IFML Seminar
IFML Seminar: 11/21/25 - Learning Dynamics in Multiplayer Games
Abstract: Intelligence often emerges through interaction and competition. Likewise, advanced AI algorithms often rely on competing learning objectives. Whether through data sampling, environmental interaction, or self-play, agents iteratively adapt their strategies in pursuit of...
Upcoming Events
- November2112:15 - 1:15pm
IFML Seminar
Event DetailsAbstract: Intelligence often emerges through interaction and competition. Likewise, advanced AI algorithms often rely on competing learning objectives.…
Past Events
- November13throughNovember15Event Details
Join us for UT Austin’s Year of AI celebration as we showcase the best ideas, innovations and inspiration in the...
- November112:15 - 1 pm
IFML Seminar
Event DetailsSpeaker Bio: Cristopher Moore received his B.A. in Physics, Mathematics, and Integrated Science from Northwestern University, and his Ph.D. in...
- October2512:15 - 1 pm
IFML Seminar
Event DetailsAbstract: Reinforcement learning often faces a trade-off between model flexibility and computational tractability. Flexible models can capture complex…
- October1712:15 - 1 pm
IFML Seminar
Event DetailsAbstract: I will argue that deep networks work well because of a characteristic structure in the space of learnable tasks...
- October412:15 - 1 pm
IFML Seminar
Event DetailsAbstract: Sequential decision-making (SDM) is crucial for adapting machine learning to dynamic real-world scenarios such as fluctuating markets or evolving…
- September2712:15 - 1 pm
IFML Seminar
Event DetailsAbstract: One of the most natural approaches to reinforcement learning (RL) with function approximation is value iteration, which inductively generates...
