Vote Elicitation with Probabilistic Preference Models: Empirical Estimation and Cost Tradeoffs Tyler Lu and Craig Boutilier University of Toronto.
Lecture outline Nearest-neighbor search in low dimensions – kd-trees Nearest-neighbor search in high dimensions – LSH Applications to data mining.
Optimal Meshing
Optimal Nudging. Presentation UD.
Active learning lecture
CS 598AGB What simulations can tell us. Questions that simulations cannot answer Simulations are on finite data. Some questions (e.g., whether a method.
On Large-Scale Peer-to-Peer Streaming Systems with Network Coding Chen Feng, Baochun Li Dept. of Electrical and Computer Engineering University of Toronto.
Apprenticeship Learning Pieter Abbeel Stanford University In collaboration with: Andrew Y. Ng, Adam Coates, J. Zico Kolter, Morgan Quigley, Dmitri Dolgov,
7. Experiments 6. Theoretical Guarantees Let the local policy improvement algorithm be policy gradient. Notes: These assumptions are insufficient to give.
IC Research Seminar A Teaser Prof. Dr. Volkan Cevher LIONS/Laboratory for Information and Inference Systems lions@epfl.
Physics Based Formation Behavior in Autonomous Robots
Optimizing Sensing from Water to the Web
