Search Results for author: Alexander Ratner

Found 22 papers, 11 papers with code

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

2 code implementations28 Jun 2023 Yue Yu, Yuchen Zhuang, Jieyu Zhang, Yu Meng, Alexander Ratner, Ranjay Krishna, Jiaming Shen, Chao Zhang

Large language models (LLMs) have been recently leveraged as training data generators for various natural language processing (NLP) tasks.

Language Modelling Large Language Model

MaskSearch: Querying Image Masks at Scale

no code implementations3 May 2023 Dong He, Jieyu Zhang, Maureen Daum, Alexander Ratner, Magdalena Balazinska

Machine learning tasks over image databases often generate masks that annotate image content (e. g., saliency maps, segmentation maps) and enable a variety of applications (e. g., determine if a model is learning spurious correlations or if an image was maliciously modified to mislead a model).

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

1 code implementation3 May 2023 Cheng-Yu Hsieh, Chun-Liang Li, Chih-Kuan Yeh, Hootan Nakhost, Yasuhisa Fujii, Alexander Ratner, Ranjay Krishna, Chen-Yu Lee, Tomas Pfister

Third, we reduce both the model size and the amount of data required to outperform LLMs; our finetuned 770M T5 model outperforms the few-shot prompted 540B PaLM model using only 80% of available data on a benchmark, whereas standard finetuning the same T5 model struggles to match even by using 100% of the dataset.

Leveraging Instance Features for Label Aggregation in Programmatic Weak Supervision

2 code implementations6 Oct 2022 Jieyu Zhang, Linxin Song, Alexander Ratner

In particular, it is built on a mixture of Bayesian label models, each corresponding to a global pattern of correlation, and the coefficients of the mixture components are predicted by a Gaussian Process classifier based on instance features.

Variational Inference

Binary Classification with Positive Labeling Sources

no code implementations2 Aug 2022 Jieyu Zhang, Yujing Wang, Yaming Yang, Yang Luo, Alexander Ratner

Thus, in this work, we study the application of WS on binary classification tasks with positive labeling sources only.

Benchmarking Binary Classification +1

Understanding Programmatic Weak Supervision via Source-aware Influence Function

no code implementations25 May 2022 Jieyu Zhang, Haonan Wang, Cheng-Yu Hsieh, Alexander Ratner

Programmatic Weak Supervision (PWS) aggregates the source votes of multiple weak supervision sources into probabilistic training labels, which are in turn used to train an end model.

Nemo: Guiding and Contextualizing Weak Supervision for Interactive Data Programming

1 code implementation2 Mar 2022 Cheng-Yu Hsieh, Jieyu Zhang, Alexander Ratner

Weak Supervision (WS) techniques allow users to efficiently create large training datasets by programmatically labeling data with heuristic sources of supervision.

A Survey on Programmatic Weak Supervision

1 code implementation11 Feb 2022 Jieyu Zhang, Cheng-Yu Hsieh, Yue Yu, Chao Zhang, Alexander Ratner

Labeling training data has become one of the major roadblocks to using machine learning.

WRENCH: A Comprehensive Benchmark for Weak Supervision

1 code implementation23 Sep 2021 Jieyu Zhang, Yue Yu, Yinghao Li, Yujing Wang, Yaming Yang, Mao Yang, Alexander Ratner

To address these problems, we introduce a benchmark platform, WRENCH, for thorough and standardized evaluation of WS approaches.

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

2 code implementations NeurIPS 2019 Vincent S. Chen, Sen Wu, Zhenzhen Weng, Alexander Ratner, Christopher Ré

In real-world machine learning applications, data subsets correspond to especially critical outcomes: vulnerable cyclist detections are safety-critical in an autonomous driving task, and "question" sentences might be important to a dialogue agent's language understanding for product purposes.

Autonomous Driving BIG-bench Machine Learning

Improving Sample Complexity with Observational Supervision

no code implementations ICLR Workshop LLD 2019 Khaled Saab, Jared Dunnmon, Alexander Ratner, Daniel Rubin, Christopher Re

Supervised machine learning models for high-value computer vision applications such as medical image classification often require large datasets labeled by domain experts, which are slow to collect, expensive to maintain, and static with respect to changes in the data distribution.

Image Classification Medical Image Classification

Snorkel: Rapid Training Data Creation with Weak Supervision

2 code implementations28 Nov 2017 Alexander Ratner, Stephen H. Bach, Henry Ehrenberg, Jason Fries, Sen Wu, Christopher Ré

In a user study, subject matter experts build models 2. 8x faster and increase predictive performance an average 45. 5% versus seven hours of hand labeling.

BIG-bench Machine Learning

Data Programming: Creating Large Training Sets, Quickly

4 code implementations NeurIPS 2016 Alexander Ratner, Christopher De Sa, Sen Wu, Daniel Selsam, Christopher Ré

Additionally, in initial user studies we observed that data programming may be an easier way for non-experts to create machine learning models when training data is limited or unavailable.

BIG-bench Machine Learning Slot Filling

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