Frederick Liu
12 papers · 2016–2023 · 6 venues · across top CS/AI venues
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Achievements
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Research topics
Machine Learning
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Artificial Intelligence
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Core AI Β· Artificial Intelligence
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Core Methods
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Deep Learning
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Natural Language Processing
(4)
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(2)
Optimization & Theory
(2)
Computer Vision
(1)
Mathematics & Optimization
(1)
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(1)
Generation
(1)
Keywords
kernel approximation
(2)
random feature
(2)
training data
(2)
model interpretability
(2)
gaussian kernel
(2)
text classification
(2)
language model
(2)
influence function
(2)
softmax kernel
(2)
knowledge distillation
(1)
factual knowledge
(1)
lexical ambiguity
(1)
explainable ai
(1)
feature importance
(1)
stochastic gradient descent
(1)
information retrieval
(1)
prior knowledge
(1)
neural machine translation
(1)
ensemble learning
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error detection
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Papers
Augmentation with Projection: Towards an Effective and Efficient Data Augmentation Paradigm for Distillation
ICLR 2023
Dense-Exponential Random Features: Sharp Positive Estimators of the Gaussian Kernel
NeurIPS 2023
Chefs' Random Tables: Non-Trigonometric Random Features
NeurIPS 2022
First is Better Than Last for Language Data Influence
NeurIPS 2022
Threading the Needle of On and Off-Manifold Value Functions for Shapley Explanations
AISTATS 2022
Towards Tracing Knowledge in Language Models Back to the Training Data
EMNLP 2022
Detecting Errors and Estimating Accuracy on Unlabeled Data with Self-training Ensembles
NeurIPS 2021
Estimating Training Data Influence by Tracing Gradient Descent
NeurIPS 2020
Incorporating Priors with Feature Attribution on Text Classification
ACL 2019
Handling Homographs in Neural Machine Translation
NAACL 2018
Learning Character-level Compositionality with Visual Features
ACL 2017
The Creation and Analysis of a Website Privacy Policy Corpus
ACL 2016