Best AI papers explained
A podcast by Enoch H. Kang
427 Episodes
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Reward Shaping from Confounded Offline Data
Published: 5/25/2025 -
Trajectory Bellman Residual Minimization: A Simple Value-Based Method for LLM Reasoning
Published: 5/25/2025 -
Understanding Best-of-N Language Model Alignment
Published: 5/25/2025 -
Maximizing Acquisition Functions for Bayesian Optimization - and its relation to Gradient Descent
Published: 5/24/2025 -
Bayesian Prompt Ensembles: Model Uncertainty Estimation for Black-Box Large Language Models
Published: 5/24/2025 -
Prompting Strategies for Enabling Large Language Models to Infer Causation from Correlation
Published: 5/24/2025 -
The Parallel Knowledge Gradient Method for Batch Bayesian Optimization
Published: 5/24/2025 -
FunBO: Discovering Acquisition Functions for Bayesian Optimization with FunSearch
Published: 5/24/2025 -
Automated Social Science: A Structural Causal Model-Based Approach
Published: 5/24/2025 -
Causal Interpretation of Transformer Self-Attention
Published: 5/24/2025 -
A Causal World Model Underlying Next Token Prediction: Exploring GPT in a Controlled Environment
Published: 5/24/2025 -
Trace is the Next AutoDiff: Generative Optimization with Rich Feedback, Execution Traces, and LLMs
Published: 5/24/2025 -
Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation
Published: 5/24/2025 -
Prompts from Reinforcement Learning (PRL)
Published: 5/24/2025 -
Logits are All We Need to Adapt Closed Models
Published: 5/24/2025 -
Large Language Models Are (Bayesian) Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning
Published: 5/23/2025 -
Inference-Time Intervention: Eliciting Truthful Answers from a Language Model
Published: 5/23/2025 -
From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models
Published: 5/23/2025 -
LLM In-Context Learning as Kernel Regression
Published: 5/23/2025 -
Personalizing LLMs via Decode-Time Human Preference Optimization
Published: 5/23/2025
Cut through the noise. We curate and break down the most important AI papers so you don’t have to.