99 citations · 219 across the 24 of their papers we have counts for
25 papers
Efficiently Estimating Optimal Hyperparameter Scaling Laws through Power-Law Entropy Search
Zhiliang Chen, Sebastian Ament, David Eriksson +4
Optimal hyperparameter scaling laws describe how the best hyperparameters for large language model (LLM) training change with model and data scale, enabling practitioners to predic…
BOxCrete: A Bayesian Optimization Open-Source AI Model for Concrete Strength Forecasting and Mix Optimization
Bayezid Baten, M. Ayyan Iqbal, Sebastian Ament +2
Modern concrete must simultaneously satisfy evolving demands for mechanical performance, workability, durability, and sustainability, making mix designs increasingly complex. Recen…
Empirical Gaussian Processes
Jihao Andreas Lin, Sebastian Ament, Louis C. Tiao +3
Gaussian processes (GPs) are powerful and widely used probabilistic regression models, but their effectiveness in practice is often limited by the choice of kernel function. This k…
Autonomous Materials Exploration by Integrating Automated Phase Identification and AI-Assisted Human Reasoning
Ming-Chiang Chang, Maximilian Amsler, Duncan R. Sutherland +9
Autonomous experimentation holds the potential to accelerate materials development by combining artificial intelligence (AI) with modular robotic platforms to explore extensive com…
Scalable Gaussian Processes with Latent Kronecker Structure
Jihao Andreas Lin, Sebastian Ament, Maximilian Balandat +3
Applying Gaussian processes (GPs) to very large datasets remains a challenge due to limited computational scalability. Matrix structures, such as the Kronecker product, can acceler…
Scaling Gaussian Processes for Learning Curve Prediction via Latent Kronecker Structure
Jihao Andreas Lin, Sebastian Ament, Maximilian Balandat +1
A key task in AutoML is to model learning curves of machine learning models jointly as a function of model hyper-parameters and training progression. While Gaussian processes (GPs)…