Alpha A. Lee

According to our database, Alpha A. Lee authored at least 17 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Pushing the Pareto front of band gap and permittivity: ML-guided search for dielectric materials.

Janosh Riebesell, T. Wesley Surta, Rhys E. A. Goodall, Michael Gaultois, Alpha A. Lee

CoRR, 2024

2023

Matbench Discovery - An evaluation framework for machine learning crystal stability prediction.

Janosh Riebesell, Rhys E. A. Goodall, Anubhav Jain, Philipp Benner, Kristin A. Persson, Alpha A. Lee

CoRR, 2023

GAUCHE: A Library for Gaussian Processes in Chemistry.

Ryan-Rhys Griffiths, Leo Klarner, Henry B. Moss, Aditya Ravuri, Sang Truong, Yuanqi Du, Samuel Stanton, Gary Tom, Bojana Rankovic, Arian Rokkum Jamasb, Aryan Deshwal, Julius Schwartz, Austin Tripp, Gregory Kell, Simon Frieder, Anthony Bourached, Alex Chan, Jacob Moss, Chengzhi Guo, Johannes Peter Dürholt, Saudamini Chaurasia, Ji Won Park, Felix Strieth-Kalthoff, Alpha A. Lee, Bingqing Cheng, Alán Aspuru-Guzik, Philippe Schwaller, Jian Tang

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Achieving robustness to aleatoric uncertainty with heteroscedastic Bayesian optimisation.

Ryan-Rhys Griffiths, Alexander A. Aldrick, Miguel Garcia-Ortegon, Vidhi Lalchand, Alpha A. Lee

Mach. Learn. Sci. Technol., 2022

GAUCHE: A Library for Gaussian Processes in Chemistry.

Ryan-Rhys Griffiths, Leo Klarner, Henry B. Moss, Aditya Ravuri, Sang Truong, Bojana Rankovic, Yuanqi Du, Arian R. Jamasb, Julius Schwartz, Austin Tripp, Gregory Kell, Anthony Bourached, Alex Chan, Jacob Moss, Chengzhi Guo, Alpha A. Lee, Philippe Schwaller, Jian Tang

CoRR, 2022

2021

Dataset Bias in the Natural Sciences: A Case Study in Chemical Reaction Prediction and Synthesis Design.

Ryan-Rhys Griffiths, Philippe Schwaller, Alpha A. Lee

CoRR, 2021

2020

Perspective: new insights from loss function landscapes of neural networks.

Sathya R. Chitturi, Philipp C. Verpoort, Alpha A. Lee, David J. Wales

Mach. Learn. Sci. Technol., 2020

Impact of Chemist-In-The-Loop Molecular Representations on Machine Learning Outcomes.

Todd J. Wills, Dmitrii A. Polshakov, Matthew C. Robinson, Alpha A. Lee

J. Chem. Inf. Model., 2020

Validating the validation: reanalyzing a large-scale comparison of deep learning and machine learning models for bioactivity prediction.

Matthew C. Robinson, Robert C. Glen, Alpha A. Lee

J. Comput. Aided Mol. Des., 2020

Bayesian unsupervised learning reveals hidden structure in concentrated electrolytes.

Penelope Jones, Fabian Coupette, Andreas Härtel, Alpha A. Lee

CoRR, 2020

Investigating 3D Atomic Environments for Enhanced QSAR.

William McCorkindale, Carl Poelking, Alpha A. Lee

CoRR, 2020

2019

Achieving Robustness to Aleatoric Uncertainty with Heteroscedastic Bayesian Optimisation.

Ryan-Rhys Griffiths, Miguel Garcia-Ortegon, Alexander A. Aldrick, Alpha A. Lee

CoRR, 2019

Predicting materials properties without crystal structure: Deep representation learning from stoichiometry.

Rhys E. A. Goodall, Alpha A. Lee

CoRR, 2019

Bayesian semi-supervised learning for uncertainty-calibrated prediction of molecular properties and active learning.

Yao Zhang, Alpha A. Lee

CoRR, 2019

2018

Molecular Transformer for Chemical Reaction Prediction and Uncertainty Estimation.

Philippe Schwaller, Teodoro Laino, Théophile Gaudin, Peter Bolgar, Costas Bekas, Alpha A. Lee

CoRR, 2018

Geometry of energy landscapes and the optimizability of deep neural networks.

Simon Becker, Yao Zhang, Alpha A. Lee

CoRR, 2018

Energy-entropy competition and the effectiveness of stochastic gradient descent in machine learning.

Yao Zhang, Andrew M. Saxe, Madhu S. Advani, Alpha A. Lee

CoRR, 2018