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Found 7 papers, 0 papers with code
Online Learning with Dependent Stochastic Feedback Graphs
A general framework for online learning with partial information is one where feedback graphs specify which losses can be observed by the learner.
A Discriminative Technique for Multiple-Source Adaptation
We present a new discriminative technique for the multiple-source adaptation, MSA, problem.
Adaptive Region-Based Active Learning
We present a new active learning algorithm that adaptively partitions the input space into a finite number of regions, and subsequently seeks a distinct predictor for each region, both phases actively requesting labels.
Learning GANs and Ensembles Using Discrepancy
Generative adversarial networks (GANs) generate data based on minimizing a divergence between two distributions.
Fitting a deeply-nested hierarchical model to a large book review dataset using a moment-based estimator
The main challenge in deploying this model is computational: the data sizes are large, and fitting the model at scale using off-the-shelf maximum likelihood procedures is prohibitive.
Algorithms and Theory for Multiple-Source Adaptation
This work includes a number of novel contributions for the multiple-source adaptation problem.
Multiple-Source Adaptation for Regression Problems
We present a detailed theoretical analysis of the problem of multiple-source adaptation in the general stochastic scenario, extending known results that assume a single target labeling function.
