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Found 8 papers, 0 papers with code
Learned Prioritization for Trading Off Accuracy and Speed
Users want natural language processing (NLP) systems to be both fast and accurate, but quality often comes at the cost of speed.
Imitation Learning by Coaching
However, it is important to note that these guarantees depend on how well the policy we found can imitate the oracle on the training data.
Simultaneously Leveraging Output and Task Structures for Multiple-Output Regression
In this paper, we present a multiple-output regression model that leverages the covariance structure of the functions (i. e., how the multiple functions are related with each other) as well as the conditional covariance structure of the outputs.
Co-regularized Multi-view Spectral Clustering
In many clustering problems, we have access to multiple views of the data each of which could be individually used for clustering.
Message-Passing for Approximate MAP Inference with Latent Variables
We consider a general inference setting for discrete probabilistic graphical models where we seek maximum a posteriori (MAP) estimates for a subset of the random variables (max nodes), marginalizing over the rest (sum nodes).
Learning Multiple Tasks using Manifold Regularization
We present a novel method for multitask learning (MTL) based on {\it manifold regularization}: assume that all task parameters lie on a manifold.
Co-regularization Based Semi-supervised Domain Adaptation
This paper presents a co-regularization based approach to semi-supervised domain adaptation.
Multi-Label Prediction via Sparse Infinite CCA
Canonical Correlation Analysis (CCA) is a useful technique for modeling dependencies between two (or more) sets of variables.
