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Found 10 papers, 0 papers with code
Efficient and Parsimonious Agnostic Active Learning
We develop a new active learning algorithm for the streaming setting satisfying three important properties: 1) It provably works for any classifier representation and classification problem including those with severe noise.
Scalable Non-linear Learning with Adaptive Polynomial Expansions
Can we effectively learn a nonlinear representation in time comparable to linear learning?
Contrastive Learning Using Spectral Methods
In many natural settings, the analysis goal is not to characterize a single data set in isolation, but rather to understand the difference between one set of observations and another.
Learning Mixtures of Tree Graphical Models
We consider unsupervised estimation of mixtures of discrete graphical models, where the class variable is hidden and each mixture component can have a potentially different Markov graph structure and parameters over the observed variables.
Identifiability and Unmixing of Latent Parse Trees
This paper explores unsupervised learning of parsing models along two directions.
A Spectral Algorithm for Latent Dirichlet Allocation
This work provides a simple and efficient learning procedure that is guaranteed to recover the parameters for a wide class of topic models, including Latent Dirichlet Allocation (LDA).
Stochastic convex optimization with bandit feedback
This paper addresses the problem of minimizing a convex, Lipschitz function $f$ over a convex, compact set $X$ under a stochastic bandit feedback model.
Spectral Methods for Learning Multivariate Latent Tree Structure
The setting is one where we only have samples from certain observed variables in the tree, and our goal is to estimate the tree structure (i. e., the graph of how the underlying hidden variables are connected to each other and to the observed variables).
Agnostic Active Learning Without Constraints
We present and analyze an agnostic active learning algorithm that works without keeping a version space.
A Parameter-free Hedging Algorithm
Previous algorithms for learning in this framework have a tunable learning rate parameter, and a major barrier to using online-learning in practical applications is that it is not understood how to set this parameter optimally, particularly when the number of actions is large.
