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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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Evaluating Pharmaceutical Marketing to Physicians with Panel Data 12/04/2025

Theory of the firm in the era of Agents 12/04/2025

Large Language Models: An Applied Econometric Framework 12/04/2025

Evaluating the World Model Implicit in a Generative Model 12/04/2025

Machine Learning for Hypothesis Generation in Social Science 11/04/2025

Active Learning for Moral Preference Elicitation: Challenges and Nuances 11/04/2025

Gradient-Based Surveys for Nonparametric Discrete Choice Experiments 11/04/2025

Explainable Data-driven Share-of-choice Product Line Design Optimization 11/04/2025

The More You Ask, the Less You Get: When Additional Questions Hurt External Validity 11/04/2025

Conjoint topics from Handbook of Marketing Analytics: Methods and Applications 11/04/2025

Choice-Based Conjoint Analysis: Methods and Applications 11/04/2025

Beyond Conjoint Analysis: The Future of Preference Measurement 11/04/2025

An Optimization Framework for Adaptive Questionnaire Design 11/04/2025

Adaptive Self-Explication of Multiattribute Preferences 11/04/2025

Conjoint Analysis: Methods, Applications, and Recent Developments 11/04/2025

Current Issues and a “Wish List” for Conjoint Analysis 11/04/2025

Ellipsoidal Methods for Adaptive Choice-Based Conjoint Analysis 11/04/2025

Adaptive Polyhedral Methods for Conjoint Analysis 11/04/2025

MSL: Enhancing LLM Recommenders via Masked Softmax Loss 11/04/2025

Self-Supervised Deep Reinforcement Learning for Optimal Question Ranking 11/04/2025

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