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A Tutorial on Doubly Robust Learning for Causal Inference
Doubly robust learning offers a robust framework for causal inference from observational data by integrating propensity score and outcome modeling.
Visual processing in context of reinforcement learning
This method needs only state-reward pairs from the environment for learning the representation.
Reward prediction for representation learning and reward shaping
One of the fundamental challenges in reinforcement learning (RL) is the one of data efficiency: modern algorithms require a very large number of training samples, especially compared to humans, for solving environments with high-dimensional observations.
Latent Representation Prediction Networks
The prediction function is used as a forward model for search on a graph in a viewpoint-matching task and the representation learned to maximize predictability is found to outperform a pre-trained representation.
Measuring the Data Efficiency of Deep Learning Methods
The algorithms are trained on different-sized subsets of the MNIST and Omniglot data sets.
Learning gradient-based ICA by neurally estimating mutual information
Several methods of estimating the mutual information of random variables have been developed in recent years.
Gradient-based Training of Slow Feature Analysis by Differentiable Approximate Whitening
We propose Power Slow Feature Analysis, a gradient-based method to extract temporally slow features from a high-dimensional input stream that varies on a faster time-scale, as a variant of Slow Feature Analysis (SFA) that allows end-to-end training of arbitrary differentiable architectures and thereby significantly extends the class of models that can effectively be used for slow feature extraction.
