Connectionists: CfP: Workshop on Databases and AI @NeurIPS 2021

[Nantia Makrynioti]

Call for Papers
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       DBAI
Workshop on Databases and AI @NeurIPS 2021

https://dbai-workshop.github.io/

Co-located with NeurIPS 2021 (Virtual conference)

About the workshop
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Relational data represents the vast majority of data present in the enterprise world. Yet none of the ML computations happens inside a relational database where data reside.
Instead a lot of time is wasted in denormalizing the data and moving them outside of the databases in order to train models. Relational learning, which takes advantage of
relational data structure, has been a 20 year old research area, but it hasn’t been connected with relational database systems, despite the fact that relational databases
are the natural space for storing relational data. Recent advances in database research have shown that it is possible to take advantage of the relational structure in data
in order to accelerate ML algorithms. Research in relational algebra originating from the database community has shown that it is possible to further accelerate linear algebra
operations. Probabilistic Programming has also been proposed as a framework for AI that fits can be realized in relational databases. Data programming, a mechanism for weak/self
supervision is slowly migrating to the natural space of storing data, the database. At last as models in deep learning grow several systems are being developed for model
management inside relational databases. This workshop aspires to start a conversation on the following topics:

- What is the impact of relations/relational structure in machine learning?
- Why has relational learning not been more successful? Why we don’t have yet the equivalent of tensorflow/pytorch in relational learning?
- Why is there no deep network structure for structured relational data? Are we just not there yet, or is there something intrinsic in random forest/boosted trees that work better for relational data?
- Can relational databases take advantage of the relational nature of graph neural network
- The algorithms and db communities have completely different approaches to relational learning, what is the connection?
- How does data programming connect to relational learning and can it be accelerated with the algorithmic primitives of relational databases?
- The attention network has been interpreted and used as a mechanism for discovering and expressing relations. It has also been considered as a storage mechanism of knowledge in Large Language Models (Transformers). Are transformers equivalent to databases?

Call for papers
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Areas of particular interest for the workshop include (but are not limited to):

* Data Management in Machine Learning Applications
* Definition, Execution and Optimization of Complex Machine Learning Pipelines
* Systems for Managing the Lifecycle of Machine Learning Models
* Systems for Efficient Hyperparameter Search and Feature Selection
* Machine Learning Services in the Cloud
* Modeling, Storage and Provenance of Machine Learning Artifacts
* Integration of Machine Learning and Dataflow Systems
* Integration of Machine Learning and ETL Processing
* Definition and Execution of Complex Ensemble Predictors
* Sourcing, Labeling, Integrating, and Cleaning Data for Machine Learning
* Data Validation and Model Debugging Techniques
* Privacy-preserving Machine Learning
* Benchmarking of Machine Learning Applications
* Responsible Data Management
* Transparency and Accountability of Machine-Assisted Decision Making
* Impact of Data Quality and Data Preprocessing on the Fairness of ML Predictions

Submission:
Submissions can be short papers (4 pages) or long papers (up to 8 pages, plus unlimited references). Authors are requested to prepare submissions following the NeurIPS proceedings format.
DBAI is a single-blind workshop, authors must include their names and affiliations on the manuscript cover page.
Submission Website: TBD
Inclusion and Diversity in Writing: http://2021.sigmod.org/calls_papers_inclusion_and_diversity.shtml

Conflicts:
Workshops are not a venue for work that has been previously published in other conferences on machine learning or related fields.
Work that is presented at the main NeurIPS conference wiil not be accepted in the workshop, including as part of an invited talk.

Important Dates
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Paper submission deadline: Sep 17, 2021, 11:59 PM (AoE, UTC-12)

Acceptance notification: Oct 22, 2021 EOD

Mandatory SlidesLive upload for speaker videos: Nov 08, 2021

Workshop day: Dec 13, 2021

Organizers
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Nikolaos Vasiloglou (relationalAI)
Maximilian Schleich (University of Washington)
Nantia Makrynioti (CWI)
Parisa Kordjamshidi (Michigan State University)
Kirk Pruhs (University of Pitsburg)
Zenna Tavares (MIT)