Kernel-Based Methods for Bandit Convex Optimization

Channel:
United States Simons Institute for the Theory of Computing
Subscribers:
68,700
Published on ● Video Link: https://www.youtube.com/watch?v=fV4qd43OsY8


Duration: 58:30
4,173 views
37

Sebastien Bubeck, Microsoft Research
https://simons.berkeley.edu/talks/sebastien-bubeck-09-21-2016
Optimization and Decision-Making Under Uncertainty



Other Videos By Simons Institute for the Theory of Computing


2016-09-22Bandits and Agents: How to Incentivize Exploration?
2016-09-22A Unified Duality Theory for Bayesian Mechanism Design
2016-09-22Simple Pricing Schemes for Consumers with Evolving Values
2016-09-22Procrastination with Variable Present Bias
2016-09-22Avoiding Cascading Failures for Time-of-Use Pricing
2016-09-21Online Optimization and Learning Under Long-Term Convex Constraints and Objectives
2016-09-21Learning in Games with Best-Response Oracles
2016-09-21Distribution-Free Models of Social and Information Networks
2016-09-21Revisiting the Exploration-Exploitation Trade-Off in Bandit Models
2016-09-21Always Valid Inference: Continuous Monitoring of A/B Tests
2016-09-21Kernel-Based Methods for Bandit Convex Optimization
2016-09-20Online Algorithms for Covering and Packing Problems with Convex Objectives
2016-09-20Clustering Using Pairwise Comparisons
2016-09-20Online Vector Packing
2016-09-20Distributed Partial Clustering
2016-09-20From Predictions to Decisions: Limitations and Possibilities of Optimization from Samples
2016-09-20How to Predict When Estimation is Hard: Algorithms for Learning on Graphs
2016-09-19Smarter Tools for (Citi)Bike-Sharing
2016-09-19Interpolating Between Stochastic and Worst-case Optimization
2016-09-19The Simplex and Policy-Iteration Methods are Strongly Polynomial...
2016-09-19Stable Marriages in Metric Spaces


Tags:
Simons Institute
Theory of Computing
Theory of Computation
Theoretical Computer Science
Computer Science
UC Berkeley
Optimization and Decision-Making Under Uncertainty
Sebastien Bubeck