Amii

Rich Sutton’s new path for AI | Approximately Correct Podcast

Synergy of Graph Data Management & Machine Learning in Explainability & Query Answering, Arijit Khan

Rich Sutton, Toward a better Deep Learning

Tea Time Talks 2024: Shang Wang, Reinforcement Learning for Chip Design

Tea Time Talks: Farzane Aminmansour, AProp: Decentralized Gradient-Based Learning Algorithm for DNNs

Tea Time Talks 2024: Parham Panahi, Experience Selection in Deep RL

Tea Time Talks 2024: Alex Lewandowski, Continual Learning, Scalability, and Linearity

Tea Time Talks 2024: Aidan Bush, Multi-agent Deflection Routing with Bandits

Tea Time Talks 2024: Alireza Kazemipour, Optimism and Mon-MDPs

Tea Time Talks 2024: Mahshid Rahmani Hanzaki, Tile-coding for Count-based Exploration

Tea Time Talks 2024: Yiuqi Wang, Transformers Learn Temporal Difference Methods for In-Context RL

Tea Time Talks 2024: Bounding-Box Inference for Error-Aware Model-Based RL - Erin Talvitie

Lili Mou at ACL 2020 - Stylized Text Generation: Approaches and Applications (Tutorial)

DLRLSS 2019 - Optimization in DL - Jimmy Ba

AI Seminar Series 2024: Offline RL Needs Less Data if you Have Trajectories, Vlad Tkachuk

The Tea Time Talks: Andy Patterson, Objective Function Geometry for Learning Values (June 23)

The Tea Time Talks: Sarath Chandar, On Learning Long-term Dependencies in RNNs (July 24)