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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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Maximizing Acquisition Functions for Bayesian Optimization - and its relation to Gradient Descent 24/05/2025

Bayesian Prompt Ensembles: Model Uncertainty Estimation for Black-Box Large Language Models 24/05/2025

Prompting Strategies for Enabling Large Language Models to Infer Causation from Correlation 24/05/2025

The Parallel Knowledge Gradient Method for Batch Bayesian Optimization 24/05/2025

FunBO: Discovering Acquisition Functions for Bayesian Optimization with FunSearch 24/05/2025

Automated Social Science: A Structural Causal Model-Based Approach 24/05/2025

Causal Interpretation of Transformer Self-Attention 24/05/2025

A Causal World Model Underlying Next Token Prediction: Exploring GPT in a Controlled Environment 24/05/2025

Trace is the Next AutoDiff: Generative Optimization with Rich Feedback, Execution Traces, and LLMs 24/05/2025

Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation 24/05/2025

Prompts from Reinforcement Learning (PRL) 24/05/2025

Logits are All We Need to Adapt Closed Models 24/05/2025

Large Language Models Are (Bayesian) Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning 23/05/2025

Inference-Time Intervention: Eliciting Truthful Answers from a Language Model 23/05/2025

From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models 23/05/2025

LLM In-Context Learning as Kernel Regression 23/05/2025

Personalizing LLMs via Decode-Time Human Preference Optimization 23/05/2025

Almost Surely Safe LLM Inference-Time Alignment 23/05/2025

Survey of In-Context Learning Interpretation and Analysis 23/05/2025

From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models 23/05/2025

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