Asia
Understanding Model Selection for Learning in Strategic Environments
The deployment of ever-larger machine learning models reflects a growing consensus that the more expressive the model class one optimizes over--and the more data one has access to--the more one can improve performance. As models get deployed in a variety of real-world scenarios, they inevitably face strategic environments.
Semi-Synthetic Transformers for Evaluating Mechanistic Interpretability Techniques
Training (IIT) which we call Strict IIT (SIIT). SIIT models maintain Tracr's original circuit while being more realistic.
BEACON: Benchmark for Comprehensive RNA Tasks and Language Models
RNA plays a pivotal role in translating genetic instructions into functional outcomes, underscoring its importance in biological processes and disease mechanisms.
Learning Equilibria in Adversarial Team Markov Games: A Nonconvex-Hidden-Concave Min-Max Optimization Problem
The joint decisions of the agents influence both individual rewards and the transition of the environment. MARL in general is occupied with leading the multi-agent system to a favorable outcome. Through the lens of game theory, the notion of a "favorable outcome" is formally defined through concepts like a Nash
Optimizing Prompts for Text-to-Image Generation Y aru Hao
Well-designed prompts can guide text-to-image models to generate amazing images.
