Double checking that Gauche's fingerprint kernels are positive definite.

Austin Tripp

2025-01-12

GAUCHE is a library for Gaussian processes in chemistry. I contributed a small amount to GAUCHE several years ago but am not an active developer. I recently learned that some new fingerprint kernels were added. In this post I examine whether these kernels are positive definite (PD), and if there are any restrictions attached.

Using a small set of lemmas (of which two were new to me), I am convinced that all but two of the kernels are PD, without being restricted to binary vectors. The remaining 2 I am unsure of, but don't claim that they are not PD.

What ML researchers and users get wrong: optimistic assumptions

Austin Tripp

2025-01-09

ML is often done poorly, both by "ML experts" (by which I mean people who understand the algorithms but not the data) and "ML users" (by which I mean people who understand their data, but not the algorithms). I think the cause is often over-optimism, although about different things:

New Year's Resolutions for 2025

Austin Tripp

2025-01-05

Happy 2025! Here are a few goals I am setting for myself this year!

Review of NeurIPS 2024 and predictions for ML in 2025

Austin Tripp

2025-01-01

I was fortunate to attend NeurIPS 2024, arguably the largest and most influential machine learning conference in the world (thanks Valence for sponsoring my trip 🙏). In this post I will try to summarize what I learned at NeurIPS, and cautiously make some predictions for the year ahead.

Rules of scientific English writing for an international audience.

Austin Tripp

2024-12-30

Although English is the common language for international scientific communication, most scientists are not native English speakers. To account for this, I think that all scientists (especially native English speakers) should try to write text which is easy to read for non-native speakers. I propose the following rules for this:

When should you expect Bayesian optimization to work well?

Austin Tripp

2024-12-10

As much as I believe in the potential of Bayesian optimization (BO) to be useful for scientific discovery, after 4+ years I have seen many instances where BO does not work. In this post I explain a simple heuristic rule to decide whether you should expect BO to work well or not.

What can eduroam teach us about building research infrastructure

Austin Tripp

2024-12-01

Eduroam is a fantastic piece of academic infrastructure: students/researchers from thousands of universities around the world can automatically connect to WiFi and any partner institutions using login details from their home institution. To me it's surprising that it exists, given that it has many characteristics of projects which academia is terrible at accomplishing:

Scientific conferences as approximate Bayesian inference

Austin Tripp

2024-11-17

Scientists should ideally form their beliefs based on evidence and update their beliefs as new evidence arrives. Unfortunately, humans are far from perfect Bayesian thinkers and therefore may struggle to do this properly. In this post I explain how conferences help scientists perform better Bayesian inference.

Overview of the NSGA-II algorithm for multi-objective optimization

Austin Tripp

2024-11-04

Nondominated sorting genetic algorithm version 2, more commonly called NSGA-II, is a well-established genetic algorithm (aka evolutionary algorithm) for multi-objective optimization (MOO). In this post I try to extract the key insights/lessons from this paper.

Advice for applying for a PhD in AI for science in 2024

Austin Tripp

2024-10-27

With the immense growth of AI for science (AI4sci for brevity), I imagine many younger students are considering applying for PhDs in AI for science in this application cycle. This post is my attempt to turn my 5 years of experience in AI4sci into actionable advice for prospective PhD students.