PUBLICATIONS

PUBLICATIONS

Probabilistic Programming: Past, Present, and Future

Pfeffer, A. Invited keynote at the 22nd International Conference on Information Fusion (Fusion 2019), Ottawa, Canada (July 2019)  Overview: What is Probabilistic Programming? Probabilistic Programming in Action Probabilistic Programming Inference Algorithms Probabilistic Programming for Long-Lived AI Systems Download Slides For More

Structured Factored Inference for Probabilistic Programming

Pfeffer, A., Ruttenberg, B., Kretschmer, W., and O’Connor, A. Presented at the 21st International Conference on Artificial Intelligence and Statistics (AISTATS 2018), Lanzarote, Canary Islands (April 2018) Probabilistic reasoning on complex real-world models is computationally challenging. Inference algorithms have been

Scruff: A Deep Probabilistic Cognitive Architecture

Pfeffer, A. Invited talk at the Association for the Advancement of Artificial Intelligence’s Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18), New Orleans, LA (February 2018). Probabilistic programming is able to build rich models of systems that combine prior knowledge with

Using Reinforcement Learning for Probabilistic Program Inference

Pfeffer, A. Extended abstract for the Probabilistic Program Semantics Workshop associated with the Principles of Programming Languages (POPL) conference, Los Angeles, CA (January 2018). Inference in probabilistic programming often involves choosing between different methods. For example, one could use different

Practical Probabilistic Programming

Pfeffer, A. Practical Probabilistic Programming, Manning Publications, Cherry Hill, NJ (2016) Practical Probabilistic Programming introduces the working programmer to probabilistic programming. In it, you’ll learn how to use the PP paradigm to model application domains and then express those probabilistic models in

Functional Probabilistic Programming

Pfeffer, A. Presented at the Commercial Users of Functional Programming workshop, affiliated with the International Conference on Functional Programming, Boston, MA. (September 2013) Probabilistic modeling is one of the most widely used approaches to machine learning (ML). In recent years,

Probabilistic Programming and the Democratization of AI

Ruttenberg, B. Presentation to the New England Artificial Intelligence Group, Cambridge, MA (September 2013) Probabilistic models form the foundation of modern machine learning (ML) and artificial intelligence (AI). However, building and reasoning on models that represent large and complex scenarios

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Probabilistic Programming: Past, Present, and Future

Pfeffer, A. Invited keynote at the 22nd International Conference on Information Fusion (Fusion 2019), Ottawa, Canada (July 2019)  Overview: What is Probabilistic Programming? Probabilistic Programming in Action Probabilistic Programming Inference Algorithms Probabilistic Programming for Long-Lived AI Systems Download Slides For More

Structured Factored Inference for Probabilistic Programming

Pfeffer, A., Ruttenberg, B., Kretschmer, W., and O’Connor, A. Presented at the 21st International Conference on Artificial Intelligence and Statistics (AISTATS 2018), Lanzarote, Canary Islands (April 2018) Probabilistic reasoning on complex real-world models is computationally challenging. Inference algorithms have been

Scruff: A Deep Probabilistic Cognitive Architecture

Pfeffer, A. Invited talk at the Association for the Advancement of Artificial Intelligence’s Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18), New Orleans, LA (February 2018). Probabilistic programming is able to build rich models of systems that combine prior knowledge with

Using Reinforcement Learning for Probabilistic Program Inference

Pfeffer, A. Extended abstract for the Probabilistic Program Semantics Workshop associated with the Principles of Programming Languages (POPL) conference, Los Angeles, CA (January 2018). Inference in probabilistic programming often involves choosing between different methods. For example, one could use different

Practical Probabilistic Programming

Pfeffer, A. Practical Probabilistic Programming, Manning Publications, Cherry Hill, NJ (2016) Practical Probabilistic Programming introduces the working programmer to probabilistic programming. In it, you’ll learn how to use the PP paradigm to model application domains and then express those probabilistic models in

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