Machine Learning for Data

Overview

As the use of machine learning (ML) becomes ubiquitous, there is a growing understanding and appreciation for the role that data plays for building successful ML solutions. Classical ML research has been primarily focused on learning algorithms and their guarantees. Recent progress has shown that data is playing an increasingly central role in creating ML solutions, such as the massive text data used for training powerful language models, (semi-)automatic engineering of weak supervision data that enables applications in few-labels settings, and various data augmentation and manipulation techniques that lead to performance boosts on many real world tasks. On the other hand, data is one of the main sources of security, privacy, and bias issues in deploying ML solutions in the real world.

This workshop will focus on the new perspective of machine learning for data --- specifically how ML techniques can be used to facilitate and automate a range of data operations (e.g. ML-assisted labeling, synthesis, selection, augmentation), and the associated challenges of quality, security, privacy and fairness for which ML techniques can also enable solutions. In this workshop, we aim to bring together researchers and practitioners working on methodology, theory, applications, and systems to exchange ideas, identify key challenges, and advance the field towards the most exciting and promising future directions.

Topics of particular interest include, but are not limited to:

• Methods of using ML to assist human annotators in data labeling

• Methods of automated data engineering, such as synthesis, augmentation, re-weighting, etc.

• Theories, methods, and studies to characterize, detect, or mitigate data bias

• Methods of detecting and preserving privacy information in data

• Systems for automating data operations and analytics

• Applications based on data-human-machine interactions

Invited Speakers

Important Dates and Links

Paper Submission Deadline: June 14, 2021 (11:59pm AOE)

Author Notification: July 1, 2021

Camera-ready paper submission due: July 16, 2021 (11:59pm AOE)

Workshop date: July 23, 2021

Call for Papers (CFP) and submission instructions: https://sites.google.com/view/ml4data/call-for-papers

Submission site: https://cmt3.research.microsoft.com/ICML2021ML4data

Follow us on twitter: https://twitter.com/ml4data

Schedule

The following is the workshop schedule (all in Pacific Time) on Friday, July 23, 2021:

• 08:00 - 08:10 - Opening Remarks

• 08:10 - 08:50 - Invited Talk: David Alvarez-Melis: Comparing, Transforming, and Optimizing Datasets with Optimal Transport

• 08:50 - 09:30 - Invited Talk: Lora Aroyo: TBA

• 09:30 - 09:45 - Contributed Oral: Myra Cheng: SNoB: Social Norm Bias of “Fair” Algorithms

• 09:45 - 10:00 - Contributed Oral: Hari Prasanna Das: CDCGen: Cross-Domain Conditional Generation via Normalizing Flows and Adversarial Training

• 10:00 - 10:20 - Coffee Break

• 10:20 - 11:00 - Invited Talk: Eric Xing: A Data-Centric View for Composable Natural Language Processing​

• 11:00 - 11:40 - Invited Talk: Kamalika Chaudhuri: TBA

• 11:40 - 12:30 - Poster Session

• 12:30 - 13:30 - Lunch Break

• 13:30 - 14:10 - Invited Talk: Hoifung Poon: Task-Specific Self-Supervised Learning for Precision Medicine

• 14:10 - 14:50 - Invited Talk: Dawn Song: Towards Building a Responsible Data Economy

• 14:50 - 15:05 - Contributed Oral: Mayana Pereira: An Analysis of the Deployment of Models Trained on Private Tabular Synthetic Data: Unexpected Surprises

• 15:05 - 15:20 - Coffee Break

• 15:20 - 16:00 - Invited Talk: Alex Ratner: Programmatic Weak Supervision for Data-centric AI

• 16:00 - 16:40 - Invited Talk: Kumar Chellapilla: Machine Learning with Humans-in-the-loop (HITL)

• 16:40 - 17:20 - Panel Discussion: Hoifung Poon, Paroma Varma, Kumar Chellapilla, Kamalika Chaudhuri

• 17:20 - 17:25 - Closing Remarks

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