Robert Zinkov

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Potential-based shaping was designed as a way of introducing background knowledge into model-free reinforcement-learning algorithms. By identifying states that are likely to have high value, this approach can decrease experience complexity—the number of trials needed to find near-optimal behavior. An orthogonal way of decreasing experience complexity is to(More)
We present Hakaru, a new probabilistic programming system that allows composable reuse of distributions, queries, and inference algorithms, all expressed in a single language of measures. The system implements two automatic and semantics-preserving program transformations—disintegration, which calculates conditional distributions, and simplification, which(More)
We draw a formal connection between using synthetic training data to optimize neural network parameters and approximate, Bayesian, model-based reasoning. In particular, training a neural network using synthetic data can be viewed as learning a proposal distribution generator for approximate inference in the synthetic-data generative model. We demonstrate(More)
In recent years, declarative programming languages specialized for probabilistic modeling has emerged as distinct class of languages. These languages are predominantly written by researchers in the machine learning field and concentrate on generalized MCMC inference algorithm. Unfortunately, all these languages are too slow for practical adoption. In my(More)
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