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Want to play with the technology yourself? Explore our interactive demo → https://ibm.biz/BdKSby Learn more about the ...

Time: Wednesday, Nov 19, 12:30-1:30 pm Speaker: Peter Frazier (Cornell University) Abstract: Optimization problems are ...

In many real-world applications, customers express their preferences via direct choices. Imagine we are interested in proposing ...

Ever wondered why your streaming service *always* recommends the same boring movies? Or how AI could *finally* understand ...

Lucas Maystre recently graduated with a PhD from the IC School at EPFL. He discusses his research on comparison-based ...

Starting today, Spellbook will remember how you review contracts and prioritize issues based on what matters most to you.

Direct Preference Optimization (DPO) to finetune LLMs without reinforcement learning. DPO was one of the two Outstanding Main ...

This academic paper introduces MiCRo, a two-stage framework designed to improve how Large Language Models (LLMs) learn ...

To learn more about enrolling in the graduate course, visit: ...

This is a recorded presentation by Ruiqi Wang for the paper, titled "Feedback-efficient Active Preference Learning for Socially ...

Preference Alignment / Preference Training Explained (Full Guide) In this video, I explain how Large Language Models learn ...

4th Applied Active Inference Symposium (November 13-15, 2024) Part 1: https://youtu.be/cIBIecj7UZE Part 2: ...

For more information about Stanford's Artificial Intelligence programs visit: https://stanford.io/ai Stanford CS234 Reinforcement ...

Direct Preference Optimization (DPO) is a method used for training Large Language Models (LLMs). DPO is a direct way to train ...

A rigorous guide to preference data, reward models, RLHF, DPO, and the limits of alignment optimization. In this LearnAI lesson ...

For more information about Stanford's online Artificial Intelligence programs visit: https://stanford.io/ai To learn more about ...