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Showing posts from November, 2009

Constrained MDPs and the reward hypothesis

It's been a looong ago that I posted on this blog. But this should not mean the blog is dead. Slow and steady wins the race, right? Anyhow, I am back and today I want to write about constrained Markovian Decision Process (CMDPs). The post is prompted by a recent visit of Eugene Feinberg, a pioneer of CMDPs, of our department, and also by a growing interest in CMPDs in the RL community (see this, this, or this paper).
For impatient readers, a CMDP is like an MDP except that there are multiple reward functions, one of which is used to set the optimization objective, while the others are used to restrict what policies can do. Now, it seems to me that more often than not the problems we want to solve are easiest to specify using multiple objectives (in fact, this is a borderline tautology!). An example, which given our current sad situation is hard to escape, is deciding what interventions a government should apply to limit the spread of a virus while maintaining economic prod…

Djvu vs. Pdf

Long blog again, so here is the executive summary: Djvu files are typically smaller than Pdf files. Why? Can we further compress pdf files? Yes, we can, but the current best solution has limitations. And you can forget all "advanced" commercial solutions. They are not as good as a free solution.

Introduction

DJVU is a proprietary file format by LizardTech. Incidentally, it was invented by some machine learning researchers, Yann LeCun, Léon Bottou, Patrick Haffner and the image compression researcher Paul G. Howard at AT&T back in 1996. The DJVULibre library provides a free implementation, but is GPLd and hence is not suitable for certain commercial softwares, like Papers, which I am using to organize my electronic paper collection. Hence, Papers, might not support djvu in the near future (the authors of Papers do not want to make it free, and, well, this is their software, their call).
Djvu files can converted to Pdf files using ddjvu, a command line tool which is part of D…

Keynote vs. Powerpoint vs. Beamer

A few days ago I decided to give Keynote, Apple's presentation software, a try (part of iWork '09). Beforehand I used MS Powerpoint 2003, Impress from NeoOffice 3.0 (OpenOffice's native Mac version) and LaTeX with beamer. Here is a comparison of the ups and downs of these software, mainly to remind myself when I will reconsider my choice in half a year and also to help people decide what to use for their presentation. Comments, suggestions, critics are absolutely welcome, as usual. Btw, while preparing this note I have learned that go-oo.org has a native Mac Aqua version of OpenOffice. Maybe I will try it some day and update the post. It would also be good to include a recent version of Powerpoint in the comparison.
StabilityKeynote: Excellent
After a few days of usage, so take this statement with a grain of salt..MS Powerpoint 2003: ExcellentImpress: Poor
Save your work very oftenBeamer: ExcellentCreating visually appealing slides, graphics on slides
Keynote: Excellent
Posit…