October 7, 2016


Filed under: Computer Science, Machine Learning — dvd @ 4:14 pm

http://anglican.ml/, the proper domain for the Anglican way of machine learning. Also http://probprog.ml/.

October 8, 2015

Immanuel Kant and Probability

Kant said: there are two a priori intuitions — space and time. There are also categories, and “the number of the categories in each class is always the same, namely, three”, like unity-plurality-modality, or possibility-existence-necessity. It would be fun to have three a priori intuitions, but only two exist, sigh. Really though?

Kant probably did not realize: there is a third one — probability, to wit, certainty of our experience. Just like space, probability precedes any experience. Every object is uncertain as much as it is extended.

The three a priori intuitions are related — infinite and undirected space, infinite and directed time, finite and undirected probability. Physics knows of uncertainty principle, we are uncertain about relation of time and space: both time and space cannot be intuited with certainty. Probability is as basic and fundamental as time and space for our cognition.

Just like geometry deals with a priori intuition of space, and mathematical analysis — with intuition of time, theory of probability deals with intuition of probability. There is philosophical justification for studying uncertainty, probability, and bayesian inference.

June 10, 2015

Maximum a Posteriori Estimation by Search in Probabilistic Programs

Filed under: Artificial Intelligence, Computer Science, Machine Learning — dvd @ 11:33 pm

Paper, slides, and poster as presented at SOCS 2015.

We introduce an approximate search algorithm for fast maximum a posteriori probability estimation in probabilistic programs, which we call Bayesian ascent Monte Carlo (BaMC). (more…)

June 8, 2015

Path Finding under Uncertainty through Probabilistic Inference

An early workshop paper, superseded by current research but still relevant, slides, and a poster.


We introduce a new approach to solving path-finding problems under uncertainty by representing them as probabilistic models and applying domain-independent inference algorithms to the models. (more…)

May 6, 2015

Anglican the Probabilistic Programming Concept

Filed under: Computer Science, Machine Learning — dvd @ 1:44 am

Anglican is a probabilistic programming language, or better yet, a concept, living in symbiosis with Clojure. Anglican stands for Church of England (because we are here in Oxford). To create your Turing-complete probabilistic models, clone anglican-user and hack away. Or, look at cool examples.

Read more…

Old Job Talk Slides

Filed under: Artificial Intelligence, Computer Science — Tags: — dvd @ 1:27 am

Found my own slides from a talk I gave a year ago, about rational meta-reasoning. Do they seem interesting to me because I have degraded during this year?

December 10, 2014

Output-Sensitive Adaptive MH for Probabilistic Programs

Filed under: Machine Learning — dvd @ 12:20 pm

A poster for the 3rd NIPS Workshop on Probabilistic Programming; also available as A0 PDF. Slides for a 15-minute talk.



November 19, 2014

Merge-and-Restart Meta-Agent Conflict-Based Search
for Multi-agent Path Finding

Filed under: Artificial Intelligence, Computer Science, Search, Uncategorized — Tags: , , , — dvd @ 6:48 pm

We introduce a new algorithm for multi-agent path finding, derived from the idea of meta-agent conflict-based search (MA-CBS). (more…)

October 2, 2014

Slides for my Tea Talk

Filed under: Computer Science, Machine Learning — dvd @ 12:55 am

My Tea Talk slides, on October 1st, 2014.

October 8, 2013


Filed under: Artificial Intelligence, Computer Science, Uncategorized — Tags: — dvd @ 2:07 am

My dissertation “Rational Metareasoning in Problem-Solving Search”.

Older Posts »

Powered by WordPress