Paper: High Confidence Generalization for Reinforcement Learning
We present several classes of reinforcement learning algorithms that safely generalize to Markov decision processes (MDPs) not seen during training. Specifically, we study the setting in which some set of MDPs is accessible for training. For various definitions of safety, our algorithms give probabilistic guarantees that agents can safely generalize to MDPs that are sampled … Continue reading "Paper: High Confidence Generalization for Reinforcement Learning"
Read MorePaper: RealMVP: A Change of Variables Method For Rectangular Matrix-Vector Products
Rectangular matrix-vector products are used extensively throughout machine learning and are fundamental to neural networks such as multi-layer perceptrons, but are notably absent as normalizing flow layers. This paper identifies this methodological gap and plugs it with a tall and wide MVP change of variables formula. Our theory builds up to a practical algorithm that … Continue reading "Paper: RealMVP: A Change of Variables Method For Rectangular Matrix-Vector Products"
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