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e andreas stuhlmüller and noah d goodman 2016 proceedings of the 19th international conference on artificial intelligence and statistics aistats abstract lightweight source to source transformation approaches to implementing mcmc for probabilistic programming languages are popular for their simplicity support of existing deterministic code and ability to execute on existing fast runtimes however they are also slow requiring a complete re execution of the program on every metropolis hastings proposal we present a new extension to the lightweight approach c3 which enables efficient incrementalized re execution of mh proposals c3 is based on two core ideas transforming probabilistic programs into continuation passing style cps and caching the results of function calls we show that on several common models c3 reduces proposal runtime by 20 100x in some cases reducing runtime complexity from linear in model size to constant we also demonstrate nearly an order of magnitude speedup on a complex inverse procedural modeling application bibtex article ritchie2015c3 author daniel ritchie and andreas stuhlm u ller and noah d goodman title c3 lightweight incrementalized mcmc for probabilistic programs using continuations and callsite caching booktitle proceedings of the 19th international conference on artificial intelligence and statistics aistats year 2016 keywords probabilistic programming inference mcmc metropolis hastings url https arxiv org abs 1509 02151 learning the preferences of bounded agents owain evans andreas stuhlmüller and noah d goodman 2015 poster at the nips 2015 workshop on bounded optimality bibtex article evans2015learning author owain evans and andreas stuhlm u ller and noah d goodman booktitle nips 2015 workshop on bounded optimality title learning the preferences of bounded agents year 2015 url https stuhlmueller org papers preferences nipsworkshop2015 pdf coarse to fine sequential monte carlo for probabilistic programs andreas stuhlmüller robert x d hawkins n siddharth and noah d goodman 2015 technical report arxiv 1509 02962 cs ai abstract many practical techniques for probabilistic inference require a sequence of distributions that interpolate between a tractable distribution and an intractable distribution of interest usually the sequences used are simple e g based on geometric averages between distributions when models are expressed as probabilistic programs the models themselves are highly structured objects that can be used to derive annealing sequences that are more sensitive to domain structure we propose an algorithm for transforming probabilistic programs to coarse to fine programs which have the same marginal distribution as the original programs but generate the data at increasing levels of detail from coarse to fine we apply this algorithm to an ising model its depth from disparity variation and a factorial hidden markov model we show preliminary evidence that the use of coarse to fine models can make existing generic inference algorithms more efficient bibtex article stuhlmueller2015coarse author andreas stuhlm u ller and robert x d hawkins and n siddharth and noah d goodman archiveprefix arxiv eprint 1509 02962 primaryclass cs ai title coarse to fine sequential monte carlo for probabilistic programs year 2015 keywords probabilistic programming inference smc particle filter coarse to fine url https arxiv org abs 1509 02962 why do you ask good questions provoke informative answers robert x d hawkins andreas stuhlmüller judith degen and noah d goodman 2015 proceedings of the thirty seventh annual conference of the cognitive science society abstract what makes a question useful what makes an answer appropriate in this paper we formulate a family of increasingly sophisticated models of question answer behavior within the rational speech act framework we compare these models based on three different pieces of evidence first we demonstrate how our answerer models capture a classic effect in psycholinguistics showing that an answerer s level of informativeness varies with the inferred questioner goal while keeping the question constant second we jointly test the questioner and answerer components of our model based on empirical evidence from a question answer reasoning game third we examine a special case of this game to further distinguish among the questioner models we find that sophisticated pragmatic reasoning is needed to account for some of the data people can use questions to provide cues to the answerer about their interest and can select answers that are informative about inferred interests bibtex inproceedings hawkins2015why author hawkins robert x d and stuhlm uller andreas and degen judith and goodman noah d booktitle proceedings of the thirty seventh annual conference of the c ognitive s cience s ociety title why do you ask good questions provoke informative answers year 2015 modeling cognition with probabilistic programs representations and algorithms andreas stuhlmüller 2015 ph d thesis massachusetts institute of technology abstract this thesis develops probabilistic programming as a productive metaphor for understanding cognition both with respect to mental representations and the manipulation of such representations in the first half of the thesis i demonstrate the representational power of probabilistic programs in the domains of concept learning and social reasoning i provide examples of richly structured concepts defined in terms of systems of relations subparts and recursive embeddings that are naturally expressed as programs and show initial experimental evidence that they match human generalization patterns i then proceed to models of reasoning about reasoning a domain where the expressive power of probabilistic programs is necessary to formalize our intuitive domain understanding due to the fact that unlike previous formalisms probabilistic programs allow conditioning to be represented a model not just applied to a model i illustrate this insight with programs that model nested reasoning in game theory artificial intelligence and linguistics in the second half i develop three inference algorithms with the dual intent of showing how to efficiently compute the marginal distributions defined by probabilistic programs and providing building blocks for process level accounts of human cognition first i describe a dynamic programming algorithm for computing the marginal distribution of discrete probabilistic programs by compiling to systems of equations and show that it can make inference in models of reasoning about reasoning tractable by merging and reusing subcomputations second i introduce the setting of amortized inference and show how learning inverse models lets us leverage samples generated by other inference algorithms to compile probabilistic models into fast recognition functions third i develop a generic approach to coarse to fine inference in probabilistic programs and provide evidence that it can speed up inference in models with large state spaces that have appropriate hierarchical structure finally i substantiate the claim that probabilistic programming is a productive metaphor by outlining new research questions that have been opened up by this line of investigation bibtex phdthesis astthesis author andreas stuhlm u ller title modeling cognition with probabilistic programs representations and algorithms school massachusetts institute of technology year 2015 month 6 the design and