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09/08/2023
Zero-Shot Robustification of Zero-Shot Models With Foundation Models
Zero-shot inference is a powerful paradigm that enables the use of large...
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07/26/2023
Skill-it! A Data-Driven Skills Framework for Understanding and Training Language Models
The quality of training data impacts the performance of pre-trained larg...
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07/23/2023
Geometry-Aware Adaptation for Pretrained Models
Machine learning models – including prominent zero-shot models – are oft...
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07/20/2023
Embroid: Unsupervised Prediction Smoothing Can Improve Few-Shot Classification
Recent work has shown that language models' (LMs) prompt-based learning ...
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03/30/2023
Mitigating Source Bias for Fairer Weak Supervision
Weak supervision overcomes the label bottleneck, enabling efficient deve...
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03/13/2023
Domain Generalization via Nuclear Norm Regularization
The ability to generalize to unseen domains is crucial for machine learn...
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12/20/2022
Resonant Anomaly Detection with Multiple Reference Datasets
An important class of techniques for resonant anomaly detection in high ...
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11/24/2022
Lifting Weak Supervision To Structured Prediction
Weak supervision (WS) is a rich set of techniques that produce pseudolab...
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11/22/2022
Good Data from Bad Models : Foundations of Threshold-based Auto-labeling
Creating large-scale high-quality labeled datasets is a major bottleneck...
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10/05/2022
Ask Me Anything: A simple strategy for prompting language models
Large language models (LLMs) transfer well to new tasks out-of-the-box s...
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08/30/2022
AutoWS-Bench-101: Benchmarking Automated Weak Supervision with 100 Labels
Weak supervision (WS) is a powerful method to build labeled datasets for...
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03/24/2022
Shoring Up the Foundations: Fusing Model Embeddings and Weak Supervision
Foundation models offer an exciting new paradigm for constructing models...
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12/07/2021
Universalizing Weak Supervision
Weak supervision (WS) frameworks are a popular way to bypass hand-labeli...
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03/03/2021
Comparing the Value of Labeled and Unlabeled Data in Method-of-Moments Latent Variable Estimation
Labeling data for modern machine learning is expensive and time-consumin...
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06/26/2020
Train and You'll Miss It: Interactive Model Iteration with Weak Supervision and Pre-Trained Embeddings
Our goal is to enable machine learning systems to be trained interactive...
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05/01/2020
Low-Dimensional Hyperbolic Knowledge Graph Embeddings
Knowledge graph (KG) embeddings learn low-dimensional representations of...
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04/11/2020
Ivy: Instrumental Variable Synthesis for Causal Inference
A popular way to estimate the causal effect of a variable x on y from ob...
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02/27/2020
Fast and Three-rious: Speeding Up Weak Supervision with Triplet Methods
Weak supervision is a popular method for building machine learning model...
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10/21/2019
Multi-Resolution Weak Supervision for Sequential Data
Since manually labeling training data is slow and expensive, recent indu...
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03/14/2019
Learning Dependency Structures for Weak Supervision Models
Labeling training data is a key bottleneck in the modern machine learnin...
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10/05/2018
Training Complex Models with Multi-Task Weak Supervision
As machine learning models continue to increase in complexity, collectin...
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04/10/2018
Representation Tradeoffs for Hyperbolic Embeddings
Hyperbolic embeddings offer excellent quality with few dimensions when e...
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12/19/2017
Codes Correcting Two Deletions
In this work, we investigate the problem of constructing codes capable o...
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03/08/2017