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Some Semantic Issues in Probabilistic Programming Languages (Invited Talk)

Hongseok Yang, Michael Wagner
2019 International Conference on Rewriting Techniques and Applications  
This is a slightly extended abstract of my talk at FSCD'19 about probabilistic programming and a few semantic issues on it.  ...  The main purpose of this abstract is to provide keywords and references on the work mentioned in my talk, and help interested audience to do follow-up study.  ...  To understand how machine learning researchers think about probabilistic programming, I recommend to watch the video recording of Josh Tenenbaum's ICML'18 invited talk, and read the introduction of the  ... 
doi:10.4230/lipics.fscd.2019.4 dblp:conf/rta/Yang19 fatcat:2riwxibo5jh4xbcibhoqcowoqy

Parallel Algorithms Reconsidered (Invited Talk)

Peter Sanders, Marc Herbstritt
2015 Symposium on Theoretical Aspects of Computer Science  
In practice, parallel programs have to share resources (e.g. processors) with other programs.  ...  Priority Queues: Show probabilistic quality guarantees for the MultiQueues from [28] or design a comparably fast data structure with provable guarantees. 2.  ... 
doi:10.4230/lipics.stacs.2015.10 dblp:conf/stacs/Sanders15 fatcat:77dekvtdw5fxlpswrupwboikqa

Solvability in a Probabilistic Setting (Invited Talk)

Simona Ronchi Della Rocca, Ugo Dal Lago, Claudia Faggian, Zena M. Ariola
2020 International Conference on Formal Structures for Computation and Deduction  
The notion of solvability, crucial in the λ-calculus, is conservatively extended to a probabilistic setting, and a complete characterization of it is given.  ...  Introduction In probabilistic computation, the current state of the underlying program or machine can evolve in different ways depending on the outcome of probabilistic choices, this way turning an essentially  ...  If the languages one has in mind are higher-order probabilistic languages, a natural model to consider is the λ-calculus, of course enriched with one or more probabilistic constructs.  ... 
doi:10.4230/lipics.fscd.2020.1 dblp:conf/fscd/RoccaLF20 fatcat:dtsnnqxrinezjc57e7afh3tpo4

A Fresh Look at the lambda-Calculus (Invited Talk)

Beniamino Accattoli, Michael Wagner
2019 International Conference on Rewriting Techniques and Applications  
Books are written about it, it is taught in most curricula on theoretical computer science, and it is the basis of many fashionable trends in programming languages such as probabilistic or quantum programming  ...  In 2011, we attended a talk where the speaker started by motivating the study of strong evaluation as follows.  ... 
doi:10.4230/lipics.fscd.2019.1 dblp:conf/rta/Accattoli19 fatcat:75swok72vvb6fdfl3fx5lu5sey

Proof-of-Work Certificates that Can Be Efficiently Computed in the Cloud (Invited Talk) [chapter]

Jean-Guillaume Dumas
2018 Lecture Notes in Computer Science  
We talk about a Prover (the server performing the computations) and a Verifier.  ...  here present problem-specific procedures in computer algebra, e.g. for exact linear algebra computations, that are Proveroptimal, that is that have much less financial overhead. • We combine those with probabilistic  ...  In computer algebra, the Prover can be a probabilistic algorithm or a symbolic-numeric program, where the Verifier would perform the checks exactly or symbolically; further, computer algebra systems could  ... 
doi:10.1007/978-3-319-99639-4_1 fatcat:7usrmrmjizg45g2tyfyye3rqwe

Probabilistic Programming: A True Verification Challenge [chapter]

Joost-Pieter Katoen
2015 Lecture Notes in Computer Science  
In this invited talk, I will survey recent progress on the formal semantics and verification of (parametric) probabilistic programs.  ...  Probabilistic programming.  ...  In this invited talk, I will survey recent progress on the formal semantics and verification of (parametric) probabilistic programs.  ... 
doi:10.1007/978-3-319-24953-7_1 fatcat:i7bwx2oaufguhpzbedvqoap26m

End-User Probabilistic Programming [chapter]

Judith Borghouts, Andrew D. Gordon, Advait Sarkar, Neil Toronto
2019 Lecture Notes in Computer Science  
Hence, we draw conclusions about the promise and limitations of probabilistic programming for end-users.  ...  Probabilistic programming aims to help users make decisions under uncertainty. The user writes code representing a probabilistic model, and receives outcomes as distributions or summary statistics.  ...  End-User Probabilistic Programming  ... 
doi:10.1007/978-3-030-30281-8_1 fatcat:zqq5vff2ujdevcpdfv2n4z4klu

The complexity of analyzing infinite-state Markov chains, Markov decision processes, and stochastic games (Invited talk)

Kousha Etessami, Marc Herbstritt
2013 Symposium on Theoretical Aspects of Computer Science  
RMCs and RMDPs provide natural abstract models of probabilistic procedural programs with recursion, and they are expressively equivalent to probabilistic and MDP extensions of pushdown automata.  ...  Our algorithms combine variations and generalizations of Newton's method with other techniques, including linear programming.  ... 
doi:10.4230/lipics.stacs.2013.1 dblp:conf/stacs/Etessami13 fatcat:jitefxp2brcrxc755si74xdo6q

