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Found 22 papers, 11 papers with code
One Weird Trick to Improve Your Semi-Weakly Supervised Semantic Segmentation Model
Semi-weakly supervised semantic segmentation (SWSSS) aims to train a model to identify objects in images based on a small number of images with pixel-level labels, and many more images with only image-level labels.
Better Supervisory Signals by Observing Learning Paths
Observing the learning path not only provides a new perspective for understanding knowledge distillation, overfitting, and learning dynamics, but also reveals that the supervisory signal of a teacher network can be very unstable near the best points in training on real tasks.
Optimistic Rates: A Unifying Theory for Interpolation Learning and Regularization in Linear Regression
We study a localized notion of uniform convergence known as an "optimistic rate" (Panchenko 2002; Srebro et al. 2010) for linear regression with Gaussian data.
Uniform Convergence of Interpolators: Gaussian Width, Norm Bounds, and Benign Overfitting
We consider interpolation learning in high-dimensional linear regression with Gaussian data, and prove a generic uniform convergence guarantee on the generalization error of interpolators in an arbitrary hypothesis class in terms of the class's Gaussian width.
Self-Supervised Learning with Kernel Dependence Maximization
We approach self-supervised learning of image representations from a statistical dependence perspective, proposing Self-Supervised Learning with the Hilbert-Schmidt Independence Criterion (SSL-HSIC).
Meta Two-Sample Testing: Learning Kernels for Testing with Limited Data
In realistic scenarios with very limited numbers of data samples, however, it can be challenging to identify a kernel powerful enough to distinguish complex distributions.
Uniform Convergence of Interpolators: Gaussian Width, Norm Bounds and Benign Overfitting
We consider interpolation learning in high-dimensional linear regression with Gaussian data, and prove a generic uniform convergence guarantee on the generalization error of interpolators in an arbitrary hypothesis class in terms of the class’s Gaussian width.
Does Invariant Risk Minimization Capture Invariance?
We show that the Invariant Risk Minimization (IRM) formulation of Arjovsky et al. (2019) can fail to capture "natural" invariances, at least when used in its practical "linear" form, and even on very simple problems which directly follow the motivating examples for IRM.
On Uniform Convergence and Low-Norm Interpolation Learning
But we argue we can explain the consistency of the minimal-norm interpolator with a slightly weaker, yet standard, notion: uniform convergence of zero-error predictors in a norm ball.
Learning Deep Kernels for Non-Parametric Two-Sample Tests
We propose a class of kernel-based two-sample tests, which aim to determine whether two sets of samples are drawn from the same distribution.
Ranked #1 on
Two-sample testing
on HIGGS Data Set
Unbiased estimators for the variance of MMD estimators
The maximum mean discrepancy (MMD) is a kernel-based distance between probability distributions useful in many applications (Gretton et al. 2012), bearing a simple estimator with pleasing computational and statistical properties.
Learning deep kernels for exponential family densities
The kernel exponential family is a rich class of distributions, which can be fit efficiently and with statistical guarantees by score matching.
On gradient regularizers for MMD GANs
We propose a principled method for gradient-based regularization of the critic of GAN-like models trained by adversarially optimizing the kernel of a Maximum Mean Discrepancy (MMD).
Ranked #74 on
Image Generation
on CIFAR-10
Demystifying MMD GANs
We investigate the training and performance of generative adversarial networks using the Maximum Mean Discrepancy (MMD) as critic, termed MMD GANs.
Efficient and principled score estimation with Nyström kernel exponential families
We propose a fast method with statistical guarantees for learning an exponential family density model where the natural parameter is in a reproducing kernel Hilbert space, and may be infinite-dimensional.
Bayesian Approaches to Distribution Regression
Distribution regression has recently attracted much interest as a generic solution to the problem of supervised learning where labels are available at the group level, rather than at the individual level.
Fixing an error in Caponnetto and de Vito (2007)
The seminal paper of Caponnetto and de Vito (2007) provides minimax-optimal rates for kernel ridge regression in a very general setting.
Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy
In this context, the MMD may be used in two roles: first, as a discriminator, either directly on the samples, or on features of the samples.
Understanding the 2016 US Presidential Election using ecological inference and distribution regression with census microdata
We combine fine-grained spatially referenced census data with the vote outcomes from the 2016 US presidential election.
Deep Mean Maps
The use of distributions and high-level features from deep architecture has become commonplace in modern computer vision.
Linear-time Learning on Distributions with Approximate Kernel Embeddings
This work develops the first random features for pdfs whose dot product approximates kernels using these non-Euclidean metrics, allowing estimators using such kernels to scale to large datasets by working in a primal space, without computing large Gram matrices.
Kernels on Sample Sets via Nonparametric Divergence Estimates
Most machine learning algorithms, such as classification or regression, treat the individual data point as the object of interest.
