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 two years of its conception.
Conference and Journal Papers
A Deeper Look at Zero-Cost Proxies for Lightweight NAS
Colin White, Mikhail Khodak, Renbo Tu, Shital Shah, Sébastien Bubeck, Debadeepta Dey
ICLR 2022 Blog Track
NAS-Bench-Suite: NAS Evaluation is (Now) Surprisingly Easy
Yash Mehta, Colin White, Arber Zela, Arjun Krishnakumar, Guri Zabergja, Shakiba Moradian, Mahmoud Safari, Kaicheng Yu, Frank Hutter
ICLR 2022
Synthetic Benchmarks for Scientific Research in Explainable Machine Learning
Yang Liu, Sujay Khandagale, Colin White, Willie Neiswanger
NeurIPS Datasets Track 2021
NAS-Bench-x11 and the Power of Learning Curves
Shen Yan, Colin White, Yash Savani, Frank Hutter
NeurIPS 2021
How Powerful are Performance Predictors in Neural Architecture Search?
Colin White, Arber Zela, Binxin Ru, Yang Liu, Frank Hutter
NeurIPS 2021
An Analysis of Super-Net Heuristics in Weight-Sharing NAS
Kaicheng Yu, René Ranftl, Mathieu Salzmann
TPAMI 2021
Learning by Turning: Neural Architecture Aware Optimisation
Yang Liu, Jeremy Bernstein, Markus Meister, Yisong Yue
ICML 2021
BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search
Colin White, Willie Neiswanger, Yash Savani
AAAI 2021
Exploring the Loss Landscape in Neural Architecture Search
Colin White, Sam Nolen, Yash Savani
UAI 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
Workshop Papers
On the Generalizability and Predictability of Recommender Systems
Duncan McElfresh, Sujay Khandagale, Jonathan Valverde, John Dickerson, Colin White
Workshop at AutoML-Conf 2022
Speeding up NAS with Adaptive Subset Selection
Vishak Prasad, Colin White, Paarth Jain, Sibasis Nayak, Rishabh Iyer, Ganesh Ramakrishnan
Workshop at AutoML-Conf 2022
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
CVPR 2021 Unseen Data in Neural Architecture Search
Simon Schrodi, Colin White, Ekrem Ozturk, Danny Stoll, Frank Hutter
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