Danica J. Sutherland
31 papers · 2015–2025 · 7 conferences · across top CS/AI conferences
Achievements
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π§ Keyword Pioneer πΊοΈ Taxonomy Completionist (10) π Interdisciplinary Bridge π Renaissance Researcher (5) π£ Hot Topic Early Bird
π
Renaissance Researcher
(5)
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Cross-Pollinator
(11)
πΊοΈ
Taxonomy Completionist
(10)
π₯
Mega-Team
(22)
π
Triple Crown
ποΈ
Keyword Collector
(106)
β‘
Prolific Year
(6)
π
Conference Pioneer
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Trend Setter
π
Century Club
(31)
π₯
Unstoppable
(8)
β
The Questioner
Conferences
NIPS (11)
ICLR (6)
ICML (5)
AISTATS (4)
ECCV (3)
IJCAI (1)
JMLR (1)
Top co-authors
Research topics
Keywords
kernel methods
(4)
generalization bound
(3)
maximum mean discrepancy
(3)
graph neural network
(2)
interpolation learning
(2)
neural tangent kernel
(2)
graph transformer
(2)
active learning
(2)
representation learning
(2)
deep neural network
(2)
iterated learning
(2)
deep kernel
(2)
contrastive learning
(2)
bayesian inference
(2)
density estimation
(2)
uniform convergence
(2)
score matching
(2)
linear regression
(2)
sparse attention
(2)
transfer learning
(1)
Papers
Uncertainty Herding: One Active Learning Method for All Label Budgets
ICLR 2025
Learning Dynamics of LLM Finetuning
ICLR 2025
Bias Amplification in Language Model Evolution: An Iterated Learning Perspective
NIPS 2024
Even Sparser Graph Transformers
NIPS 2024
Why Do You Grok? A Theoretical Analysis on Grokking Modular Addition
ICML 2024
Generalized Coverage for More Robust Low-Budget Active Learning
ECCV 2024
Exploring Active Learning in Meta-Learning: Enhancing Context Set Labeling
ECCV 2024
A Fast, Well-Founded Approximation to the Empirical Neural Tangent Kernel
ICML 2023
MMD-B-Fair: Learning Fair Representations with Statistical Testing
AISTATS 2023
Efficient Conditionally Invariant Representation Learning
ICLR 2023
Improving Compositional Generalization using Iterated Learning and Simplicial Embeddings
NIPS 2023
How to prepare your task head for finetuning
ICLR 2023
Exphormer: Sparse Transformers for Graphs
ICML 2023
Making Look-Ahead Active Learning Strategies Feasible with Neural Tangent Kernels
NIPS 2022
Better Supervisory Signals by Observing Learning Paths
ICLR 2022
Object Discovery via Contrastive Learning for Weakly Supervised Object Detection
ECCV 2022
One Weird Trick to Improve Your Semi-Weakly Supervised Semantic Segmentation Model
IJCAI 2022
Evaluating Graph Generative Models with Contrastively Learned Features
NIPS 2022
A Non-Asymptotic Moreau Envelope Theory for High-Dimensional Generalized Linear Models
NIPS 2022
POT: Python Optimal Transport
JMLR 2021
Meta Two-Sample Testing: Learning Kernels for Testing with Limited Data
NIPS 2021
Self-Supervised Learning with Kernel Dependence Maximization
NIPS 2021
Uniform Convergence of Interpolators: Gaussian Width, Norm Bounds and Benign Overfitting
NIPS 2021
Learning Deep Kernels for Non-Parametric Two-Sample Tests
ICML 2020
On Uniform Convergence and Low-Norm Interpolation Learning
NIPS 2020
Learning deep kernels for exponential family densities
ICML 2019
On gradient regularizers for MMD GANs
NIPS 2018
Bayesian Approaches to Distribution Regression
AISTATS 2018
Efficient and principled score estimation with NystrΓΆm kernel exponential families
AISTATS 2018
Demystifying MMD GANs
ICLR 2018
Active Pointillistic Pattern Search
AISTATS 2015