Actionable Machine Learning in the Browser with PyScript
Valerio Maggio

Interactive ML apps in the browser with zero installation and no server needed? Come to my talk to know how..

AutoGluon: AutoML for Tabular, Multimodal and Time Series Data
Caner Turkmen, Oleksandr Shchur

Learn about #AutoML and @AutoGluon, which can handle a range of tasks from regression to image classification and time series forecasting with state-of-the-art performance. #AutoML #datascience

Bayesian Marketing Science: Solving Marketing's 3 Biggest Problems
Dr. Thomas Wiecki

A Bayesian modeling toolkit to solve today's biggest marketing challenges.

BHAD: Explainable unsupervised anomaly detection using Bayesian histograms
Alexander Vosseler

We present a Bayesian histogram anomaly detector (BHAD). BHAD scales linearly with the size of the data and allows a direct explanation of individual anomaly scores due to its simple linear form

evosax: JAX-Based Evolution Strategies
Robert Lange

Tired of having to handle asynchronous processes for neuroevolution? Do you want to leverage high-throughput accelerators for evolution strategies (ES)? evosax allows you to leverage JAX, XLA compilation & auto-vectorization/parallelization to scale ES to accelerators.

Grokking Anchors: Uncovering What a Machine-Learning Model Relies On
KIlian Kluge

What makes or breaks a machine-learning model's decision? Let's use anchor explanations to find out!

Hyperparameter optimization for the impatient
Martin Wistuba

HPO does not need to be expensive, see how to speed it up with a couple of simple algorithms

Improving Machine Learning from Human Feedback
Erin Mikail Staples, Nikolai

While powerful, models built off large datasets like GPT-3 often bring their biases along with them. However, is this the best future for machine learning? Join us to explore Reinforcement Learning from Human Feedback (RLHF) techniques and why they matter more now than ever.

Performing Root Cause Analysis with DoWhy, a Causal Machine-Learning Library
Patrick Blöbaum

Learn how to use the Python DoWhy library to perform root cause analysis using methods of causal machine-learning.

Propensity Score Matching in a nOtshell
Alon Nir

Propensity Score Matching in a nOtshell - intuitive introduction to the wonderful world of PSMs!

When A/B testing isn’t an option: an introduction to quasi-experimental methods
Inga Janczuk

Have you ever wanted to know the causal effect of an action but A/B testing wasn’t an option? Here’s a brief helicopter tour over quasi-experimental methods that can be used instead!