4 accepted papers
Training reinforcement learning (RL) agents directly from high-dimensional image observations continues to be a challenging problem. Recent line of work on behavioral distances proposes to learn representations that encode behavioral similarities quantified by the bisimulation metric. By learning an…
Kernel herding is a deterministic sampling algorithm designed to draw ‘super samples' from probability distributions when provided with their kernel mean embeddings in a reproducing kernel Hilbert space (RKHS). Empirical expectations of functions in the RKHS formed using these super samples tend to…
Modeling joint probability distributions over sequences has been studied from many perspectives. The physics community developed matrix product states, a tensor-train decomposition for probabilistic modeling, motivated by the need to tractably model many-body systems. But similar models have also be…
Extending classical probabilistic reasoning using the quantum mechanical view of probability has been of recent interest, particularly in the development of hidden quantum Markov models (HQMMs) to model stochastic processes. However, there has been little progress in characterizing the expressivenes…