Rachel Ward
18 papers · 2014–2024 · 6 venues · across top CS/AI venues
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🗺️ Taxonomy Completionist (28) 🏃 Academic Marathon · Half 🌍 Conference Polyglot · Silver 🌉 Interdisciplinary Bridge · Bronze 🐣 Hot Topic Early Bird
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Prolific Year · Productive
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Unstoppable · Bronze
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The Questioner
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Century Club · Bronze
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Research topics
Machine Learning
(17)
Optimization & Theory · Machine Learning
(14)
Mathematics & Optimization
(10)
Optimization
(9)
Core Methods
(6)
Learning Types · Machine Learning
(4)
Application Areas
(3)
Mathematics
(3)
Deep Learning
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Artificial Intelligence
(1)
Computer Vision
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Domain-Specific
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Learning Paradigms
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Learning Types · Deep Learning
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Optimization & Theory · Deep Learning
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Keywords
non-convex optimization
(5)
convergence rate
(5)
linear convergence
(4)
stochastic gradient descent
(4)
gradient descent
(4)
adaptive gradient
(3)
data augmentation
(2)
continual learning
(2)
matrix factorization
(2)
polyak-lojasiewicz inequality
(2)
consistency regularization
(2)
matrix completion
(2)
leverage score
(2)
convergence analysis
(2)
semi-supervised learning
(1)
uncertainty quantification
(1)
sample efficiency
(1)
principal component analysis
(1)
importance sampling
(1)
convex optimization
(1)
Papers
Provable Acceleration of Nesterov's Accelerated Gradient for Asymmetric Matrix Factorization and Linear Neural Networks
NeurIPS 2024
Adaptively Weighted Data Augmentation Consistency Regularization for Robust Optimization under Concept Shift
ICML 2023
Cluster-aware Semi-supervised Learning: Relational Knowledge Distillation Provably Learns Clustering
NeurIPS 2023
Convergence of Alternating Gradient Descent for Matrix Factorization
NeurIPS 2023
Nearly Optimal Bounds for Cyclic Forgetting
NeurIPS 2023
Sample Efficiency of Data Augmentation Consistency Regularization
AISTATS 2023
AdaLoss: A Computationally-Efficient and Provably Convergent Adaptive Gradient Method
AAAI 2022
How catastrophic can catastrophic forgetting be in linear regression?
COLT 2022
The Power of Adaptivity in SGD: Self-Tuning Step Sizes with Unbounded Gradients and Affine Variance
COLT 2022
Bootstrapping the Error of Oja's Algorithm
NeurIPS 2021
Streaming k-PCA: Efficient guarantees for Oja’s algorithm, beyond rank-one updates
COLT 2021
AdaGrad stepsizes: Sharp convergence over nonconvex landscapes
JMLR 2020
Implicit Regularization and Convergence for Weight Normalization
NeurIPS 2020
Linear Convergence of Adaptive Stochastic Gradient Descent
AISTATS 2020
AdaGrad Stepsizes: Sharp Convergence Over Nonconvex Landscapes
ICML 2019
Completing Any Low-rank Matrix, Provably
JMLR 2015
Coherent Matrix Completion
ICML 2014
Stochastic Gradient Descent, Weighted Sampling, and the Randomized Kaczmarz algorithm
NeurIPS 2014