Showcase Deconstructed Research
Publications
The research team at Abacus.AI makes fundamental contributions to the field of AI/ML that might have an impact in the short or long run and does applied research that has benefits in practice. The areas we have been focusing on include automated machine learning, meta-learning, data augmentation, deep learning optimization, and fairness/debiasing in deep learning.
The team already has an impressive list of publications in top-tier conferences and workshops in just over one year of its conception.
 
Peer-Reviewed Conference Papers
 
BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search
Colin White, Willie Neiswanger, Yash Savani
AAAI 2021
 
 
A Study on Encodings for Neural Architecture Search
Colin White, Willie Neiswanger, Sam Nolen, Yash Savani
Selected for spotlight presentation | NeurIPS 2020
 
 
Intra-Processing 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, Sam Nolen, Yash Savani
ICML Workshop on AutoML 2020
 
 
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