The AutoML Podcast

Today we're talking with Caitlin Owen, a post-doc at the University of Otago about her work on error decomposition.

She recently published a paper titled "Towards Explainable AutoML Using Error Decomposition" about how a more granular view of the components of error can lead the construction of better AutoML systems.

Read her paper here: https://link.springer.com/chapter/10.1007/978-3-031-22695-3_13
Follow her on Twitter here: @CaitAshfordOwen
Connect with her on LinkedIn here: https://www.linkedin.com/in/caitlin-owen-5b9b08193/

Today we're talking with Gaurav Rao, the EVP & GM of Machine Learning and AI at AtScale, a company centered around the semantic layer.

For some time now, I've been feeling that there is a deep connection between a formal articulation of business context and the realization of the dream of AutoML, so I searched for people in the space who can help shine light on this direction.

Gaurav is one of the few who can speak about this. As you'll hear, he's extremely pedagogic and he's walking us through the origins of the concept, how it addresses some of the challenges that businesses face when trying to operationalize their ML, what it takes to build a universal semantic layer, how downstream ML applications are affected by the presence or absence of a semantic layer, and how the space of AutoML factors into this.

Connect his Gaurav and learn more about the semantic layer through his LinkedIn: https://www.linkedin.com/in/gauravraotechenthusiast/

Today Ankush Garg is speaking with Rishi Bommasani, PhD student at Stanford and one of the originator of the term Foundation Models.

They’re talking about the origins of the term Foundation Model, which he and his group advanced, in the paper "On the Opportunities and Risks of Foundation Models".

They’ll talk about self-supervision, issues of scale, the motivation behind the terminology, the origins of the Research for Foundation Models Institute at Stanford, outcome homogenization, emergence and phase transitions, and some of the social consequences to look out for.

Thank you both for this conversation. As the world is coming to terms with GPT-4, this will be increasingly relevant.

Paper: On the Opportunities and Risks of Foundation Models.
Rishi's twitter: @RishiBommasani
Center for Research on Foundation Models (CRFM)

Today we're talking with Raymond Peck, a senior engineer and director in the AutoML space. He spent time at H2O, dotData, Alteryx and many other places.

This is a fascinating conversation about the business, engineering, and science of machine learning automation in production. Learning about his experience is crucial for understanding the biography of the space.

We discuss the early motivations behind AutoML, the initial value propositions that propelled the first movers in the market, the market dynamics that operated in the early days, the evolution of the relevant engineering and science, how customers evaluate AutoML tools, the role of feature engineering and relational tables, the crucial role that explainability plays in AutoML, and many more topics.

Raymond is a prolific writer on LinkedIn. You should follow him here: https://www.linkedin.com/in/raymondpeck/.

Today we’re talking to Noah Hollmann and Samuel Muller about their paper on TabPFN - which is an incredible spin on AutoML based on Bayesian inference and transformers.

[Quick note on audio quality]: Some of the tracks have not recorded perfectly but I felt that the content there was too important not to release. Sorry for any ear-strain!

In the episode, we spend some time discussing posterior predictive probabilities before discussing how exactly they’ve pre-fitted their network, how they got their training data, what the network looks like, and how the system is performing.

To give you a taste of it, on datasets up to 1,000 training instances and 100 features, it takes less than a second to train and predict a classifier!

Read their paper here: https://arxiv.org/pdf/2207.01848.pdf

Follow Samuel on Twitter, here: https://twitter.com/SamuelMullr

Follow Noah on Twitter, here: https://twitter.com/noahholl

Today we’re talking to Sean Sexton, the Director of Modeling and Analytics Consulting at KPMG, about the role of models in financial institutions and how the risks associated with them is managed.

This turned out to be an incredibly deep and interesting topic, and we really only scratched the surface of it.

Sean has a unique ability to summarize developments in an entire space. If you're interested to learn more about modeling in financial institutions and about the history of how we got here, you should definitely study his dissertation, on managing model risk, here: https://macsphere.mcmaster.ca/handle/11375/28049

Today we’re talking to Matt Levine. Matt is a PhD student in computing and mathematical sciences at Caltech, and he focuses on improving the prediction and inference of physical systems by blending together both mechanistic modeling and  machine learning.

