Feynman T. Liang

Publication Activity (10 Years)

Years Active: 2016-2023
Publications (10 Years): 14

Top Topics

Experimental Design

Point Processes

Metropolis Hastings

Variational Inference

Top Venues

Publications

Feynman T. Liang, Liam Hodgkinson, Michael W. Mahoney
A Heavy-Tailed Algebra for Probabilistic Programming. NeurIPS (2023)
Feynman T. Liang, Liam Hodgkinson, Michael W. Mahoney
A Heavy-Tailed Algebra for Probabilistic Programming. CoRR (2023)
Feynman T. Liang, Liam Hodgkinson, Michael W. Mahoney
Fat-Tailed Variational Inference with Anisotropic Tail Adaptive Flows. CoRR (2022)
Feynman T. Liang, Michael W. Mahoney, Liam Hodgkinson
Fat-Tailed Variational Inference with Anisotropic Tail Adaptive Flows. ICML (2022)
Feynman T. Liang, Nimar S. Arora, Nazanin Khosravani Tehrani, Yucen Lily Li, Michael Tingley, Erik Meijer
Accelerating Metropolis-Hastings with Lightweight Inference Compilation. AISTATS (2021)
Michal Derezinski, Feynman T. Liang, Michael W. Mahoney
Bayesian experimental design using regularized determinantal point processes. AISTATS (2020)
Michal Derezinski, Feynman T. Liang, Michael W. Mahoney
Exact expressions for double descent and implicit regularization via surrogate random design. NeurIPS (2020)
Michal Derezinski, Feynman T. Liang, Zhenyu Liao, Michael W. Mahoney
Precise expressions for random projections: Low-rank approximation and randomized Newton. CoRR (2020)
Feynman T. Liang, Nimar S. Arora, Nazanin Khosravani Tehrani, Yucen Lily Li, Michael Tingley, Erik Meijer
Accelerating Metropolis-Hastings with Lightweight Inference Compilation. CoRR (2020)
Michal Derezinski, Feynman T. Liang, Zhenyu Liao, Michael W. Mahoney
Precise expressions for random projections: Low-rank approximation and randomized Newton. NeurIPS (2020)
Michal Derezinski, Feynman T. Liang, Michael W. Mahoney
Bayesian experimental design using regularized determinantal point processes. CoRR (2019)
Michal Derezinski, Feynman T. Liang, Michael W. Mahoney
Exact expressions for double descent and implicit regularization via surrogate random design. CoRR (2019)
Feynman T. Liang, Mark Gotham, Matthew Johnson, Jamie Shotton
Automatic Stylistic Composition of Bach Chorales with Deep LSTM. ISMIR (2017)
Firas Abuzaid, Joseph K. Bradley, Feynman T. Liang, Andrew Feng, Lee Yang, Matei Zaharia, Ameet S. Talwalkar
Yggdrasil: An Optimized System for Training Deep Decision Trees at Scale. NIPS (2016)