Safe and Fair Reinforcement Learning Video playback requires cookie consent 2019年10月3日 Philip Thomas | UMass Amherst Reinforcement Learning Day 2019: Safe and Fair Reinforcement Learning (在新选项卡中打开) 相关链接 研究领域 Artificial intelligence 研究院 Microsoft Research Lab - New York City 组 Reinforcement Learning 活动 Reinforcement Learning Day 2019 接下来观看 Expanding Flows for Fast and Flexible Generation Beyond the Fixed Canvas July 21, 2026 Sophia Tang Microsoft AI for Good Lab - Introduction to HASTE July 17, 2026 Juan M. Lavista Ferres, Caleb Robinson, Cameron Birge 等。 Learning Genetic Perturbation Effects at Single-Cell Resolution for Virtual Cells July 14, 2026 Jiaqi Zhang Convergence Analysis for Fast High-Order ODE Solvers in Diffusion Probabilistic Models July 7, 2026 Zhengjiang Lin Reinforce Adjoint Matching: Scaling Diffusion RL June 30, 2026 Andreas Bergmeister Plenary Talk 1: Navigating the AI Horizon: Promises, Perils, and the Power of Collaboration June 9, 2026 Ece Kamar, Srinivasan Iyengar Welcome Session - Microsoft Research India Academic Summit 2026 June 9, 2026 Venkat Padmanabhan, Srinivasan Iyengar Paza Playbook Walkthrough March 23, 2026 Mercy Muchai Atlas Playbook Walkthrough February 5, 2026 Arnab Paul Choudhury Vibhasha Playbook Walkthrough February 5, 2026 Prashant Kodali
Microsoft AI for Good Lab - Introduction to HASTE July 17, 2026 Juan M. Lavista Ferres, Caleb Robinson, Cameron Birge 等。
Learning Genetic Perturbation Effects at Single-Cell Resolution for Virtual Cells July 14, 2026 Jiaqi Zhang
Convergence Analysis for Fast High-Order ODE Solvers in Diffusion Probabilistic Models July 7, 2026 Zhengjiang Lin
Plenary Talk 1: Navigating the AI Horizon: Promises, Perils, and the Power of Collaboration June 9, 2026 Ece Kamar, Srinivasan Iyengar
Welcome Session - Microsoft Research India Academic Summit 2026 June 9, 2026 Venkat Padmanabhan, Srinivasan Iyengar