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Best AI papers explained

Por: Enoch H. Kang

Cut through the noise. We curate and break down the most important AI papers so you don’t have to.

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Latest episodes of the podcast Best AI papers explained

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Bayesian Concept Bottlenecks with LLM Priors 17/05/2025

Transformers for In-Context Reinforcement Learning 17/05/2025

Evaluating Large Language Models Across the Lifecycle 17/05/2025

Active Ranking from Human Feedback with DopeWolfe 16/05/2025

Optimal Designs for Preference Elicitation 16/05/2025

Dual Active Learning for Reinforcement Learning from Human Feedback 16/05/2025

Active Learning for Direct Preference Optimization 16/05/2025

Active Preference Optimization for RLHF 16/05/2025

Test-Time Alignment of Diffusion Models without reward over-optimization 16/05/2025

Test-Time Preference Optimization: On-the-Fly Alignment via Iterative Textual Feedback 16/05/2025

GenARM: Reward Guided Generation with Autoregressive Reward Model for Test-time Alignment 16/05/2025

Advantage-Weighted Regression: Simple and Scalable Off-Policy RL 16/05/2025

Can RLHF be More Efficient with Imperfect Reward Models? A Policy Coverage Perspective 16/05/2025

Transformers can be used for in-context linear regression in the presence of endogeneity 15/05/2025

Bayesian Concept Bottlenecks with LLM Priors 15/05/2025

In-Context Parametric Inference: Point or Distribution Estimators? 15/05/2025

Enough Coin Flips Can Make LLMs Act Bayesian 15/05/2025

Bayesian Scaling Laws for In-Context Learning 15/05/2025

Posterior Mean Matching Generative Modeling 15/05/2025

Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective 15/05/2025

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