Musings about artificial intelligence, search engines, machine learning, computational advertising, intellectual property law, social media & widgets, and good beer.
Thursday, December 30, 2010
List of Best Paper awards in CS/AI/ML conferences
Wednesday, December 29, 2010
Managing Open Source Licenses
- A clear company policy is set on what open source licenses are allowed and how developers can use open source come or components.
- The corporate code is cleanly annotated with any third party attributions (see below).
- Open Source code that has bad licenses for commercial usage is identified and removed before release.
- A Bill of Materials is created for each release listing third-party software in the release.
- Necessary copyright or other notices appear in About dialogs, manuals or product websites.
* XYZ.com Third-party or Open Source Declaration
* Name: Bart Simpson
* Date of first commit: 04/25/2009
* Release: 3.5 “The Summer Lager Release”
* Component: tinyjson
* Description: C++ JSON object serializer/deserializer
* Homepage: http://blog.beef.de/projects/tinyjson/
* License: MIT style license
* Copyright: Copyright (c) 2008 Thomas Jansen (email@example.com)
* Note: See below for original declarations from the code
Friday, December 17, 2010
Stochastic Universal Sampling/Selection
Monday, November 22, 2010
Computing, economics and the financial meltdown (a collection of links)
Information technology has enabled the development of a global financial system of incredible sophistication. At the same time, it has enabled the development of a global financial system of such complexity that our ability to comprehend it and assess risk, both localized and systemic, is severely limited. Financial-oversight reform is now a topic of great discussion. The focus of these talks is primarily over the structure and authority of regulatory agencies. Little attention has been given to what I consider a key issue—the opaqueness of our financial system—which is driven by its fantastic complexity. The problem is not a lack of models. To the contrary, the proliferation of models may have created an illusion of understanding and control, as is argued in a recent report titled "The Financial Crisis and the Systemic Failure of Academic Economics."
Krugman's essay at the time How Did Economists Get It So Wrong? gave a nice history of economic ideas, the models behind and his interpretations of their correctness.
The theoretical model that finance economists developed by assuming that every investor rationally balances risk against reward — the so-called Capital Asset Pricing Model, or CAPM (pronounced cap-em) — is wonderfully elegant
Economics, as a field, got in trouble because economists were seduced by the vision of a perfect, frictionless market system.
H. L. Mencken: “There is always an easy solution to every human problem — neat, plausible and wrong.”
“You put chicken into the grinder”—he laughed with that infectious Wall Street black humor—“and out comes sirloin.”
Poormojo "Any sufficiently advanced financial instrument is indistinguishable from fraud."
Recipe for Disaster: The Formula That Killed Wall Street
Wall Street’s Math Wizards Forgot a Few Variables
Wednesday, October 27, 2010
Review of "Learning to Rank with Partially-Labeled Data"
Tuesday, October 26, 2010
Stanford Computational Advertising course - Fall 2010
Monday, August 16, 2010
1) Making decisions by experimentation versus meetings+intuition is crucial.
2) Don't assume your role is to know the answer. Your role is really to work out how to find the answer as quickly as possible.
3) Brand is unimportant when customers can observe you are meeting their needs.
4) Brand is important when they can't search/observe and must reason with less data.
5) Don't price your products based upon cost or competition, work out your true value to the customer.
Tuesday, July 27, 2010
Really happy to be done.
Advice for working professionals attempting a PhD:
1) pick something relevant to your work.
2) think twice about a theoretical topic.
3) don't make it longer/bigger than necessary.
4) don't grow your family during this time
I did not follow this advice and this likely resulted in a 4 year delay. The outcome was great and the topic is now relevant to the new job at Rubicon.
On Mutation and Crossover in the Theory of Evolutionary Algorithms
The Evolutionary Algorithm is a population-based metaheuristic optimization algorithm. The EA employs mutation, crossover and selection operators inspired by biological evolution. It is commonly applied to find exact or approximate solutions to combinatorial search and optimization problems.
This dissertation describes a series of theoretical and experimental studies on a variety of evolutionary algorithms and models of those algorithms. The effects of the crossover and mutation operators are analyzed. Multiple examples of deceptive fitness functions are given where the crossover operator is shown or proven to be detrimental to the speedy optimization of a function. While other research monographs have shown the benefits of crossover on various fitness functions, this is one of the few (or only) doing the inverse.
A background literature review is given of both population genetics and evolutionary computation with a focus on results and opinions on the relative merits of crossover and mutation. Next, a family of new fitness functions is introduced and proven to be difficult for crossover to optimize. This is followed by the construction and evaluation of executable theoretical models of EAs in order to explore the effects of parameterized mutation and crossover.
These models link the EA to the Metropolis-Hastings algorithm. Dynamical systems analysis is performed on models of EAs to explore their attributes and fixed points. Additional crossover deceptive functions are shown and analyzed to examine the movement of fixed points under changing parameters. Finally, a set of online adaptive parameter experiments with common fitness functions is presented.
Finalized April 19, 2010