Danica J. Sutherland

Gabriel L. Oliveira

CoRR, 2024

Even Sparser Graph Transformers.

[BibT_eX]

[DOI]

CoRR, 2024

A Theory for Compressibility of Graph Transformers for Transductive Learning.

[BibT_eX]

[DOI]

CoRR, 2024

Understanding Simplicity Bias towards Compositional Mappings via Learning Dynamics.

[BibT_eX]

[DOI]

Yi Ren

CoRR, 2024

Learning Deep Kernels for Non-Parametric Independence Testing.

[BibT_eX]

[DOI]

Nathaniel Xu

Feng Liu

CoRR, 2024

Learning Dynamics of LLM Finetuning.

[BibT_eX]

[DOI]

Yi Ren

CoRR, 2024

Language Model Evolution: An Iterated Learning Perspective.

[BibT_eX]

[DOI]

CoRR, 2024

Practical Kernel Tests of Conditional Independence.

[BibT_eX]

[DOI]

CoRR, 2024

Why Do You Grok? A Theoretical Analysis on Grokking Modular Addition.

[BibT_eX]

[DOI]

Zhiyuan Li

Lei Wu

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Exploring Active Learning in Meta-learning: Enhancing Context Set Labeling.

[BibT_eX]

[DOI]

Jing Wang

Proceedings of the Computer Vision - ECCV 2024, 2024

Generalized Coverage for More Robust Low-Budget Active Learning.

[BibT_eX]

[DOI]

Junhyug Noh

Proceedings of the Computer Vision - ECCV 2024, 2024

2023

Pre-trained Perceptual Features Improve Differentially Private Image Generation.

[BibT_eX]

[DOI]

Trans. Mach. Learn. Res., 2023

AdaFlood: Adaptive Flood Regularization.

[BibT_eX]

[DOI]

CoRR, 2023

Queer In AI: A Case Study in Community-Led Participatory AI.

[BibT_eX]

[DOI]

CoRR, 2023

Differentially Private Neural Tangent Kernels for Privacy-Preserving Data Generation.

[BibT_eX]

[DOI]

CoRR, 2023

Improving Compositional Generalization using Iterated Learning and Simplicial Embeddings.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Exphormer: Sparse Transformers for Graphs.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

A Fast, Well-Founded Approximation to the Empirical Neural Tangent Kernel.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

How to prepare your task head for finetuning.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Efficient Conditionally Invariant Representation Learning.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Queer In AI: A Case Study in Community-Led Participatory AI.

[BibT_eX]

[DOI]

Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, 2023

MMD-B-Fair: Learning Fair Representations with Statistical Testing.

[BibT_eX]

[DOI]

Namrata Deka

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

A Fast, Well-Founded Approximation to the Empirical Neural Tangent Kernel.

[BibT_eX]

[DOI]

CoRR, 2022

Differentially Private Data Generation Needs Better Features.

[BibT_eX]

[DOI]

Frederik Harder

Milad Jalali Asadabadi

Mijung Park

CoRR, 2022

A Non-Asymptotic Moreau Envelope Theory for High-Dimensional Generalized Linear Models.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Evaluating Graph Generative Models with Contrastively Learned Features.

[BibT_eX]

[DOI]

Hamed Shirzad

Kaveh Hassani

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Making Look-Ahead Active Learning Strategies Feasible with Neural Tangent Kernels.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

One Weird Trick to Improve Your Semi-Weakly Supervised Semantic Segmentation Model.

[BibT_eX]

[DOI]

Junhyug Noh

Milad Jalali Asadabadi

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

Better Supervisory Signals by Observing Learning Paths.

[BibT_eX]

[DOI]

Yi Ren

Shangmin Guo

Proceedings of the Tenth International Conference on Learning Representations, 2022

Object Discovery via Contrastive Learning for Weakly Supervised Object Detection.

[BibT_eX]

[DOI]

Proceedings of the Computer Vision - ECCV 2022, 2022

2021

POT: Python Optimal Transport.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2021

Optimistic Rates: A Unifying Theory for Interpolation Learning and Regularization in Linear Regression.

[BibT_eX]

[DOI]

CoRR, 2021

Meta Two-Sample Testing: Learning Kernels for Testing with Limited Data.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Self-Supervised Learning with Kernel Dependence Maximization.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Uniform Convergence of Interpolators: Gaussian Width, Norm Bounds and Benign Overfitting.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Does Invariant Risk Minimization Capture Invariance?

[BibT_eX]

[DOI]

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

On Uniform Convergence and Low-Norm Interpolation Learning.

[BibT_eX]

[DOI]

Lijia Zhou

Nati Srebro

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Learning Deep Kernels for Non-Parametric Two-Sample Tests.

[BibT_eX]

[DOI]

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Unbiased estimators for the variance of MMD estimators.

[BibT_eX]

[DOI]

CoRR, 2019

Learning deep kernels for exponential family densities.

[BibT_eX]

[DOI]

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

On gradient regularizers for MMD GANs.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Demystifying MMD GANs.

[BibT_eX]

[DOI]

Proceedings of the 6th International Conference on Learning Representations, 2018

Efficient and principled score estimation with Nyström kernel exponential families.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

Bayesian Approaches to Distribution Regression.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

Efficient and principled score estimation.

[BibT_eX]

[DOI]

CoRR, 2017

Fixing an error in Caponnetto and de Vito (2007).

[BibT_eX]

[DOI]

CoRR, 2017

Bayesian Distribution Regression.

[BibT_eX]

[DOI]

CoRR, 2017

Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy.

[BibT_eX]

[DOI]

Proceedings of the 5th International Conference on Learning Representations, 2017

2016

Linear-Time Learning on Distributions with Approximate Kernel Embeddings.

[BibT_eX]

[DOI]

Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015

Deep Mean Maps.

[BibT_eX]

[DOI]

CoRR, 2015

On the Error of Random Fourier Features.

[BibT_eX]

[DOI]

Jeff G. Schneider

Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, 2015

Active Pointillistic Pattern Search.

[BibT_eX]

[DOI]

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2013

Active learning and search on low-rank matrices.

[BibT_eX]

[DOI]