Showcase Deconstructed Research
Publications
The research team at Abacus.ai is focused on making fundamental contributions to important areas of deep learning. These areas include automated machine learning, meta learning, data augmentation, deep learning optimization, and fairness/debiasing in deep learning.
The team has an impressive list of top conference and workshop publications after just over one year.
 
Peer-Reviewed Conference Papers
 
A Study on Encodings for Neural Architecture Search
Colin White, Willie Neiswanger, Sam Nolen, Yash Savani
Selected for spotlight presentation | NeurIPS 2020
 
 
Post-Hoc Methods for Debiasing Neural Networks
Yash Savani, Colin White, Naveen Sundar Govindarajulu
NeurIPS 2020
 
Peer-Reviewed Workshop Papers
 
Local Search is State of the Art for Neural Architecture Search Benchmarks
Colin White, Willie Neiswanger, Yash Savani
ICML Workshop on AutoML 2020
 
 
Neural Architecture Search via Bayesian Optimization with a Neural Network Prior
Colin White, Willie Neiswanger, Yash Savani
NeurIPS Workshop on Meta Learning 2019
 
 
Deep Uncertainty Estimation for Model-based Neural Architecture Search
Colin White, Willie Neiswanger, Yash Savani
NeurIPS Workshop on Bayesian Deep Learning 2019
 
 
DECO: Debiasing through Compositional Optimization of Machine Learning Models
Naveen Sundar Govindarajulu and Colin White
NeurIPS Workshop on Robust AI in Financial Services, 2019
 
 
Differentiable Functions for Combining First-order Constraints with Deep Learning via Weighted Proof Tracing
Naveen Sundar Govindarajulu and Colin White
NeurIPS Workshop on Knowledge Representation to ML, 2019