15:30

10:30

7:30

Arrival and networking

16:00

11:00

8:00

Welcome: Vikash Mansinghka, Jan-Willem van de Meent, Jean-Baptiste Tristan

16:10

11:10

8:10

Keynote: Leslie Kaelbling (Massachusetts Institute of Technology)

Doing for Our Robots What Nature Did for Us

16:50

11:50

8:50

Talk: Ben Zinberg (Massachusetts Institute of Technology)

Structured Differentiable Models of 3D Scenes via Generative Scene Graphs

17:10

12:10

9:10

Pre-recorded Talk: Iris Seaman (Northeastern University)

Nested Reasoning About Autonomous Agents

17:30

12:30

9:30

Talk: Zenna Tavares (Massachusetts Institute of Technology)

A Language for Counterfactual Generative Models

17:50

12:50

9:50

Invited talk: Robert Ness (Gamalon)

Towards Causal Inference with Latent Variable Models and Programs

18:10

13:10

10:10

End

18:30

13:30

10:30

Arrival and networking

18:45

13:45

10:45

Industry Panel – Probabilistic Programming in the Field: Bayesian Modeling

Nimar S. Arora (Facebook), Daniel Lee (Generable), Lawrence Murray (Uber), Rif A. Saurous (Google), Ulrich Schaechtle (Massachusetts Institute of Technology), Veronica Sara Weiner (Massachusetts Institute of Technology - panelist and moderator)

19:30

14:30

11:30

Posters/Networking

21:00

16:00

13:00

Meetup: Developers and Users

22:00

17:00

14:00

End

12:30

7:30

4:30

Arrival and networking

13:00

8:00

5:00

Invited talk: Christine Tasson (Université de Paris)

Semantics for Probabilistic Programming

13:20

8:20

5:20

Invited talk: Daniel Ritchie (Brown University)

Learning Neurosymbolic 3D Models

13:40

8:40

5:40

Talk: Maria Gorinova (University of Edinburgh)

Efficient Inference With Discrete Parameters in Stan

14:00

9:00

6:00

Talk: Yura Perov (EQL)

Multiverse: Causal Reasoning Using Importance Sampling in Probabilistic Programming

14:20

9:20

6:20

Talk: Hugo Paquet (University of Oxford)

Almost Surely Terminating Probabilistic Programs Are Differentiable Almost Everywhere

14:40

9:40

6:40

Talk: Jan Kudlicka (Uppsala University)

Probabilistic Programming for Birth-Death Models of Evolution Using an Alive Particle Filter With Delayed Sampling

15:00

10:00

7:00

Divide, Conquer, and Combine: A New Inference Strategy for Probabilistic Programs With Stochastic Support

15:20

10:20

7:20

End

15:30

10:30

7:30

Arrival and networking

15:45

10:45

7:45

Industry Panel – Probabilistic Programming in the Field: Complex Simulators

Nimar S. Arora (Facebook), Güneş Baydin (University of Oxford), Ashish Kapoor (Microsoft), Tejas Kulkarni (Common Sense Machines), Veronica Sara Weiner (Massachusetts Institute of Technology - moderator)

16:30

11:30

8:30

Posters/Networking

18:00

13:00

10:00

Zoom Call: Community Members from Underrepresented Groups

19:00

14:00

11:00

End

16:30

11:30

8:30

Arrival and networking

17:00

12:00

9:00

Keynote: Guy van den Broeck (UCLA)

From Probabilistic Circuits to Probabilistic Programs and Back

17:40

12:40

9:40

Talk: Ekansh Sharma (University of Toronto)

Approximations in Probabilistic Programs: a Compositional Nonasymptotic analysis of Nested MCMC

18:00

13:00

10:00

Talk: Steven Holtzen (UCLA)

Modular Exact Inference for Discrete Probabilistic Programs

18:20

13:20

10:20

Talk: Kinjal Shah (Facebook)

Bean Machine: A Declarative Probabilistic Programming Language For Efficient Programmable Inference

18:40

13:40

10:40

Talk: Avi Bryant (Gradient Retreat)

A Bayesian Computation Graph for High-Performance Gradient Evaluation on the JVM

19:00

14:00

11:00

Talk: Guangyao Zhou (Vicarious AI)

Mixed Hamiltonian Monte Carlo for Mixed Discrete and Continuous Variables

19:20

14:20

11:20

Talk: David Chiang (University of Notre Dame)

Translating Recursive Probabilistic Programs to Factor Graph Grammars

19:40

14:40

11:40

Closing Remarks: Vikash Mansinghka, Jan-Willem van de Meent, Jean-Baptiste Tristan

19:45

14:45

11:45

End