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06/20/2023
The Dyson Equalizer: Adaptive Noise Stabilization for Low-Rank Signal Detection and Recovery
Detecting and recovering a low-rank signal in a noisy data matrix is a f...
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03/16/2023
Multi-modal Differentiable Unsupervised Feature Selection
Multi-modal high throughput biological data presents a great scientific ...
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10/19/2022
Autoregressive Generative Modeling with Noise Conditional Maximum Likelihood Estimation
We introduce a simple modification to the standard maximum likelihood es...
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07/18/2022
ManiFeSt: Manifold-based Feature Selection for Small Data Sets
In this paper, we present a new method for few-sample supervised feature...
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06/22/2022
Neural Inverse Transform Sampler
Any explicit functional representation f of a density is hampered by two...
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10/11/2021
Deep Unsupervised Feature Selection by Discarding Nuisance and Correlated Features
Modern datasets often contain large subsets of correlated features and n...
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10/01/2021
Probabilistic Robust Autoencoders for Anomaly Detection
Empirical observations often consist of anomalies (or outliers) that con...
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06/11/2021
Locally Sparse Networks for Interpretable Predictions
Despite the enormous success of neural networks, they are still hard to ...
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04/25/2021
On the Efficient Evaluation of the Azimuthal Fourier Components of the Green's Function for Helmholtz's Equation in Cylindrical Coordinates
In this manuscript, we develop an efficient algorithm to evaluate the az...
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03/25/2021
Biwhitening Reveals the Rank of a Count Matrix
Estimating the rank of a corrupted data matrix is an important task in d...
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02/26/2021
Spectral Top-Down Recovery of Latent Tree Models
Modeling the distribution of high dimensional data by a latent tree grap...
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11/06/2020
Local Two-Sample Testing over Graphs and Point-Clouds by Random-Walk Distributions
Two-sample testing is a fundamental tool for scientific discovery. Yet, ...
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10/12/2020
Deep Gated Canonical Correlation Analysis
Canonical Correlation Analysis (CCA) models can extract informative corr...
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07/09/2020
Let the Data Choose its Features: Differentiable Unsupervised Feature Selection
Scientific observations often consist of a large number of variables (fe...
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05/31/2020
Doubly-Stochastic Normalization of the Gaussian Kernel is Robust to Heteroskedastic Noise
A fundamental step in many data-analysis techniques is the construction ...
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02/28/2020
Spectral neighbor joining for reconstruction of latent tree models
A key assumption in multiple scientific applications is that the distrib...
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02/27/2020
The Spectral Underpinning of word2vec
word2vec due to Mikolov et al. (2013) is a word embedding method that is...
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02/15/2019
Heavy-tailed kernels reveal a finer cluster structure in t-SNE visualisations
T-distributed stochastic neighbour embedding (t-SNE) is a widely used da...
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10/09/2018
Deep supervised feature selection using Stochastic Gates
In this study, we propose a novel non-parametric embedded feature select...
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03/28/2018
Defending against Adversarial Images using Basis Functions Transformations
We study the effectiveness of various approaches that defend against adv...
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02/27/2018
Learning Binary Latent Variable Models: A Tensor Eigenpair Approach
Latent variable models with hidden binary units appear in various applic...
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01/04/2018
SpectralNet: Spectral Clustering using Deep Neural Networks
Spectral clustering is a leading and popular technique in unsupervised d...
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12/25/2017
Efficient Algorithms for t-distributed Stochastic Neighborhood Embedding
t-distributed Stochastic Neighborhood Embedding (t-SNE) is a method for ...
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11/13/2017
Randomized Near Neighbor Graphs, Giant Components, and Applications in Data Science
If we pick n random points uniformly in [0,1]^d and connect each point t...
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08/18/2017
Data-Driven Tree Transforms and Metrics
We consider the analysis of high dimensional data given in the form of a...
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08/13/2017
Mahalanonbis Distance Informed by Clustering
A fundamental question in data analysis, machine learning and signal pro...
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03/08/2017
Unsupervised Ensemble Regression
Consider a regression problem where there is no labeled data and the onl...
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10/13/2016
Removal of Batch Effects using Distribution-Matching Residual Networks
Sources of variability in experimentally derived data include measuremen...
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06/02/2016
DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network
Medical practitioners use survival models to explore and understand the ...
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02/06/2016
A Deep Learning Approach to Unsupervised Ensemble Learning
We show how deep learning methods can be applied in the context of crowd...
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10/20/2015
Unsupervised Ensemble Learning with Dependent Classifiers
In unsupervised ensemble learning, one obtains predictions from multiple...
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07/29/2014
Estimating the Accuracies of Multiple Classifiers Without Labeled Data
In various situations one is given only the predictions of multiple clas...
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03/13/2013