Publicado: (2024)
Information-Theoretic Framework for Understanding Modern Machine-Learning
por: Feder, Meir, et al.
Publicado: (2025)
por: Feder, Meir, et al.
Publicado: (2025)
On the optimal regret of collaborative personalized linear bandits
por: Huang, Bruce, et al.
Publicado: (2025)
por: Huang, Bruce, et al.
Publicado: (2025)
Context Channel Capacity: An Information-Theoretic Framework for Understanding Catastrophic Forgetting
por: Cheng, Ran
Publicado: (2026)
por: Cheng, Ran
Publicado: (2026)
Information-Theoretic Measures on Lattices for High-Order Interactions
por: Liu, Zhaolu, et al.
Publicado: (2024)
por: Liu, Zhaolu, et al.
Publicado: (2024)
Information-Theoretic State Variable Selection for Reinforcement Learning
por: Westphal, Charles, et al.
Publicado: (2024)
por: Westphal, Charles, et al.
Publicado: (2024)
Attention with Markov: A Framework for Principled Analysis of Transformers via Markov Chains
por: Makkuva, Ashok Vardhan, et al.
Publicado: (2024)
por: Makkuva, Ashok Vardhan, et al.
Publicado: (2024)
Directed Information $γ$-covering: An Information-Theoretic Framework for Context Engineering
por: Huang, Hai
Publicado: (2025)
por: Huang, Hai
Publicado: (2025)
Common Information Dimension
por: Hanna, Osama, et al.
Publicado: (2023)
por: Hanna, Osama, et al.
Publicado: (2023)
Cost-Aware Optimal Pairwise Pure Exploration
por: Wu, Di, et al.
Publicado: (2025)
por: Wu, Di, et al.
Publicado: (2025)
Harnessing the Power of Federated Learning in Federated Contextual Bandits
por: Shi, Chengshuai, et al.
Publicado: (2023)
por: Shi, Chengshuai, et al.
Publicado: (2023)
Information Theoretic Perspective on Representation Learning
por: Pereg, Deborah, et al.
Publicado: (2026)
por: Pereg, Deborah, et al.
Publicado: (2026)
Provable Policy Gradient Methods for Average-Reward Markov Potential Games
por: Cheng, Min, et al.
Publicado: (2024)
por: Cheng, Min, et al.
Publicado: (2024)
On the Generalization for Transfer Learning: An Information-Theoretic Analysis
por: Wu, Xuetong, et al.
Publicado: (2022)
por: Wu, Xuetong, et al.
Publicado: (2022)
Near-Optimal Clustering in Mixture of Markov Chains
por: Lee, Junghyun, et al.
Publicado: (2025)
por: Lee, Junghyun, et al.
Publicado: (2025)
Staggered Quantizers for Perfect Perceptual Quality: A Connection between Quantizers with Common Randomness and Without
por: Zhou, Ruida, et al.
Publicado: (2024)
por: Zhou, Ruida, et al.
Publicado: (2024)
Information Theoretic Learning for Diffusion Models with Warm Start
por: Shen, Yirong, et al.
Publicado: (2025)
por: Shen, Yirong, et al.
Publicado: (2025)
Improved Information Theoretic Generalization Bounds for Distributed and Federated Learning
por: Barnes, L. P., et al.
Publicado: (2022)
por: Barnes, L. P., et al.
Publicado: (2022)
Information-Theoretic Minimax Regret Bounds for Reinforcement Learning based on Duality
por: Bongole, Raghav, et al.
Publicado: (2024)
por: Bongole, Raghav, et al.
Publicado: (2024)
Information-Theoretic Discrete Diffusion
por: Jeon, Moongyu, et al.
Publicado: (2025)
por: Jeon, Moongyu, et al.
Publicado: (2025)
An Information Theoretic Perspective on Conformal Prediction
por: Correia, Alvaro H. C., et al.
Publicado: (2024)
por: Correia, Alvaro H. C., et al.
Publicado: (2024)
An Information-Theoretic Analysis of Temporal GNNs
por: Farzaneh, Amirmohammad
Publicado: (2024)
por: Farzaneh, Amirmohammad
Publicado: (2024)
Information-Theoretic Proofs for Diffusion Sampling
por: Reeves, Galen, et al.
Publicado: (2025)
por: Reeves, Galen, et al.
Publicado: (2025)
On Causality in Domain Adaptation and Semi-Supervised Learning: an Information-Theoretic Analysis for Parametric Models
por: Wu, Xuetong, et al.
Publicado: (2022)
por: Wu, Xuetong, et al.
Publicado: (2022)
On the Generalization of Knowledge Distillation: An Information-Theoretic View
por: Li, Bingying, et al.
Publicado: (2026)
por: Li, Bingying, et al.
Publicado: (2026)
Understanding Encoder-Decoder Structures in Machine Learning Using Information Measures
por: Silva, Jorge F., et al.
Publicado: (2024)
por: Silva, Jorge F., et al.
Publicado: (2024)
Information-Theoretic Generalization Bounds for Deep Neural Networks
por: He, Haiyun, et al.
Publicado: (2024)
por: He, Haiyun, et al.
Publicado: (2024)
Unifying Information-Theoretic and Pair-Counting Clustering Similarity
por: Gates, Alexander J.
Publicado: (2025)
por: Gates, Alexander J.
Publicado: (2025)
Transformers on Markov Data: Constant Depth Suffices
por: Rajaraman, Nived, et al.
Publicado: (2024)
por: Rajaraman, Nived, et al.
Publicado: (2024)
Broadcast Channel Cooperative Gain: An Operational Interpretation of Partial Information Decomposition
por: Tian, Chao, et al.
Publicado: (2025)
por: Tian, Chao, et al.
Publicado: (2025)
Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective
por: Laakom, Firas, et al.
Publicado: (2025)
por: Laakom, Firas, et al.
Publicado: (2025)
On Theoretical Limits of Learning with Label Differential Privacy
por: Zhao, Puning, et al.
Publicado: (2025)
por: Zhao, Puning, et al.
Publicado: (2025)
Information Theoretic Guarantees For Policy Alignment In Large Language Models
por: Mroueh, Youssef
Publicado: (2024)
por: Mroueh, Youssef
Publicado: (2024)
Information-Theoretic Thresholds for the Alignments of Partially Correlated Graphs
por: Huang, Dong, et al.
Publicado: (2024)
por: Huang, Dong, et al.
Publicado: (2024)
Ejemplares similares
-
Robust Federated Personalised Mean Estimation for the Gaussian Mixture Model
por: Managoli, Malhar A., et al.
Publicado: (2025) -
ADEPT: Hierarchical Bayes Approach to Personalized Federated Unsupervised Learning
por: Ozkara, Kaan, et al.
Publicado: (2024) -
On the Training Convergence of Transformers for In-Context Classification of Gaussian Mixtures
por: Shen, Wei, et al.
Publicado: (2024) -
SPIRE: Conditional Personalization for Federated Diffusion Generative Models
por: Ozkara, Kaan, et al.
Publicado: (2025) -
From Markov to Laplace: How Mamba In-Context Learns Markov Chains
por: Bondaschi, Marco, et al.
Publicado: (2025)