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Burkay Gur

dblp:273/0304 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1Databases, data management, data science and information retrieval · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Efficient and distributed learning · 77% Deep learning architectures and training · 23%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Computational finance and economics · 100%
Databases, data mining, and information retrieval
1 paper
Machine learning and data management · 100%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Efficient and distributed learning
automated machine learning
0.412020
Build the State-of-the-Art Machine Learning Technology for the Crypto Economy · KDD 2020
Machine learning › Deep learning architectures and training › sequence modeling
transformer-based sequence modeling
0.112020
Build the State-of-the-Art Machine Learning Technology for the Crypto Economy · KDD 2020
Machine learning and data management › data management for machine learning
feature store
0.112020
Build the State-of-the-Art Machine Learning Technology for the Crypto Economy · KDD 2020

Methods — techniques the papers use, named apart from their topics

linear blending · 1.3gradient boosting trees · 1.3deep learning · 1.3AutoML · 1.3
YearPublicationVenuePosition
2020 Build the State-of-the-Art Machine Learning Technology for the Crypto Economy
abstract
Coinbase's mission is "to create an open financial system for the world". This presentation serves as an overview of our efforts in building the state-of-the-art machine learning technology for the fast-evolving crypto economy, which follows a prototype, productization, and experimentation development cycle. On the machine learning side, it covers topics around proper train/validation setup, maintaining a fast iteration cycle using a custom-built AutoML framework (called "EasyML"), a deep learning Transformers-based sequence based model and how to incorporate timing into it, how to combine gradient boosting trees with deep learning using linear blending, as well as model interpretability, evaluation, and experimentation. On the machine learning platform side, we will dive into the internals of Nostradamus, our in-house-built framework that manages model life-cycle, and Feature Store, our self-serve feature management, computation and serving framework.
Michael Li, Catalin Tiseanu, Burkay Gur
KDD3