ICML 2024 Past Other
ICML 2024 Workshop on In-Context Learning
ICML 2024 Workshop ICL
- Submission deadline
- May 28, 2024, 12:00 UTC imported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Auto-imported from the OpenReview venue record on 2026-06-10 — please verify and enrich (topics are keyword-guessed).
Accepted papers (39)
Fetched from OpenReview (v2) on 2026-06-10.
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A Theoretical Understanding of Self-Correction through In-context Alignment
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An In-Context Learning Theoretic Analysis of Chain-of-Thought
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Automatic Domain Adaptation by Transformers in In-Context Learning
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Can large language models explore in-context?
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Can LLMs predict the convergence of Stochastic Gradient Descent?
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Can Mamba In-Context Learn Task Mixtures?
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Can Transformers Solve Least Squares to High Precision?
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Cross-lingual QA: A Key to Unlocking In-context Cross-lingual Performance
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DETAIL: Task DEmonsTration Attribution for Interpretable In-context Learning
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Fast Training Dataset Attribution via In-Context Learning
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Fine-grained Analysis of In-context Linear Estimation: Data, Architecture, and Beyond
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Improve Temporal Awareness of LLMs for Domain-general Sequential Recommendation
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In-Context Generalization to New Tasks From Unlabeled Observation Data
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In-Context Learning from Training on Unstructured Data: The Role of Co-Occurrence, Positional Information, and Training Data Structure
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In-context learning in presence of spurious correlations
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In-Context Learning of Energy Functions
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In-Context Principle Learning from Mistakes
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In-Context Reinforcement Learning Without Optimal Action Labels
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In-Context Symmetries: Self-Supervised Learning through Contextual World Models
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Learning Fast and Slow: Representations for In-Context Weight Modulation
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Learning Task Representations from In-Context Learning
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Linear Transformers are Versatile In-Context Learners
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LLM Processes: Numerical Predictive Distributions Conditioned on Natural Language
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LLMs learn governing principles of dynamical systems, revealing an in-context neural scaling law
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Localized Zeroth-Order Prompt Optimization
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Many-shot In-Context Learning
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Many-Shot In-Context Learning in Multimodal Foundation Models
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Polynomial Regression as a Task for Understanding In-context Learning Through Finetuning and Alignment
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Probing the Decision Boundaries of In-context Learning in Large Language Models
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Prompt Optimization with EASE? Efficient Ordering-aware Automated Selection of Exemplars
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Retrieval & Fine-Tuning for In-Context Tabular Models
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TabMDA: Tabular Manifold Data Augmentation for Any Classifier using Transformers with In-context Subsetting
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Task Descriptors Help Transformers Learn Linear Models In-Context
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Transformers are Minimax Optimal Nonparametric In-Context Learners
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Transformers as Stochastic Optimizers
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Transformers Can Perform Distributionally-robust Optimisation through In-context Learning
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Transformers Learn Temporal Difference Methods for In-Context Reinforcement Learning
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Universal Self-Consistency for Large Language Models
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Verbalized Machine Learning: Revisiting Machine Learning with Language Models