Alexander Ratner

According to our database¹, Alexander Ratner authored at least 42 papers between 2016 and 2023.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of three.

Timeline

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In proceedings

Article

PhD thesis

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Other

Bibliography

2023

Tool Documentation Enables Zero-Shot Tool-Usage with Large Language Models.

[BibT_eX]

[DOI]

CoRR, 2023

Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias.

[BibT_eX]

[DOI]

CoRR, 2023

MaskSearch: Querying Image Masks at Scale.

[BibT_eX]

[DOI]

CoRR, 2023

On the Trade-off of Intra-/Inter-class Diversity for Supervised Pre-training.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Characterizing the Impacts of Semi-supervised Learning for Weak Supervision.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

DataComp: In search of the next generation of multimodal datasets.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Leveraging Instance Features for Label Aggregation in Programmatic Weak Supervision.

[BibT_eX]

[DOI]

Jieyu Zhang

Linxin Song

Alex Ratner

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes.

[BibT_eX]

[DOI]

Proceedings of the Findings of the Association for Computational Linguistics: ACL 2023, 2023

2022

Nemo: Guiding and Contextualizing Weak Supervision for Interactive Data Programming.

[BibT_eX]

[DOI]

Cheng-Yu Hsieh

Jieyu Zhang

Alexander J. Ratner

Proc. VLDB Endow., 2022

A Survey on Programmatic Weak Supervision.

[BibT_eX]

[DOI]

CoRR, 2022

Understanding Programmatic Weak Supervision via Source-aware Influence Function.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Creating Training Sets via Weak Indirect Supervision.

[BibT_eX]

[DOI]

Proceedings of the Tenth International Conference on Learning Representations, 2022

Binary Classification with Positive Labeling Sources.

[BibT_eX]

[DOI]

Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022

2021

Proceedings of the First Workshop on Weakly Supervised Learning (WeaSuL).

[BibT_eX]

[DOI]

CoRR, 2021

WRENCH: A Comprehensive Benchmark for Weak Supervision.

[BibT_eX]

[DOI]

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

2020

Cross-Modal Data Programming Enables Rapid Medical Machine Learning.

[BibT_eX]

[DOI]

Christopher Lee-Messer

Matthew P. Lungren

Daniel L. Rubin

Christopher Ré

Patterns, 2020

Extracting chemical reactions from text using Snorkel.

[BibT_eX]

[DOI]

Emily K. Mallory

Matthieu de Rochemonteix

BMC Bioinform., 2020

2019

Accelerating machine learning with training data management.

[BibT_eX]

[DOI]

Alexander Ratner

PhD thesis, 2019

Slice-based Learning: A Programming Model for Residual Learning in Critical Data Slices.

[BibT_eX]

[DOI]

CoRR, 2019

SysML: The New Frontier of Machine Learning Systems.

[BibT_eX]

[DOI]

Alexandros G. Dimakis

Anastasios Kyrillidis

Dimitris S. Papailiopoulos

Shivaram Venkataraman

CoRR, 2019

Osprey: Weak Supervision of Imbalanced Extraction Problems without Code.

[BibT_eX]

[DOI]

Proceedings of the 3rd International Workshop on Data Management for End-to-End Machine Learning, 2019

Snorkel DryBell: A Case Study in Deploying Weak Supervision at Industrial Scale.

[BibT_eX]

[DOI]

Proceedings of the 2019 International Conference on Management of Data, 2019

Slice-based Learning: A Programming Model for Residual Learning in Critical Data Slices.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Doubly Weak Supervision of Deep Learning Models for Head CT.

[BibT_eX]

[DOI]

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2019, 2019

Learning Dependency Structures for Weak Supervision Models.

[BibT_eX]

[DOI]

Proceedings of the 36th International Conference on Machine Learning, 2019

A Kernel Theory of Modern Data Augmentation.

[BibT_eX]

[DOI]

Proceedings of the 36th International Conference on Machine Learning, 2019

The Role of Massively Multi-Task and Weak Supervision in Software 2.0.

[BibT_eX]

[DOI]

Alexander J. Ratner

Braden Hancock

Christopher Ré

Proceedings of the 9th Biennial Conference on Innovative Data Systems Research, 2019

Training Complex Models with Multi-Task Weak Supervision.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

Knowledge Base Construction in the Machine-learning Era.

[BibT_eX]

[DOI]

Alexander Ratner

Christopher Ré

ACM Queue, 2018

Research for practice: knowledge base construction in the machine-learning era.

[BibT_eX]

[DOI]

Alexander Ratner

Christopher Ré

Peter Bailis

Commun. ACM, 2018

Snorkel MeTaL: Weak Supervision for Multi-Task Learning.

[BibT_eX]

[DOI]

Proceedings of the Second Workshop on Data Management for End-To-End Machine Learning, 2018

2017

Incremental knowledge base construction using DeepDive.

[BibT_eX]

[DOI]

VLDB J., 2017

Snorkel: Rapid Training Data Creation with Weak Supervision.

[BibT_eX]

[DOI]

Proc. VLDB Endow., 2017

SwellShark: A Generative Model for Biomedical Named Entity Recognition without Labeled Data.

[BibT_eX]

[DOI]

CoRR, 2017

Snorkel: Fast Training Set Generation for Information Extraction.

[BibT_eX]

[DOI]

Proceedings of the 2017 ACM International Conference on Management of Data, 2017

Learning to Compose Domain-Specific Transformations for Data Augmentation.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Learning the Structure of Generative Models without Labeled Data.

[BibT_eX]

[DOI]

Proceedings of the 34th International Conference on Machine Learning, 2017

Snorkel: A System for Lightweight Extraction.

[BibT_eX]

[DOI]

Proceedings of the 8th Biennial Conference on Innovative Data Systems Research, 2017

2016

DeepDive: Declarative Knowledge Base Construction.

[BibT_eX]

[DOI]

SIGMOD Rec., 2016

Data programming with DDLite: putting humans in a different part of the loop.

[BibT_eX]

[DOI]

Proceedings of the Workshop on Human-In-the-Loop Data Analytics, 2016

Data Programming: Creating Large Training Sets, Quickly.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Alexander Ratner

Timeline

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