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Efficient Online Learning via Randomized Rounding
Efficient Online Learning Randomized Rounding
2011/7/6
Most online algorithms used in machine learning today are based on variants of mirror descent or follow-the-leader.
Efficient Optimal Learning for Contextual Bandits
Efficient Optimal Learning Contextual Bandits
2011/7/6
We address the problem of learning in an online setting where the learner repeatedly observes features, selects among a set of actions, and receives reward for the action taken.
Bayesian and L1 Approaches to Sparse Unsupervised Learning
Bayesian L1 Approaches Sparse Unsupervised Learning
2011/7/6
The use of L1 regularisation for sparse learning has generated immense research interest, with successful application in such diverse areas as signal acquisition, image coding, genomics and collaborat...
ProDiGe: PRioritization Of Disease Genes with multitask machine learning from positive and unlabeled examples
ProDiGe PRioritization Disease Genes multitask machine learning positive unlabeled examples
2011/7/6
Elucidating the genetic basis of human diseases is a central goal of genetics and molecular biology. While traditional linkage analysis and modern high-throughput techniques often provide long lists o...
Bayesian multitask inverse reinforcement learning
Bayesian inference multitask learning inverse reinforce-ment learning
2011/7/6
We generalise the problem of inverse reinforcement learning to multiple tasks, from a set of demonstrations. Each demonstration may represent one expert trying to solve a different task.
In this paper, we first demonstrate that b-bit minwise hashing, whose estimators are positive definite kernels, can be naturally integrated with learning algorithms such as SVM and logistic regression...
Identifying Hosts of Families of Viruses: A Machine Learning Approach
viral host machine learning adaboost alternating decision tree mismatch k-mers
2011/6/21
Identifying viral pathogens and characterizing their transmission is essential to developing effective
public health measures in response to a pandemic. Phylogenetics, though currently the most popul...
Density Estimation and Classification via Bayesian Nonparametric Learning of Affine Subspaces
Dimension reduction Classi er Variable selection Nonparametric Bayes
2011/6/20
It is now practically the norm for data to be very high dimensional in areas such as genetics, machine
vision, image analysis and many others. When analyzing such data, parametric models are often to...
From Agreement to Asymptotic Learning
Agreement Asymptotic Learning Bayesian agents asymptotic learning communication model
2011/6/20
Since Aumann's Agreement Theorem [3], the study of the exchange of information between
Bayesian agents has resulted in broad theoretical insight into the phenomenon of agreement
and the dynamics tha...
State-Observation Sampling and the Econometrics of Learning Models
Hidden Markov model particle filter state-observation sampling learning indirect inference forecasting state space model value at risk
2011/6/20
In nonlinear state-space models, sequential learning about the hidden state can proceed
by particle filtering when the density of the observation conditional on the state is available
analytically (...
Self-configuration from a Machine-Learning Perspective
Machine-Learning Perspective Self-configuration Sequen-tial Parameter Optimization
2011/6/21
The goal of machine learning is to provide solutions which are trained by data
or by experience coming from the environment. Many training algorithms exist and
some brilliant successes were achieved...
Rapid Learning with Stochastic Focus of Attention
Rapid Learning Stochastic Attention MNIST data
2011/6/21
We present a method to stop the evaluation
of a decision making process when the result
of the full evaluation is obvious. This
trait is highly desirable for online marginbased
machine learning al...
Optimal Reinforcement Learning for Gaussian Systems
Optimal Reinforcement Learning Gaussian Systems
2011/7/5
The exploration-exploitation tradeoff is among the central challenges of reinforcement learning. A hypothetical exact Bayesian learner would provide the optimal solution, but is intractable in general...
Learning Item-Attribute Relationship in Q-Matrix Based Diagnostic Classification Models
Cognitive assessment consistency DINA model DINO model
2011/7/5
Recent surge of interests in cognitive assessment has led to the developments of novel statistical models for diagnostic classification. Central to many such models is the well-known Q-matrix, which s...
Sparse Signal Recovery with Temporally Correlated Source Vectors Using Sparse Bayesian Learning
Bayesian Learning Temporally Correlated Signal Recovery
2011/3/23
We address the sparse signal recovery problem in the context of multiple measurement vectors (MMV) when elements in each nonzero row of the solution matrix are temporally correlated. Existing algorith...