ICML 2011
TechTalks from event: ICML 2011
Tutorial: Introduction to Bandits: Algorithms and Theory
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Tutorial: Introduction to Bandits: Algorithms and TheoryThis tutorial intends to be an introduction to stochastic and adversarial multi-armed bandit algorithms and to survey some of the recent advances. In the multi-armed bandit problem, at each stage, an agent (or decision maker) chooses one action (or arm), and receives a reward from it. The agent aims at maximizing his rewards. Since he does not know the process generating the rewards, he needs to explore (try) the different actions and yet, exploit (concentrate its draws on) the seemingly most rewarding arms. The bandit problem has been increasingly popular in the machine learning community. It is the simplest setting where one encounters the exploration-exploitation dilemma. It has a wide range of applications including advertizement [1, 6], economics [2, 12], games [7] and optimization [10, 5, 9, 3], model selection and machine learning algorithms itself [13, 4]. It can be a central building block of larger systems, like in evolutionary programming [8] and reinforcement learning [14], in particular in large state space Markovian Decision Problems [11]. -
Tutorial: Introduction to Bandits: Algorithms and Theory - Part 2Continuation of Part 1
- All Sessions
- Keynotes
- Bandits and Online Learning
- Structured Output
- Reinforcement Learning
- Graphical Models and Optimization
- Recommendation and Matrix Factorization
- Neural Networks and Statistical Methods
- Invited Cross-Conference Track
- Feature Selection, Dimensionality Reduction
- Learning Theory
- Large-Scale Learning
- Latent-Variable Models
- Neural Networks and Deep Learning
- Active and Online Learning
- Latent-Variable Models
- Tutorial : Collective Intelligence and Machine Learning
- Tutorial: Learning Kernels
- Tutorial: Machine Learning for Large Scale Recommender Systems
- Tutorial: Introduction to Bandits: Algorithms and Theory
- Ensemble Methods
- Tutorial: Machine Learning and Robotics
- Tutorial: Machine Learning in Ecological Science and Environmental Policy
- Test-of-Time
- Best Paper
- Robotics and Reinforcement Learning
- Transfer Learning
- Kernel Methods
- Optimization
- Learning Theory
- Invited Cross-Conference Session
- Neural Networks and Deep Learning
- Reinforcement Learning
- Bayesian Inference and Probabilistic Models
- Supervised Learning
- Social Networks
- Evaluation Metrics
- statistical relational learning
- Outlier Detection
- Time Series
- Graphical Models and Bayesian Inference
- Sparsity and Compressed Sensing
- Clustering
- Game Theory and Planning and Control
- Semi-Supervised Learning
- Kernel Methods and Optimization
- Neural Networks and NLP
- Probabilistic Models & MCMC
- Online Learning
- Ranking and Information Retrieval