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06/01/2023
White-Box Transformers via Sparse Rate Reduction
In this paper, we contend that the objective of representation learning ...
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02/06/2023
Variational Information Pursuit for Interpretable Predictions
There is a growing interest in the machine learning community in develop...
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01/04/2023
Unsupervised Manifold Linearizing and Clustering
Clustering data lying close to a union of low-dimensional manifolds, wit...
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10/01/2022
Learning Globally Smooth Functions on Manifolds
Smoothness and low dimensional structures play central roles in improvin...
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07/03/2022
Interpretable by Design: Learning Predictors by Composing Interpretable Queries
There is a growing concern about typically opaque decision-making with h...
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03/31/2022
Efficient Maximal Coding Rate Reduction by Variational Forms
The principle of Maximal Coding Rate Reduction (MCR^2) has recently been...
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01/22/2022
Implicit Bias of Projected Subgradient Method Gives Provable Robust Recovery of Subspaces of Unknown Codimension
Robust subspace recovery (RSR) is a fundamental problem in robust repres...
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07/19/2021
Wave-Informed Matrix Factorization withGlobal Optimality Guarantees
With the recent success of representation learning methods, which includ...
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11/30/2020
Doubly Stochastic Subspace Clustering
Many state-of-the-art subspace clustering methods follow a two-step proc...
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10/08/2020
A Critique of Self-Expressive Deep Subspace Clustering
Subspace clustering is an unsupervised clustering technique designed to ...
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10/30/2019
On the Regularization Properties of Structured Dropout
Dropout and its extensions (eg. DropBlock and DropConnect) are popular h...
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10/10/2017
An Analysis of Dropout for Matrix Factorization
Dropout is a simple yet effective algorithm for regularizing neural netw...
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08/25/2017
Structured Low-Rank Matrix Factorization: Global Optimality, Algorithms, and Applications
Recently, convex formulations of low-rank matrix factorization problems ...
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06/24/2015