Machine Learning & Stats
10 talks from the 2023 edition.
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Actionable Machine Learning in the Browser with PyScript ▶ Recording 📝 Transcript
Valerio Maggio
PyScript brings the full PyData stack in the browser, opening up to unprecedented use cases for interactive data-intensive applications. In this scenario, the web browser becomes a ubiquitous computing platform,...
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AutoGluon: AutoML for Tabular, Multimodal and Time Series Data ▶ Recording 📝 Transcript
Caner Turkmen, Oleksandr Shchur
AutoML, or automated machine learning, offers the promise of transforming raw data into accurate predictions with minimal human intervention, expertise, and manual experimentation. In this talk, we will introduce...
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Bayesian Marketing Science: Solving Marketing's 3 Biggest Problems ▶ Recording 📝 Transcript
Dr. Thomas Wiecki
In this talk I will present two new open-source packages that make up a powerful and state-of-the-art marketing analytics toolbox. Specifically, PyMC-Marketing is a new library built on top of the popular Bayesian...
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BHAD: Explainable unsupervised anomaly detection using Bayesian histograms ▶ Recording 📝 Transcript
Alexander Vosseler
The detection of outliers or anomalous data patterns is one of the most prominent machine learning use cases in industrial applications. I present a Bayesian histogram anomaly detector (BHAD), where the number of...
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evosax: JAX-Based Evolution Strategies ▶ Recording 📝 Transcript
Robert Lange
Tired of having to handle asynchronous processes for neuroevolution? Do you want to leverage massive vectorization and high-throughput accelerators for evolution strategies (ES)?...
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Grokking Anchors: Uncovering What a Machine-Learning Model Relies On
KIlian Kluge
Assessing the robustness of models is an essential step in developing machine-learning systems. To determine if a model is sound, it often helps to know which and how many input features its output hinges on. This...
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Hyperparameter optimization for the impatient ▶ Recording 📝 Transcript
Martin Wistuba
In the last years, Hyperparameter Optimization (HPO) became a fundamental step in the training of Machine Learning (ML) models and in the creation of automatic ML pipelines. Unfortunately, while HPO improves the...
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Improving Machine Learning from Human Feedback ▶ Recording 📝 Transcript
Erin Mikail Staples, Nikolai
Large generative models rely upon massive data sets that are collected automatically. For example, GPT-3 was trained with data from “Common Crawl” and “Web Text”, among other sources. As the saying goes — bigger...
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Performing Root Cause Analysis with DoWhy, a Causal Machine-Learning Library ▶ Recording 📝 Transcript
Patrick Blöbaum
In this talk, we will introduce the audience to [DoWhy](https://www.pywhy.org/dowhy), a library for causal machine-learning (ML). We will introduce typical problems where causal ML can be applied and will...
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When A/B testing isn’t an option: an introduction to quasi-experimental methods ▶ Recording 📝 Transcript
Inga Janczuk
Identification of causal relationships through running experiments is not always possible. In this talk, an alternative approach towards it, quasi-experimental frameworks, is discussed. Additionally, I will present...