AutoML Seminars Courses

Tuning LLM Judge Design Decisions for 1/1000 of the Cost

Discover how to systematically tune LLM judge hyperparameters using multi-objective multi-fidelity optimization to achieve better accuracy at 1/1000th the cost while ensuring reproducibility.

• YouTube
• 42 minutes
• On-Demand
• Free Video

Hyperband-based Bayesian Optimization for Efficient Black-box Prompt Selection

Discover HbBoPs framework combining Gaussian process surrogates with Hyperband for sample-efficient black-box prompt selection in large language models across API-only settings.

• YouTube
• 48 minutes
• On-Demand
• Free Video

carps - A Framework for Comparing N Hyperparameter Optimizers on M Benchmarks

Discover a comprehensive framework for evaluating hyperparameter optimization methods across diverse benchmark tasks, featuring 3336 tasks and 28 optimizer variants for standardized HPO research.

• YouTube
• 36 minutes
• On-Demand
• Free Video

Reshuffling Resampling Splits Can Improve Generalization of Hyperparameter Optimization

Discover how reshuffling resampling splits during hyperparameter optimization can enhance machine learning model performance and improve generalization on unseen data through theoretical and practical insights.

• YouTube
• 46 minutes
• On-Demand
• Free Video

Time Series Forecasting in the Age of Pretrained Models - Chronos

Discover how pretrained models revolutionize time series forecasting through Chronos, exploring zero-shot performance capabilities and applications in energy, retail, and finance sectors.

• YouTube
• 43 minutes
• On-Demand
• Free Video

Scaling Exponents Across Parameterizations and Optimizers

Explore groundbreaking research on model scaling, parameterization choices, and optimizer behavior, revealing new insights for effective hyperparameter transfer and introducing the innovative Adam-atan2 optimizer.

• YouTube
• 40 minutes
• On-Demand
• Free Video

TabArena - A Living Benchmark for Machine Learning on Tabular Data

Discover TabArena, a continuously maintained benchmarking system for tabular machine learning that compares deep learning, gradient-boosted trees, and foundation models across datasets.

• YouTube
• 46 minutes
• On-Demand
• Free Video

Multi-Objective AutoML - Towards Accurate and Robust Models

Discover how AutoML can optimize machine learning models for both accuracy and robustness against domain shifts and input perturbations in trustworthy AI systems.

• YouTube
• 47 minutes
• On-Demand
• Free Video

Do-PFN - In-Context Learning for Causal Effect Estimation

Explore Do-PFN's innovative approach to causal effect estimation using in-context learning, eliminating the need for interventional data or known causal graphs.

• YouTube
• 46 minutes
• On-Demand
• Free Video

A LLM Evolutionary Algorithm for Automatically Generating Bayesian Optimization Algorithms

Discover how LLMs can automatically generate and evolve Bayesian optimization algorithms through evolutionary computation, creating competitive optimizers across various problem spaces.

• YouTube
• 47 minutes
• On-Demand
• Free Video

Accurate Predictions on Small Data and Time Series with the Tabular Foundation Model TabPFN

Discover how TabPFN revolutionizes tabular data prediction with superior accuracy and speed, outperforming traditional methods across scientific fields while enabling fine-tuning and data generation capabilities.

• YouTube
• 50 minutes
• On-Demand
• Free Video

Large Language Models to Enhance Bayesian Optimization

Discover how Large Language Models enhance Bayesian optimization through LLAMBO, improving hyperparameter tuning and black-box function optimization with natural language integration.

• YouTube
• 31 minutes
• On-Demand
• Free Video

The AI Scientist - Towards Fully Automated Open-Ended Scientific Discovery

Explore groundbreaking research on autonomous AI systems capable of conducting end-to-end scientific discovery, from generating ideas to writing papers and performing peer reviews in machine learning.

• YouTube
• 52 minutes
• On-Demand
• Free Video

Searching for Neural Architectures from Fundamental Operations - An Introduction to einspace

Explore neural architecture search through einspace, a novel approach using fundamental operations to discover diverse and competitive network architectures for machine learning tasks.

• YouTube
• 38 minutes
• On-Demand
• Free Video

Mixture-of-Supernets - Improving Weight-Sharing Supernet Training with Architecture-Routed MoE

Explore cutting-edge research on Mixture-of-Supernets architecture that enhances neural architecture search efficiency and improves BERT and MT model performance through innovative weight-sharing techniques.

• YouTube
• 35 minutes
• On-Demand
• Free Video