The AutoML Podcast

Today I'm speaking with Jan N. van Rijn about metalearning. Jan is an assistant professor at Leiden University, where he also did his PhD. He is one of the founders of the OpenML Foundation, he previously did a post-doc at Freiburg in the Frank Hutter lab, and he is one of the authors of the metalearning book, which we'll be discussing. We’ll be primarily examining the contents of their...

Today we’re speaking with three researchers: Karan Grewal, Abhi Iyer and Akash Velu, about multi-task learning and how their new brain-inspired approach can help tackle it. We’ll be discussing what a task is, what exactly we mean by multi-task systems, distances between tasks, the difference between continual learning and multi-task learning, catastrophic forgetting, catastrophic interference and...

Today I’m speaking with Mayukh Das about using neural architecture search for resource-constrained devices and about a new multi-objective reinforcement-learning based framework that he recently published called AUTOCOMET. We’ll be covering such topics as how NAS research is done both at Samsung and at Microsoft,  the relationship between NAS and product teams, devices and the various types of con...

Today we’re speaking with José Hernández-Orallo. José is a Professor at the Polytechnic University of València in Spain and a Senior Research Fellow at the Leverhulme Centre for the Future of Intelligence, at Cambridge. We'll be covering an enormous amount of ground surrounding intelligence and its evaluation. We’ll touch on topics such as operating conditions in ML, agent characteristic curv...

Today I’m talking with Wout Schellaert about assessor models. Wout is a PhD student at the Polytechnic University of Valencia.. We’ll be covering a lot of different topics, such as the distributional hypothesis in machine learning, evaluation criteria, the reductive nature of current evaluation methods, task systems, the desiderata of assessor models, how to build assessor models, when to use them...

Today we’re talking to Karthi Ramamurthy about a novel approach to similarity learning explainability. Karthi is a research staff member in IBM Research at the Watson Research Center. He studies the relationship between humans, machines, data and the societal implications of machine learning. He was involved in the initial development of the open source AI Fairness 360 toolkit, where he’s still an...

Today I’m speaking with Vasco Lopes, about the state of Neural Architecture Search, NAS, and about a new method that he published that takes a very creative look at how to do NAS. We’ll be discussing the motivation behind NAS, the current state of its deployment, the biggest use-cases today, the three components that make up NAS, the drawbacks to the current NAS paradigm, search spaces and how to...

Today I’m speaking with Yury Gorishniy about the state of the competition between Deep Learning and Gradient Boosted Decision Trees when it comes to tabular datasets, and about a recent paper he published that seems to take a stab at improving the state of deep learning on tabular datasets. We discuss whether or not there exists a gap between deep learning and gradient boosted decision trees, what...

Today we’ll be speaking with Julian Morimoto about missing data, its impact on the reliability of statistical inference, and two theorems that he recently discovered using concepts from real analysis about what guarantees we can expect, at the limit of arbitrarily large data sets. Julian has a background in math, and studied law at Harvard Law School and he speaks about the unique challenges of ad...

In this episode, Adam introduces the show, the motivations for it, and why and how you should participate.

Today we're speaking with Luigi Quaranta about the state of reproducibility in machine learning. Luigi published a taxonomy of support for reproducibility by various tools in the space and together we’re exploring the need for reproducibility, challenges and limitations, how to evaluate opportunities for improving your current systems, and what the future might hold. A few papers would be rel...

In this episode, Adam speaks with Doctor Torsten Ensslin about simulating reputation networks and their manipulation using Information Theory. Torsten is an Astrophysicist and cosmologist at the Max Plank institute, where he’s held many titles and positions. His current scientific work investigates theoretical cosmology and information field theory.  As he discusses in this episode, Torsten co-cre...

In this episode, Adam discusses Multi-objective optimization with Laurent Parmentier. Laurent works at OVHCloud, most recently as a data scientist but previously in various software engineering roles. He published his thesis on AutoML at OVHCloud, and had previously released a paper titled TPOT-SH: A Faster Optimization Algorithm to Solve the AutoML Problem on Large Datasets. The conversation cent...

This episode launches us into the deep waters of ML interpretability with Jessica Schrouff. Jessica is a Senior Research Scientist at Google Research working on machine learning for healthcare. Before joining Google in 2019, she was a postdoctoral fellow at University College London (UK) and Stanford University (USA), developing machine learning techniques for neuroscience discovery and clinical p...

This is a conversation between data scientist Ankush Garg, from Telepath, and fourth-year Ph. D. student Iman Mirzadeh and they’ll be talking about Continual Learning and about Iman’s paper titled “Architecture Matters in Continual Learning”. Iman is interested in Artificial General Intelligence and so he’s researching systems that can over time develop increasingly more complex skills and a riche...

Today we’re talking about MLOps - with our guide Georgios Symeonidis and we’ll be orienting around a recent paper he published titled “MLOps - Definitions, Tools and Challenges”. Georgios studied electrical and computer engineering at Democritus University of Thrace at Xanthi, in Greece. He specialized in information and electronics. He’s also worked as a research engineer at Athena Research and I...

This episode covers the relationship between Curriculum Learning and AutoML with Lucas Nildaimon dos Santos Silva. Lucas is a data scientist at americanas s.a., in Brazil, and is currently pursuing a Ph. D. in computer science from the Federal University of São Carlos where he also did his master’s. He’s researching machine learning and NLP and has recently published a paper on Curriculum Learning...

In this episode, we explore the relationship between Machine Learning and Statistical Mechanics with the guidance of Alia Abbara. This conversation centers around her PhD dissertation and we cover such topics as the relationship between statistics and physics,  the long legacy of physics on machine learning, and the role of physical intuition in the future of machine learning. Find her original pa...