Nicolas Beltran-Velez

@velezbeltran.bsky.social

Machine Learning PhD Student @ Blei Lab & Columbia University. Working on probabilistic ML | uncertainty quantification | LLM interpretability. Excited about everything ML, AI and engineering!

I received a review like this five years ago. It’s probably the right time now to share it with everyone who wrote or got random discouraging reviews from ICML/ACL.

Bild

Really excited about this! We note a connection between diffusion/flow models and neural/latent SDEs. We show how to use this for simulation-free learning of fully flexible SDEs. We refer to this as SDE Matching and show speed improvements of several orders of magnitude. arxiv.org/abs/2502.02472

SDE Matching: Scalable and Simulation-Free Training of Latent Stochastic Differential Equations

The Latent Stochastic Differential Equation (SDE) is a powerful tool for time series and sequence modeling. However, training Latent SDEs typically relies on adjoint sensitivity methods, which depend ...

arxiv.org

I have a sinking feeling that by 2029 I'm going to be faking a British accent so no one will think I was one of the *Americans* working on AI during the regime.

This is a scatterplot with the following key features:

Axes:
The x-axis represents "Interest in AI," with values ranging approximately from -2 to 2.
The y-axis represents "Willingness to Tolerate Closed, Autocratic Systems," also ranging from about -2 to 2.
Data Points:
Black dots dominate the plot, distributed across all four quadrants, indicating diverse positions on both variables.
A few red dots labeled "my peeps" are clustered in the bottom-right quadrant, signifying high interest in AI but low tolerance for closed, autocratic systems.
Blue Lines:
The plot includes horizontal and vertical blue lines at zero, dividing it into four quadrants for visual reference.
This visualization highlights a subset of individuals ("my peeps") who stand out from the majority based on their distinct combination of interest and values.

Something I really like about NLP research is that it makes everything super intuitive. This week I have been thinking about variational inference in NLP and a lot of the things that seemed to require mathematical intuition just become trivial when thinking about language. So cool:)

New randomized, controlled trial by the World Bank of students using GPT-4 as a tutor in Nigeria. Six weeks of after-school AI tutoring = 2 years of typical learning gains, outperforming 80% of other educational interventions. And it helped all students, especially girls who were initially behind.

BildBild

Proud of this work spearheaded by the phenomenal @jlfan.bsky.social and @mingxz.bsky.social in collaboration w/ Ben Izar! The past 3 years we've worked hard to unravel how #CNVs shape #tumor phenotypic plasticity seen in #singlecell #RNAseq data ➡️ #Echidna 🦔

BildBild
Joy Fan@jlfan.bsky.social · 2y ago

🧵 Excited to share #Echidna, a Bayesian framework for quantifying the impact of gene dosage on phenotypic plasticity: tinyurl.com/296kf7hf! With @elhamazizi.bsky.social and @mingxz.bsky.social, we integrate scRNA-seq & WGS to uncover how CNAs drive tumor evolution and transcriptional variability.

I have been a little bit scarce on social media in the last few months. Some of that has just been from being busy at work, but some of it has had to do with my father's passing. He hated the very idea of social media, but he religiously followed my Twitter, and then Bluesky, feeds. /n