implementation of probabilistic programming languages noah d goodman and andreas stuhlmüller electronic abstract probabilistic programming languages ppls unify techniques for the formal description of computation and for the representation and use of uncertain knowledge ppls have seen recent interest from the artificial intelligence programming languages cognitive science and natural languages communities this book explains how to implement ppls by lightweight embedding into a host language we illustrate this by designing and implementing webppl a small ppl embedded in javascript we show how to implement several algorithms for universal probabilistic inference including priority based enumeration with caching particle filtering and markov chain monte carlo we use program transformations to expose the information required by these algorithms including continuations and stack addresses we illustrate these ideas with examples drawn from semantic parsing natural language pragmatics and procedural graphics bibtex misc dippl author noah d goodman and andreas stuhlm u ller title the design and implementation of probabilistic programming languages howpublished url https dippl org note accessed 2014 08 27 learning physics from dynamical scenes tomer d ullman andreas stuhlmüller noah d goodman and joshua b tenenbaum 2014 proceedings of the thirty sixth annual conference of the cognitive science society abstract humans acquire their most basic physical concepts early in development but continue to enrich and expand their intuitive physics throughout life as they are exposed to more and varied dynamical environments we introduce a hierarchical bayesian framework to explain how people can learn physical theories across multiple timescales and levels of abstraction in contrast to previous bayesian models of theory acquisition tenenbaum kemp griffiths goodman 2011 we work with more expressive probabilistic program representations suitable for learning the forces and properties that govern how objects interact in dynamic scenes unfolding over time we compare our model and human learners on a challenging task of inferring novel physical laws in microworlds given short movies people are generally able to perform this task and behave in line with model predictions yet they also make systematic errors suggestive of how a top down bayesian approach to learning might be complemented by a more bottomup feature based approximate inference scheme to best explain theory learning at an algorithmic level bibtex article ullman cogsci2014 author tomer d ullman and andreas stuhlm u ller and noah d goodman and joshua b tenenbaum title learning physics from dynamical scenes booktitle proceedings of the thirty sixth annual conference of the cognitive science society year 2014 keywords theory learning intuitive physics probabilistic inference physical reasoning url https stuhlmueller org papers physics cogsci2014 pdf learning stochastic inverses andreas stuhlmüller jessica taylor and noah d goodman 2013 advances in neural information processing systems nips 27 abstract we describe a class of algorithms for amortized inference in bayesian networks in this setting we invest computation upfront to support rapid online inference for a wide range of queries our approach is based on learning an inverse factorization of a model s joint distribution a factorization that turns observations into root nodes our algorithms accumulate information to estimate the local conditional distributions that constitute such a factorization these stochastic inverses can be used to invert each of the computation steps leading to an observation sampling backwards in order to quickly find a likely explanation we show that estimated inverses converge asymptotically in number of prior or posterior training samples to make use of inverses before convergence we describe the inverse mcmc algorithm which uses stochastic inverses to make block proposals for a metropolis hastings sampler we explore the efficiency of this sampler for a variety of parameter regimes and bayes nets bibtex article ast jt ndg nips2013 author andreas stuhlm u ller and jessica taylor and noah d goodman title learning stochastic inverses booktitle advances in neural information processing systems nips 27 year 2013 keywords bayesian networks mcmc gibbs amortized inference url https stuhlmueller org papers inverses nips2013 pdf knowledge and implicature modeling language understanding as social cognition noah d goodman and andreas stuhlmüller 2013 topics in cognitive science abstract is language understanding a special case of social cognition to help evaluate this view we can formalize it as the rational speech act theory listeners assume that speakers choose their utterances approximately optimally and listeners interpret an utterance by using bayesian inference to invert this model of the speaker we apply this framework to model scalar implicature some implies not all and n implies not more than n this model predicts an interaction between the speaker s knowledge state and the listener s interpretation we test these predictions in two experiments and find good fit between model predictions and human judgements bibtex article ndg ast topics2013 author noah d goodman and andreas stuhlm u ller title knowledge and implicature modeling language understanding as social cognition year 2013 journaltitle topics in cognitive science keywords language bayesian model scalar implicature url https stuhlmueller org papers implicature topics2013 pdf reasoning about reasoning by nested conditioning modeling theory of mind with probabilistic programs andreas stuhlmüller and noah d goodman 2013 journal of cognitive systems research abstract a wide range of human reasoning patterns can be explained as conditioning in probabilistic models however conditioning has traditionally been viewed as an operation applied to such models not represented in such models we describe how probabilistic programs can explicitly represent conditioning as part of a model this enables us to describe reasoning about others reasoning using nested conditioning much of human reasoning is about the beliefs desires and intentions of other people we use probabilistic programs to formalize these inferences in a way that captures the flexibility and inherent uncertainty of reasoning about other agents we express examples from game theory artificial intelligence and linguistics as recursive probabilistic programs and illustrate how this representation language makes it easy to explore new directions in each of these fields we discuss the algorithmic challenges posed by these kinds of models and describe how dynamic programming techniques can help address these challenges bibtex article ast ndg cogsys2013 author andreas stuhlm u ller and noah d goodman journaltitle cognitive systems research title reasoning about reasoning by nested conditioning modeling theory of mind with probabilistic programs year 2013 issn 1389 0417 doi https doi org 10 1016 j cogsys 2013 07 003 url https stuhlmueller org papers nested conditioning cogsys2013 pdf learning stochastic inverses for adaptive inference in probabilistic programs andreas stuhlmüller and noah d goodman 2012 poster at the nips 2012 workshop on probabilistic programming abstract we describe an algorithm for adaptive inference in probabilistic programs during sampling the algorithm accumulates information about the local probability distributions that compose the program s overall di...