Learning Graphical Models
Model-Free Learning for Two-Player Zero-Sum Partially Observable Markov Games with Perfect Recall
We study the problem of learning a Nash equilibrium (NE) in an imperfect information game (IIG) through self-play.
Model-Free Learning for Two-Player Zero-Sum Partially Observable Markov Games with Perfect Recall
We study the problem of learning a Nash equilibrium (NE) in an imperfect information game (IIG) through self-play.
Sampling from Log-Concave Distributions with Infinity-Distance Guarantees Oren Mangoubi Worcester Polytechnic Institute Nisheeth K. Vishnoi Y ale University
This approach also allows us to obtain an improvement on the dimension d in the running time for the problem of sampling from a log-concave distribution on polytopes K with infinity distance Á, by plugging in TV -distance running time bounds for the Dikin Walk Markov chain.
Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations
Currently, the data augmentation parameters are chosen by human effort and costly cross-validation, which makes it cumbersome to apply to new datasets. We develop a convenient gradient-based method for selecting the data augmentation without validation data during training of a deep neural network.
Impression learning: Online representation learning with synaptic plasticity
Because the true underlying structure of the data is rarely accessible, this "representation learning" must be
Foundations of Symbolic Languages for Model Interpretability Marcelo Arenas 1,4, Daniel Baez
Several queries and scores have been proposed to explain individual predictions made by ML models. Examples include queries based on "anchors", which are parts of an instance that are sufficient to justify its classification, and "feature-perturbation" scores such as SHAP .
