Parsimonious Learning-Augmented Algorithms

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


Duration: 44:10
335 views
7

Ravi Kumar (Google)
https://simons.berkeley.edu/talks/tbd-474
Quantifying Uncertainty: Stochastic, Adversarial, and Beyond

In this talk we will consider the number of predictions used by a learning-augmented algorithm as a resource measure. Focusing on two problems---online learning in the regret setting and online caching in the competitive-ratio setting---we show that a sublinear number of predictions suffices to achieve non-trivial performance gains.



Other Videos By Simons Institute for the Theory of Computing


2022-09-17Generalization and Robustness in Offline Reinforcement Learning
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


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