Paroma Varma

Ph. D. Student - Electrical Engineering - Stanford University

paroma [at] stanford [dot] edu - @paroma_varma

I am a fourth year Ph.D. student advised by Prof. Christopher Ré and affiliated with the DAWN, SAIL, and StatML groups. I am supported by the Stanford Graduate Fellowship and the National Science Foundation Graduate Research Fellowship.

My research interests revolve around weak supervision, or using high-level knowledge in the form of noisy labeling sources to efficiently label massive datasets required to train machine learning models. This includes systems like Snorkel and Snorkel MeTaL that allow users to write labeling functions rather than label data by hand. In this context, I’m also interested in using developer exhaust, byproducts of the data analytics pipeline, to simplify complex statistical and search-based problems.

Latest News

11/29/18 Looking forward to co-organizing the Learning from Limited Labeled Data Workshop at ICLR 2019!
11/16/18 Over 50 papers accepted to our Relational Representation Learning Workshop at NeurIPS 2018!
11/06/18 Excited to help with the Snorkel Biomedical Knowledge Base Construction Workshop!
10/15/18 Snuba: Automating Weak Supervision to Label Training Data accepted to VLDB 2019!
09/27/18 Conversations with industry members on debugging machine learning!

Projects

Snuba: Automating Weak Supervision to Label Training Data
Formerly known as Reef, to appear at VLDB 2019

We explore how we can make weak supervision techniques easier to adopt by automating the process of generating noisy labeling heuristics. We introduce a system that takes as input a small, labeled dataset and a larger unlabeled dataset and assigns training labels to the latter automatically. It generates heuristics that each labels only the subset of the data it is accurate for, and iteratively repeats this process until the heuristics together label a large portion of the unlabeled data. We find that this method can outperform weak supervision with user-defined heuristics and crowdsourcing in many cases. [pdf] [code]

Babble Labble: Learning from Natural Language Explanations
ACL 2018, NeurIPS 2017 DEMO

Braden Hancock and I explore how we can use natural language explanations for why crowd workers provide the labels they do to label training data more efficiently. We automatically parse these explanations into executable functions and apply them to large amounts of unlabeled data. We find that collecting explanations allows us to build high quality training sets much faster than collecting labels alone. [pdf] [code] [blogpost] [demo video]

Efficient Model Search using Log Data
DEEM @ SIGMOD 2018

We present preliminary methods that use the logs generated while training complex deep learning models to predict the performance of models with different architectures. We find that without training any new models, we can predict how well a model architecture will perform according to different metrics and within training time constraints. [pdf]

Coral: Enriching Statistical Models with Static Analysis
NeurIPS 2017, NeurIPS ML4H 2017, MED-NeurIPS 2017

We introduce a weak supervision framework to efficiently label image and video training data given a small set of user-defined heuristics. We identify correlations among heuristics using static analysis and incorporate this information into a generative model that can optimally assign probabilistic labels to training data. We apply this method to video querying and medical image classification tasks, outperforming fully supervised models in some cases. [pdf] [blogpost] [video]

Socratic Learning: Finding Latent Subsets in Training Data
HILDA @ SIGMOD 2017, NeurIPS FILM 2016

We explore how we can find latent subsets in training data that affect the behavior of weak supervision sources. We automatically identify these subsets using disagreements between the discriminative and generative models and correct misspecified generative models accordingly. We improve upon existing relation extraction and sentiment analysis tasks and make these latent subsets interpretable for users. [pdf] [workshop] [blogpost] [video]

Publications

2018

Snuba: Automating Weak Supervision to Label Training Data
Paroma Varma and Christopher Ré.
To appear at International Conference on Very Large Databases (VLDB), 2019

Weakly supervised classification of rare aortic valve malformations using unlabeled cardiac MRI sequences
Jason Fries, Paroma Varma, Vincent Chen, Ke Xiao, Heliodoro Tejeda, Saha Priyanka, Jared Dunnmon, Henry Chubb, Shiraz Maskatia, Madalina Fiterau, Scott Delp, Euan Ashley, Christopher Ré and James Priest.

Training Classifiers with Natural Language Explanations
Braden Hancock, Paroma Varma, Stephanie Wang, Percy Liang and Christopher Ré.
In Association for Computational Linguistics (ACL), 2018

Exploring the Utility of Developer Exhaust
Jian Zhang, Max Lam, Stephanie Wang, Paroma Varma, Luigi Nardi, Kunle Olukotun and Christopher Ré.
In Workshop on Data Management for End-to-End Machine Learning (DEEM) at SIGMOD, 2018

2017

Inferring Generative Model Structure with Static Analysis
Paroma Varma, Bryan He, Payal Bajaj, Imon Banerjee, Nishith Khandwala, Daniel L. Rubin and Christopher Ré.
In Neural Information Processing Systems (NeurIPS), 2017

Automated Training Set Generation for Aortic Valve Classification
Vincent Chen, Paroma Varma, Madalina Fiterau, James Priest and Christopher Ré.
In Machine Learning for Health (ML4H), Neural Information Processing Systems (NeurIPS), 2017

Generating Training Labels for Cardiac Phase-Contrast MRI Images
Vincent Chen, Paroma Varma, Madalina Fiterau, James Priest and Christopher Ré.
In Medical Imaging meets NeurIPS (MED-NeurIPS), 2017

Augmenting Generative Models to Incorporate Latent Subsets in Training Data
Paroma Varma, Bryan He, Dan Iter, Peng Xu, Rose Yu, Christopher De Sa, Christopher Ré

Flipper: A Systematic Approach to Debugging Training Sets
Paroma Varma, Dan Iter, Christopher De Sa and Christopher Ré.
In Workshop on Human-In-the-Loop Data Analytics (HILDA) at SIGMOD, 2017

2016

Socratic Learning
Paroma Varma, Rose Yu, Dan Iter, Christopher De Sa, Christopher Ré
In Future of Interactive Learning Machines Workshop (FILM), Neural Information Processing Systems (NeurIPS), 2016

Efficient 3D Deconvolution Microscopy with Proximal Algorithms
Paroma Varma, Gordon Wetzstein
In Computational Optical Sensing and Imaging, Imaging and Applied Optics, 2016

Nonlinear Optimization Algorithm for Partially Coherent Phase Retrieval and Source Recovery
Jingshan Zhong, Lei Tian, Paroma Varma, Laura Waller
In IEEE Transactions on Computational Imaging, 2016

2015

Source Shape Estimation in Partially Coherent Phase Imaging with Defocused Intensity
Jingshan Zhong, Paroma Varma, Lei Tian, Laura Waller
In Computational Optical Sensing and Imaging, Imaging and Applied Optics, 2015

Design of a Domed LED Illuminator for High-Angle Computational Illumination
Zachary Phillips, Gautam Gunjala, Paroma Varma, Jingshan Zhong, Laura Waller
In Imaging Systems and Applications, 2015

In the Past

Previously, I worked on problems related to computational imaging. As an undergraduate at UC Berkeley, I studied phase retrieval via partial coherence illumination and digital holography in Prof. Laura Waller’s Computational Imaging Lab. I also rotated with Prof. Gordon Wetzstein’s Computational Imaging Group and looked at solving 3D deconvolution problems more efficiently.

Teaching

At UC Berkeley, I was a teaching assistant for the first offering of EE16A: Designing Information Devices and Systems and helped develop course material for the class as well. I was also a teaching assistant for EE20: Structure and Interpretation of Signals and Systems.

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