Stephen Mussmann
11 papers · 2016–2024 · 6 venues · across top CS/AI venues
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Research topics
Machine Learning
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Learning Types Β· Machine Learning
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Artificial Intelligence
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Optimization & Theory
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Core AI
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Deep Learning
(3)
Application Areas
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Natural Language Processing
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Computer Science
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Bayesian & Probabilistic
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Keywords
active learning
(5)
uncertainty sampling
(2)
zero-shot learning
(1)
similarity search
(1)
logistic regression
(1)
model misspecification
(1)
coreset selection
(1)
generalized binary search
(1)
question answering
(1)
interpretable machine learning
(1)
weak supervision
(1)
sequential hypothesis testing
(1)
experimental design
(1)
probabilistic inference
(1)
semi-supervised learning
(1)
query complexity
(1)
logistic loss
(1)
zero-one loss
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amortized inference
(1)
stochastic gradient descent
(1)
Papers
An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models
ACL 2024
DataComp: In search of the next generation of multimodal datasets
NeurIPS 2023
Comparing the Value of Labeled and Unlabeled Data in Method-of-Moments Latent Variable Estimation
AISTATS 2021
Concept Bottleneck Models
ICML 2020
On the Importance of Adaptive Data Collection for Extremely Imbalanced Pairwise Tasks
EMNLP 2020
Selection via Proxy: Efficient Data Selection for Deep Learning
ICLR 2020
Generalized Binary Search For Split-Neighborly Problems
AISTATS 2018
On the Relationship between Data Efficiency and Error for Uncertainty Sampling
ICML 2018
The price of debiasing automatic metrics in natural language evalaution
ACL 2018
Uncertainty Sampling is Preconditioned Stochastic Gradient Descent on Zero-One Loss
NeurIPS 2018
Learning and Inference via Maximum Inner Product Search
ICML 2016