Active learning for Big Data

• tour contains investigations on recommendations of tours, migrated to a new repository.
• binary_reward contains a summary of theoretical results about kl-UCB and KL-UCB, which is implemented in python.
• hmm_bandit contains a summary of ideas for using hidden Markov models for recommendation

There are three ideas which are often used for eliciting human responses using machine learning predictors. At a high level they are similar is spirit, but they have different foundations which lead to different formulations. The ideas are active learning, bandits and experimental design. Related to this but with literature from a different field is social choice theory, which looks at how individual preferences are aggregated.

Active learning considers the setting where the agent interacts with its environment to procure a training set, rather than passively receiving i.i.d. samples from some underlying distribution.

It is often assumed that the environment is infinite (e.g. $R^d$) and the agent has to choose a location, $x$, to query. The oracle then returns the label $y$. It is often assumed that there is no noise in the label, and hence there is no benefit of querying the same point $x$ again. In many practical applications, the environment is considered to be finite (but large). This is called the pool-based active learning.

The active learning algorithm is often compared to the passive learning algorithm.

A bandit problem is a sequential allocation problem defined by a set of actions. The agent chooses an action at each time step, and the environment returns a reward. The aim of the agent is to maximise reward.

In basic settings, the set of actions is considered to be finite. There are three fundamental formalisations of the bandit problem, depending on the assumed nature of the reward process: stochastic, adversarial and Markovian. In all three settings the reward is uncertain, and hence the agent may have to play a particular action repeatedly.

The agent is compared to a static agent which has played the best action. This difference in reward is called regret.

In contrast to active learning, experimental design considers the problem of regression, i.e. where the label $y\in R$ is a real number.

The problem to be solved in experimental design is to choose a set of trials (say of size N) to gather enough information about the object of interest. The goal is to maximise the information obtained about the parameters of the model (of the object).

It is often assumed that the observations at the N trials are independent. When N is finite this is called exact design, otherwise it is called approximate or continuous design. The environment is assumed to be infinite (e.g. $R^d$) and the observations are scalar real variables.

• Thompson sampling
• Upper Confidence Bound

In the special case when the rewards of the arms are {0,1}, we can get much tighter analysis. See pymaBandits. This is also implemented in this repository under python/digbeta.

Spectral Bandits for Smooth Graph Functions Michal Valko, Remi Munos, Branislav Kveton, Tomas Kocak ICML 2014

Study bandit problem where the arms are the nodes of a graph and the expected payoff of pulling an arm is a smooth function on this graph.

Assume that the graph is known, and its edges represent the similarities of the nodes. At time $t$, choose a node and observe its payoff. Based on the payoff, update model.

Assume that number of nodes $N$ is large, and interested in the regime $t < N$.

This is an unsorted list of references.

• Prediction, Learning, and Games, Nicolo Cesa-Bianchi, Gabor Lugosi Cambridge University Press, 2006

• Active Learning Literature Survey Burr Settles Computer Sciences Technical Report 1648 University of Wisconsin–Madison, 2010

• Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems Sebastien Bubeck, Nicolo Cesa-Bianchi Foundations and Trends in Machine Learning, Vol 5, No 1, 2012, pp. 1-122

• Spectral Bandits for Smooth Graph Functions Michal Valko, Remi Munos, Branislav Kveton, Tomas Kocak ICML 2014

• Spectral Thompson Sampling Tomas Kocak, Michal Valko, Remi Munos, Shipra Agrawal AAAI 2014

• An Analysis of Active Learning Strategies for Sequence Labeling Tasks Burr Settles, Mark Craven EMNLP 2008

• Margin-based active learning for structured predictions Kevin Small, Dan Roth International Journal of Machine Learning and Cybernetics, 2010, 1:3-25

• Emilie Kaufmann, Nathaniel Korda and Remi Munos Thompson Sampling: An Asymptotically Optimal Finite Time Analysis, ALT 2012

• Thompson Sampling for 1-Dimensional Exponential Family Bandits Nathaniel Korda, Emilie Kaufmann, Remi Munos NIPS 2013

• On Bayesian Upper Confidence Bounds for Bandit Problems Emilie Kaufmann, Olivier Cappe, Aurelien Garivier AISTATS 2012

• Building Bridges: Viewing Active Learning from the Multi-Armed Bandit Lens Ravi Ganti, Alexander G. Gray UAI 2013

• From Theories to Queries: Active Learning in Practice Burr Settles JMLR W&CP, NIPS 2011 Workshop on Active Learning and Experimental Design

• Contextual Gaussian Process Bandit Optimization. Andreas Krause, Cheng Soon Ong NIPS 2011

• Contextual Bandit for Active Learning: Active Thompson Sampling. Djallel Bouneffouf, Romain Laroche, Tanguy Urvoy, Raphael Feraud, Robin Allesiardo. NIPS 2014

• Towards Anytime Active Learning: Interrupting Experts to Reduce Annotation Costs Maria Ramirez-Loaiza, Aron Culotta, Mustafa Bilgic SIGKDD 2013

• Actively Learning Ontology Matching via User Interaction Feng Shi, Juanzi Li, Jie Tang, Guotong Xie, Hanyu Li ISWC 2009

• A Novel Method for Measuring Semantic Similarity for XML Schema Matching Buhwan Jeong, Daewon Lee, Hyunbo Cho, Jaewook Lee Expert Systems with Applications 2008

• Tamr Product White Paper http://www.tamr.com/tamr-technical-overview/

• Design of Experiments in Nonlinear Models Luc Pronzato, Andrej Pazman Springer 2013

• Optimisation in space of measures and optimal design Ilya Molchanov and Sergei Zuyev ESAIM: Probability and Statistics, Vol. 8, pp. 12-24, 2004

• Active Learning for logistic regression: an evaluation Andrew I. Schein and Lyle H. Ungar Machine Learning, 2007, 68: 235-265

• Learning to Optimize Via Information-Directed Sampling Daniel Russo and Benjamin Van Roy

• The KL-UCB Algorithm for Bounded Stochastic Bandits and Beyond Aurelien Garivier and Olivier Cappe COLT 2011

• A Finite-Time Analysis of Multi-armed Bandits Problems with Kullback-Leibler Divergences Odalric-Ambrym Maillard, Remi Munos, Gilles Stoltz COLT 2011

• Kullback-Leibler Upper Confidence Bounds for Optimal Sequential Allocation Olivier Cappe, Aurelien Garivier, Odalric-Ambrym Maillard, Remi Munos, Gilles Stoltz Annals of Statistics, 2013