Interactive Machine Learning: Algorithms and Theory
CMPSCI 691E, Fall 2016
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Optional Reading
Active Learning
- Parametric
- Minimax bounds for active learning
- Active and passive learning of linear separators under log-concave distributions
- Better algorithms for selective sampling
- Nonparametric
- Hierarchical Sampling for Active Learning
- Disagreement-based
- CAL -- Improving Generalization with Active Learning + Daniel's note
- Agnostic Active Learning -- BBL
- Importance-weighted active learning
- DHM a General agnostic active learning algorithm.
- Overview
- Two faces of active learning
Bandit
- Classical
- The non-stochastic multi-armed bandit problem
- Regret analysis of stochastic and nonstochastic multi-armed bandit problems
- Contextual
- The epoch-greedy algorithm for contextual mulit-armed bandits
- Taming the monster: a fast and simple algorihtm for contextual bandits
- Parametric
- Improved algorithms for linear stochastic bandits
- Thompson Sampling
- Learning to optimize via Posterior Sampling
- GP-Bandits
- Gaussian Process Optimization in the Bandit Seting: No Regret and Experimental Design
- Markovian
- Mahajan and Teneketzis -- Multi-armed Bandit Problems
Reinforcement Learning
- Tabular
- Near-optimal reinforcement learning in polynomial time
- R-max: a general polynomial time algorithm for near-optimal reinforcement learning
- PAC model-free reinforcement learning
- Near-optimal regret bounds for reinforcement learning
- Generalization and Exploration via Randomized Value Functions
- Policy Gradient
- Policy gradient methods for reinforcement learning with function approximation
- Contextual-MDPS for PAC-reinforcement learning with rich observations
Unsupervised
- Adaptive Sensing
- Clustering
- Learning the crowd kernel
- Efficient Active algorithms for hierarchical clustering
- Clustering with interactive feedback
- Network Tomography
- Network tomography: recent developments
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