So many ML papers contain tables like
| Method | Score(↑) |
|---|---|
| Baseline 1 | 49.9% |
| Baseline 2 | 49.8% |
| Baseline 3 | 50.0% |
| Our super fancy SOTA method | 50.1% |
then say "results on the benchmark show that our method is state-of-the-art for task X."
Knowing people is a great way to get hired. Nepotism is one obvious explanation (aka people hire you because they like you, or to gain favors from people who like you). I (along with most other people) think that nepotism is bad: it's unfair, and gives jobs to people who are probably not that good at them. However, it is a mistake to think that nepotism is the only reason why people who are known get hired, and that this practice is always bad. Some better reasons are:
Machine-learning for retrosynthesis is a popular research topic. Popular sub-topics include:
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.
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:
Happy 2025! Here are a few goals I am setting for myself this year!
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.
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:
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.