Special Issue on Invited Talks of the Second International Conference on Networking and Computing

Koji Nakano
2012 International Journal of Networking and Computing  
It is my pleasure to publish the special issue on invited talks of ICNC'12. The ICNC'12 organizing committee asked speakers of keynote and tutorials of ICNC'12 to submit papers based on their talks.  ...  to a Standard Programming Interface for Massively Parallel Computing Environ- ment: Open CL by Shinichi Yamagiwa • All about RICC: RIKEN Integrated Cluster of Clusters by Nakata Maho  ... 
doi:10.15803/ijnc.2.2_146 fatcat:4pxangd44bhz3phjzcx72egh44

An Incentive Analysis of Some Bitcoin Fee Designs (Invited Talk)

Andrew Chi chih Yao, Emanuela Merelli, Anuj Dawar, Artur Czumaj
2020 International Colloquium on Automata, Languages and Programming  
Coupled with the fact b k * −α 0 = O( 1 √ n ) and k * •b k * = A•n+O( √ n ) probabilistically, we see that the OSB condition in Lemma 1 cannot hold.  ... 
doi:10.4230/lipics.icalp.2020.1 dblp:conf/icalp/Yao20 fatcat:x43omhzz2jdhrcqnohlbi5o5su

Probabilistic Semantics and Program Analysis [chapter]

Alessandra Di Pierro, Chris Hankin, Herbert Wiklicky
2010 Lecture Notes in Computer Science  
we present a framework for probabilistic program analysis which is inspired by the classical Abstract Interpretation framework by Cousot & Cousot and which we introduced as Probabilistic Abstract Interpretation  ...  The aims of these lecture notes are two-fold: (i) we investigate the relation between the operational semantics of probabilistic programming languages and Discrete Time Markov Chains (DTMCs), and (ii)  ...  This allows us to talk about the next state in the obvious way (for CTMC this concept is a bit more complicated).  ... 
doi:10.1007/978-3-642-13678-8_1 fatcat:ftubvgn7hfg3vj2oc56zrj2b3e

Invited Talk: Re-Engineering Computing with Neuro-Inspired Learning: Devices, Circuits, and Systems

Priyadarshini Panda, Kaushik Roy
2020 2020 33rd International Conference on VLSI Design and 2020 19th International Conference on Embedded Systems (VLSID)  
Additionally, we also propose probabilistic neural and synaptic computing platforms that can leverage the underlying stochastic device physics of spin-devices due to thermal noise.  ...  In this talk, I will highlight our approaches to enabling fully optics-free, millimeter-scale, multiplexed nano-optical sensors in silicon with protein measurement sensitivities comparable to commercial  ...  His postdoctoral work has been about probabilistic computing that makes use of compact probabilistic bits (p-bit) as building blocks for p-circuits.  ... 
doi:10.1109/vlsid49098.2020.00017 dblp:conf/vlsid/Panda020 fatcat:tvsoqomvynaxtkry62vhni4ura

Invited talk

Michael K. Tanenhaus, Michael J. Spivey-Knowlton, Kathleen M. Eberhard, Julie C. Sedivy, Paul D. Allopenna, James S. Magnuson
1996 Proceedings of the 34th annual meeting on Association for Computational Linguistics -   unpublished
Acknowledgments * This paper summarizes work that the invited talk by the first author (MKT) was based upon.  ...  Thus a speaker uttering "Computational linguists give good talks" is making an assertion about computational linguists.  ...  The timing of these eye-movements indicated that they were programmed during the "ambiguous" segment of the target word.  ... 
doi:10.3115/981863.981870 fatcat:rhsw64un2ndejcpr7uobtgh7hi

Some Formal Structures in Probability (Invited Talk)

Sam Staton, Naoki Kobayashi
This invited talk will discuss how developments in the Formal Structures for Computation and Deduction can also suggest new directions for the foundations of probability theory.  ...  In the non-probabilistic setting, memoization is a program optimization, where we are lazy about re-evaluating a function at a given argument, by caching or tabling.  ...  The piecewise functional was used to program the random piecewise functions. Laziness. Laziness in programming is a counterpart to the notion of "process" which is fundamental in probability.  ... 
doi:10.4230/lipics.fscd.2021.4 fatcat:nashxj6q3jeprertoqh2st4k44

From Verification to Causality-Based Explications (Invited Talk)

Christel Baier, Clemens Dubslaff, Florian Funke, Simon Jantsch, Rupak Majumdar, Jakob Piribauer, Robin Ziemek, Nikhil Bansal, Emanuela Merelli, James Worrell
Finally, formal approaches to probabilistic causation are collected and connected, and their relevance to the understanding of probabilistic systems is discussed.  ...  An early example is program slicing (see, e.g., [61] ) where by following program dependencies one aims to identify approximations of an actual cause for reaching a program location.  ...  for reasoning about probabilistic causation.  ... 
doi:10.4230/lipics.icalp.2021.1 fatcat:x42sfj2x5zexvk2lyt5tfce3fe
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