Yggdrasil: An Optimized System for Training Deep Decision Trees at Scale. - Firas Abuzaid; Joseph K. Bradley; Feynman T. Liang; Andrew Feng; Lee Yang; Matei Zaharia; Ameet Talwalkar (NIPS, 2016)

Automatic Stylistic Composition of Bach Chorales with Deep LSTM. - Feynman T. Liang; Mark Gotham; Matthew Johnson 003; Jamie Shotton (ISMIR, 2017)

Bayesian experimental design using regularized determinantal point processes. - Michal Derezinski; Feynman T. Liang; Michael W. Mahoney (AISTATS, 2020)

Precise expressions for random projections: Low-rank approximation and randomized Newton. - Michal Derezinski; Feynman T. Liang; Zhenyu Liao 001; Michael W. Mahoney (NeurIPS, 2020)

Exact expressions for double descent and implicit regularization via surrogate random design. - Michal Derezinski; Feynman T. Liang; Michael W. Mahoney (NeurIPS, 2020)

Accelerating Metropolis-Hastings with Lightweight Inference Compilation. - Feynman T. Liang; Nimar S. Arora; Nazanin Khosravani Tehrani; Yucen Lily Li; Michael Tingley; Erik Meijer 001 (AISTATS, 2021)

Fat-Tailed Variational Inference with Anisotropic Tail Adaptive Flows. - Feynman T. Liang; Michael W. Mahoney; Liam Hodgkinson (ICML, 2022)

A Heavy-Tailed Algebra for Probabilistic Programming. - Feynman T. Liang; Liam Hodgkinson; Michael W. Mahoney (NeurIPS, 2023)