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01/15/2023
Who Should Predict? Exact Algorithms For Learning to Defer to Humans
Automated AI classifiers should be able to defer the prediction to a hum...
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10/19/2022
TabLLM: Few-shot Classification of Tabular Data with Large Language Models
We study the application of large language models to zero-shot and few-s...
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06/06/2022
Training Subset Selection for Weak Supervision
Existing weak supervision approaches use all the data covered by weak si...
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05/25/2022
Large Language Models are Zero-Shot Clinical Information Extractors
We show that large language models, such as GPT-3, perform well at zero-...
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02/02/2022
Co-training Improves Prompt-based Learning for Large Language Models
We demonstrate that co-training (Blum Mitchell, 1998) can improve th...
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11/04/2021
Leveraging Time Irreversibility with Order-Contrastive Pre-training
Label-scarce, high-dimensional domains such as healthcare present a chal...
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07/27/2021
Combining Probabilistic Logic and Deep Learning for Self-Supervised Learning
Deep learning has proven effective for various application tasks, but it...
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02/26/2021
Beyond Perturbation Stability: LP Recovery Guarantees for MAP Inference on Noisy Stable Instances
Several works have shown that perturbation stable instances of the MAP i...
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12/23/2020
Self-supervised self-supervision by combining deep learning and probabilistic logic
Labeling training examples at scale is a perennial challenge in machine ...
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11/07/2020
Graph cuts always find a global optimum (with a catch)
We prove that the alpha-expansion algorithm for MAP inference always ret...
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02/25/2020
Statistical Adaptive Stochastic Gradient Methods
We propose a statistical adaptive procedure called SALSA for automatical...
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10/30/2019
Understanding the Role of Momentum in Stochastic Gradient Methods
The use of momentum in stochastic gradient methods has become a widespre...
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09/21/2019
Using Statistics to Automate Stochastic Optimization
Despite the development of numerous adaptive optimizers, tuning the lear...
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10/12/2018
Block Stability for MAP Inference
To understand the empirical success of approximate MAP inference, recent...
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11/06/2017
