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Found 17 papers, 13 papers with code
Asymptotically free sketched ridge ensembles: Risks, cross-validation, and tuning
We also propose an "ensemble trick" whereby the risk for unsketched ridge regression can be efficiently estimated via GCV using small sketched ridge ensembles.
An Adaptive Tangent Feature Perspective of Neural Networks
In order to better understand feature learning in neural networks, we propose a framework for understanding linear models in tangent feature space where the features are allowed to be transformed during training.
Self-Consuming Generative Models Go MAD
Seismic advances in generative AI algorithms for imagery, text, and other data types has led to the temptation to use synthetic data to train next-generation models.
WIRE: Wavelet Implicit Neural Representations
Implicit neural representations (INRs) have recently advanced numerous vision-related areas.
TITAN: Bringing The Deep Image Prior to Implicit Representations
We study the interpolation capabilities of implicit neural representations (INRs) of images.
Monotonic Risk Relationships under Distribution Shifts for Regularized Risk Minimization
Machine learning systems are often applied to data that is drawn from a different distribution than the training distribution.
A Blessing of Dimensionality in Membership Inference through Regularization
Is overparameterization a privacy liability?
The Flip Side of the Reweighted Coin: Duality of Adaptive Dropout and Regularization
Among the most successful methods for sparsifying deep (neural) networks are those that adaptively mask the network weights throughout training.
Extreme Compressed Sensing of Poisson Rates from Multiple Measurements
Here, we present the first exploration of the MMV problem where signals are independently drawn from a sparse, multivariate Poisson distribution.
The Common Intuition to Transfer Learning Can Win or Lose: Case Studies for Linear Regression
We define a transfer learning approach to the target task as a linear regression optimization with a regularization on the distance between the to-be-learned target parameters and the already-learned source parameters.
Wearing a MASK: Compressed Representations of Variable-Length Sequences Using Recurrent Neural Tangent Kernels
High dimensionality poses many challenges to the use of data, from visualization and interpretation, to prediction and storage for historical preservation.
The Implicit Regularization of Ordinary Least Squares Ensembles
Ensemble methods that average over a collection of independent predictors that are each limited to a subsampling of both the examples and features of the training data command a significant presence in machine learning, such as the ever-popular random forest, yet the nature of the subsampling effect, particularly of the features, is not well understood.
Implicit Rugosity Regularization via Data Augmentation
Deep (neural) networks have been applied productively in a wide range of supervised and unsupervised learning tasks.
Thresholding Graph Bandits with GrAPL
The main goal is to efficiently identify a subset of arms in a multi-armed bandit problem whose means are above a specified threshold.
Adaptive Estimation for Approximate k-Nearest-Neighbor Computations
Algorithms often carry out equally many computations for "easy" and "hard" problem instances.
Ultra Large-Scale Feature Selection using Count-Sketches
We demonstrate that MISSION accurately and efficiently performs feature selection on real-world, large-scale datasets with billions of dimensions.
MISSION: Ultra Large-Scale Feature Selection using Count-Sketches
We demonstrate that MISSION accurately and efficiently performs feature selection on real-world, large-scale datasets with billions of dimensions.