This episode is one of my favorites: we go pretty deep into dynamical systems, and into Matt's new framework for solving them by blending traditional, mechanistic, approaches with machine learning. This is a fascinating use of machine learning, and hopefully gets us one step closer to the automation of science, in general.

A Framework for Machine Learning of Model Error in Dynamical Systems - https://arxiv.org/abs/2107.06658

Related works
Autodifferentiable Ensemble Kalman Filters - https://epubs.siam.org/doi/abs/10.1137/21M1434477

Universal Differential Equations for Scientific Machine Learning - https://arxiv.org/abs/2001.04385

Continuous-time nonlinear signal processing: a neural network based approach for gray box identification - https://ieeexplore.ieee.org/document/366006

A generalised approach to process state estimation using hybrid artificial neural network/mechanistic models - https://www.sciencedirect.com/science/article/abs/pii/S0098135496003365

Today we’re chatting with Junhong Shen, a PhD student at Carnegie Mellon.

Junhong and her team are working on the generalizability of NAS algorithms across a diverse set of tasks.

Today we'll be talking about DASH, a NAS algorithm that takes diversity of tasks at its center. In order to implement DASH, Junhong and her team implemented three clever ideas that she'll share with us.

Efficient Architecture Search for Diverse Tasks - https://arxiv.org/pdf/2204.07554.pdf

Tackling Diverse Tasks with Neural Architecture Search - https://blog.ml.cmu.edu/2022/10/14/tackling-diverse-tasks-with-neural-architecture-search/

Does AutoML work for diverse tasks? - https://blog.ml.cmu.edu/2022/07/07/automl-for-diverse-tasks

Follow Junhong @JunhongShen1

Today we're speaking with Marius Lindauer and it is certainly one of my favorite episodes!

As you’ll hear, Marius is full of ideas for where AutoML systems can and should go. These ideas are crystallized in a blog-post, published here: https://www.automl.org/rethinking-automl-advancing-from-a-machine-centered-to-human-centered-paradigm/

If you’re searching for research directions, this conversation left me with dozens of ideas. Marius and his team are doing phenomenal work to make AutoML systems more trustworthy and more human-centric.

We will be reviewing content from the following papers:

• Bayesian Optimization with a Prior for the Optimum - https://arxiv.org/abs/2006.14608
• Explaining Hyperparameter Optimization via Partial Dependence Plots - https://arxiv.org/abs/2111.04820
• Enhancing Explainability of Hyperparameter Optimization via Bayesian Algorithm Execution - https://arxiv.org/abs/2206.05447
• πBO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization - https://arxiv.org/abs/2204.11051

Follow Marius on Twitter here - https://twitter.com/LindauerMarius, and AutoML.org on Twitter here - https://twitter.com/AutoML_org.

To help Marius complete the survey he mentioned, please visit the link here - https://www.soscisurvey.de/hpo-method-validation/.

To learn more about AutoML, visit AutoML.org, here - https://www.automl.org/

Today Ankush Garg is talking to Mehdi Bahrami about his recent project: BERT-Sort.

BERT-Sort is an example of how large language models can add useful context to tabular datasets, and to AutoML systems.

Mehdi is a Member of Research Staff at Fujitsu and, as he describes, he began using AutoML systems for his research, yet he came across some crucial limitations of existing solutions. The modifications he made highlight a promising future for the relationship between language models and AutoML. This is a direction we're going to continue to explore on the show.

References:
BERT-Sort: A Zero-shot MLM Semantic Encoder on Ordinal Features for AutoML - https://proceedings.mlr.press/v188/bahrami22a.html

PyTorrent: A Python Library Corpus for Large-scale Language Models: https://arxiv.org/abs/2110.01710

AugmentedCode: Examining the Effects of Natural Language Resources in Code Retrieval Models: https://arxiv.org/abs/2110.08512