David Tolpin

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University of Oxford ∙ Ben-Gurion University of the Negev ∙ Paypal

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Stochastically Differentiable Probabilistic Programs

Probabilistic programs with mixed support (both continuous and discrete ...
03/02/2020 ∙ by David Tolpin, et al. ∙ 0 ∙ share

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Stochastic probabilistic programs

We introduce the notion of a stochastic probabilistic program and presen...
01/08/2020 ∙ by David Tolpin, et al. ∙ 0 ∙ share

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Warped Input Gaussian Processes for Time Series Forecasting

We introduce a Gaussian process-based model for handling of non-stationa...
12/05/2019 ∙ by David Tolpin, et al. ∙ 0 ∙ share

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Deployable probabilistic programming

We propose design guidelines for a probabilistic programming facility su...
06/20/2019 ∙ by David Tolpin, et al. ∙ 0 ∙ share

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Population Anomaly Detection through Deep Gaussianization

We introduce an algorithmic method for population anomaly detection base...
05/05/2018 ∙ by David Tolpin, et al. ∙ 0 ∙ share

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Intrusions in Marked Renewal Processes

We present a probabilistic model of an intrusion in a marked renewal pro...
09/24/2017 ∙ by David Tolpin, et al. ∙ 0 ∙ share

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A Planning Approach to Monitoring Behavior of Computer Programs

We describe a novel approach to monitoring high level behaviors using co...
09/11/2017 ∙ by Alexandre Cukier, et al. ∙ 0 ∙ share

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Process Monitoring on Sequences of System Call Count Vectors

We introduce a methodology for efficient monitoring of processes running...
07/12/2017 ∙ by Michael Dymshits, et al. ∙ 0 ∙ share

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Session Analysis using Plan Recognition

This paper presents preliminary results of our work with a major financi...
06/20/2017 ∙ by Reuth Mirsky, et al. ∙ 0 ∙ share

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Maximum a Posteriori Estimation by Search in Probabilistic Programs

We introduce an approximate search algorithm for fast maximum a posterio...
04/26/2015 ∙ by David Tolpin, et al. ∙ 0 ∙ share

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Path Finding under Uncertainty through Probabilistic Inference

We introduce a new approach to solving path-finding problems under uncer...
02/25/2015 ∙ by David Tolpin, et al. ∙ 0 ∙ share

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Output-Sensitive Adaptive Metropolis-Hastings for Probabilistic Programs

We introduce an adaptive output-sensitive Metropolis-Hastings algorithm ...
01/22/2015 ∙ by David Tolpin, et al. ∙ 0 ∙ share

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Rational Deployment of Multiple Heuristics in IDA*

Recent advances in metareasoning for search has shown its usefulness in ...
11/24/2014 ∙ by David Tolpin, et al. ∙ 0 ∙ share

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Justifying and Improving Meta-Agent Conflict-Based Search

The Meta-Agent Conflict-Based Search (MA-CBS) is a recently proposed alg...
10/23/2014 ∙ by David Tolpin, et al. ∙ 0 ∙ share

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Selecting Computations: Theory and Applications

Sequential decision problems are often approximately solvable by simulat...
08/09/2014 ∙ by Nicholas Hay, et al. ∙ 0 ∙ share

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Towards Rational Deployment of Multiple Heuristics in A*

The obvious way to use several admissible heuristics in A* is to take th...
05/22/2013 ∙ by David Tolpin, et al. ∙ 0 ∙ share

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VOI-aware MCTS

UCT, a state-of-the art algorithm for Monte Carlo tree search (MCTS) in ...
07/24/2012 ∙ by David Tolpin, et al. ∙ 0 ∙ share

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MCTS Based on Simple Regret

UCT, a state-of-the art algorithm for Monte Carlo tree search (MCTS) in ...
07/23/2012 ∙ by David Tolpin, et al. ∙ 0 ∙ share

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Doing Better Than UCT: Rational Monte Carlo Sampling in Trees

UCT, a state-of-the art algorithm for Monte Carlo tree sampling (MCTS), ...
08/18/2011 ∙ by David Tolpin, et al. ∙ 0 ∙ share

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Rational Deployment of CSP Heuristics

Heuristics are crucial tools in decreasing search effort in varied field...
04/11/2011 ∙ by David Tolpin, et al. ∙ 0 ∙ share

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Rational Value of Information Estimation for Measurement Selection

Computing value of information (VOI) is a crucial task in various aspect...
03/27/2010 ∙ by David Tolpin, et al. ∙ 0 ∙ share

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Semi-Myopic Sensing Plans for Value Optimization

We consider the following sequential decision problem. Given a set of it...
06/17/2009 ∙ by David Tolpin, et al. ∙ 0 ∙ share

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