Multi-armed Bandits in Production at Stitch Fix
Explore scaling challenges and solutions for multi-armed bandits in online experimentation, including a new method for efficient, deterministic Thompson sampling at Stitch Fix.
Probability
Explore Bayes Theorem, Hidden Markov Models, Shannon Entropy, Naive Bayes classifier, Beta distribution, and Thompson sampling in this friendly introduction to key probability concepts.
Machine Learning
Comprehensive introduction to key machine learning concepts, algorithms, and applications, covering testing, error metrics, recommendation systems, and various classification methods.
Planning and Learning in Risk-Aware Restless Multi-Arm Bandit
Explore risk-aware restless multi-arm bandits, indexability conditions, and Thompson sampling for bounded regret in resource distribution across Markov decision processes.
Deep Bayesian Bandits - Exploring in Online Personalized Recommendations
Explore online personalized recommendations using Deep Bayesian Bandits. Learn about UCB, Thompson Sampling, and innovative approaches like bootstrapping and dropout methods for effective exploration strategies.
Online Learning and Bandits - Part 2
Explore advanced bandit algorithms, including Exponential Weights, UCB, and Thompson Sampling. Learn design principles and analysis techniques for optimal decision-making in uncertain environments.
2019 ADSI Summer Workshop- Algorithmic Foundations of Learning and Control
Explore advanced techniques in reinforcement learning, focusing on randomized value functions and their applications in exploration strategies for complex decision-making problems.
Bandits - Kevin Jamieson - University of Washington
Explore bandit algorithms and their applications in machine learning, from drug development to content recommendation, with a focus on regret minimization and stochastic models.
Applying Active Learning in Drug Discovery - There's No Free Lunch, but You Can Get a Discount
Explore active learning applications in drug discovery, from prioritizing molecules for synthesis to virtual screening of large libraries, with insights on machine learning's impact across the process.
Adversarially Robust Stochastic Multi-Armed Bandits
Explore adversarially robust stochastic multi-armed bandits for optimal experimental design. Learn about algorithms balancing fast and robust learning in real-world applications like digital advertising.
Advanced Techniques for Online Advertisement Spend Optimization
Explore advanced techniques for optimizing online ad spend using Reinforcement Learning, Bayesian statistics, and Thompson sampling to improve budget allocation and maximize returns.
Adaptive Data Collection via Autoregressive Generation in News Recommendation Systems
Discover how autoregressive models can enhance adaptive data collection and decision-making through Thompson sampling, with applications in news recommendation systems and cold-start problems.