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Paroma Varma

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3 papers

2 author rows

ICML Conference 2019 Conference Paper

Learning Dependency Structures for Weak Supervision Models

Paroma Varma
Frederic Sala
Ann He
Alexander Ratner
Christopher Ré

Labeling training data is a key bottleneck in the modern machine learning pipeline. Recent weak supervision approaches combine labels from multiple noisy sources by estimating their accuracies without access to ground truth labels; however, estimating the dependencies among these sources is a critical challenge. We focus on a robust PCA-based algorithm for learning these dependency structures, establish improved theoretical recovery rates, and outperform existing methods on various real-world tasks. Under certain conditions, we show that the amount of unlabeled data needed can scale sublinearly or even logarithmically with the number of sources m, improving over previous efforts that ignore the sparsity pattern in the dependency structure and scale linearly in m. We provide an information-theoretic lower bound on the minimum sample complexity of the weak supervision setting. Our method outperforms weak supervision approaches that assume conditionally-independent sources by up to 4. 64 F1 points and previous structure learning approaches by up to 4. 41 F1 points on real-world relation extraction and image classification tasks.

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NeurIPS Conference 2019 Conference Paper

Multi-Resolution Weak Supervision for Sequential Data

Paroma Varma
Frederic Sala
Shiori Sagawa
Jason Fries
Daniel Fu
Saelig Khattar
Ashwini Ramamoorthy
Ke Xiao

Since manually labeling training data is slow and expensive, recent industrial and scientific research efforts have turned to weaker or noisier forms of supervision sources. However, existing weak supervision approaches fail to model multi-resolution sources for sequential data, like video, that can assign labels to individual elements or collections of elements in a sequence. A key challenge in weak supervision is estimating the unknown accuracies and correlations of these sources without using labeled data. Multi-resolution sources exacerbate this challenge due to complex correlations and sample complexity that scales in the length of the sequence. We propose Dugong, the first framework to model multi-resolution weak supervision sources with complex correlations to assign probabilistic labels to training data. Theoretically, we prove that Dugong, under mild conditions, can uniquely recover the unobserved accuracy and correlation parameters and use parameter sharing to improve sample complexity. Our method assigns clinician-validated labels to population-scale biomedical video repositories, helping outperform traditional supervision by 36. 8 F1 points and addressing a key use case where machine learning has been severely limited by the lack of expert labeled data. On average, Dugong improves over traditional supervision by 16. 0 F1 points and existing weak supervision approaches by 24. 2 F1 points across several video and sensor classification tasks.

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NeurIPS Conference 2017 Conference Paper

Inferring Generative Model Structure with Static Analysis

Paroma Varma
Bryan He
Payal Bajaj
Nishith Khandwala
Imon Banerjee
Daniel Rubin
Christopher Ré

Obtaining enough labeled data to robustly train complex discriminative models is a major bottleneck in the machine learning pipeline. A popular solution is combining multiple sources of weak supervision using generative models. The structure of these models affects the quality of the training labels, but is difficult to learn without any ground truth labels. We instead rely on weak supervision sources having some structure by virtue of being encoded programmatically. We present Coral, a paradigm that infers generative model structure by statically analyzing the code for these heuristics, thus significantly reducing the amount of data required to learn structure. We prove that Coral's sample complexity scales quasilinearly with the number of heuristics and number of relations identified, improving over the standard sample complexity, which is exponential in n for learning n-th degree relations. Empirically, Coral matches or outperforms traditional structure learning approaches by up to 3. 81 F1 points. Using Coral to model dependencies instead of assuming independence results in better performance than a fully supervised model by 3. 07 accuracy points when heuristics are used to label radiology data without ground truth labels.

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