Machine Learning for Faster Optimization

Channel:
United States Simons Institute for the Theory of Computing
Subscribers:
68,700
Published on ● Video Link: https://www.youtube.com/watch?v=ANaEUtqCQl0


Duration: 44:16
1,068 views
26

Ben Moseley (Carnegie Mellon University)
https://simons.berkeley.edu/talks/tbd-475
Quantifying Uncertainty: Stochastic, Adversarial, and Beyond

This talk will discuss a model for augmenting algorithms with useful predictions to improve algorithm performance for running time. The model ensures predictions are formally learnable and robust. Learnability guarantees that predictions can be efficiently constructed from past data. Robustness formally ensures a prediction is robust to modest changes in the problem input. This talk will discuss predictions that satisfy these properties and result in improved run times for matching algorithms.



Other Videos By Simons Institute for the Theory of Computing


2022-09-17Adaptivity and Confounding in Multi-armed Bandit Experiments
2022-09-16When Can We Use Weak Function Approximation to Solve Large Scale Planning Problems in MDPs?
2022-09-16Dynamic Regret Minimization for Bandits without Prior Knowledge
2022-09-16Oracle-Efficient Online Learning or: How to Use Non-Robust Optimization for Robust Learning
2022-09-16Adaptive Monopoly Regulation
2022-09-16Information Collection Through Strategic Agents
2022-09-16Incentivized Exploration
2022-09-15Attributes: Selective Learning and Influence
2022-09-15Markov Persuasion Process and its Reinforcement Learning
2022-09-15Parsimonious Learning-Augmented Algorithms
2022-09-14Machine Learning for Faster Optimization
2022-09-14Flow Time Scheduling with Uncertain Processing Time
2022-09-14Best Of Both Worlds: Stochastic & Adversarial Best-Arm Identification
2022-09-14Best of Both World Algorithms from I.I.D. to Adversarial Data
2022-09-14Corruption-Robust Contextual Search
2022-09-14Contextual Inverse Optimization: Offline and Online Learning
2022-09-13Causal Inference in Complex Systems: Network Interference, Strategic Agents, and Beyond
2022-09-13Inference and Interference in Marketplace Experimentation
2022-09-13Causal Matrix Completion: Applications to Offline Causal Reinforcement Learning
2022-09-13Greedy Approximation Algorithms for Active Sequential Hypothesis Testing
2022-09-13Expert Advice in Complex Environments


Tags:
Simons Institute
theoretical computer science
UC Berkeley
Computer Science
Theory of Computation
Theory of Computing
Quantifying Uncertainty: Stochastic Adversarial and Beyond
Ben Moseley