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Found 21 papers, 4 papers with code
Self-Supervised Learning for Covariance Estimation
We consider the use of deep learning for covariance estimation.
Probabilistic Simplex Component Analysis by Importance Sampling
In this paper we consider the problem of linear unmixing hidden random variables defined over the simplex with additive Gaussian noise, also known as probabilistic simplex component analysis (PRISM).
CFARnet: deep learning for target detection with constant false alarm rate
We consider the problem of target detection with a constant false alarm rate (CFAR).
Learning to Detect with Constant False Alarm Rate
We consider the use of machine learning for hypothesis testing with an emphasis on target detection.
On the Optimization Landscape of Maximum Mean Discrepancy
Our analysis shows that that the MMD optimization landscape is benign in these cases, and therefore gradient based methods will globally minimize the MMD objective.
Learning to Estimate Without Bias
In such applications, we show that BCE leads to asymptotically consistent estimators.
Conditional Frechet Inception Distance
We consider distance functions between conditional distributions.
Maximin Optimization for Binary Regression
Interestingly, we prove that this approach is optimal in linear regression with low noise conditions as well as robust regression with a small number of outliers.
Convex Nonparanormal Regression
Quantifying uncertainty in predictions or, more generally, estimating the posterior conditional distribution, is a core challenge in machine learning and statistics.
PnP-Net: A hybrid Perspective-n-Point Network
We consider the robust Perspective-n-Point (PnP) problem using a hybrid approach that combines deep learning with model based algorithms.
Fair Principal Component Analysis and Filter Design
We analyze the landscape of the underlying optimization in the case of orthogonal targets.
Globally Optimal Learning for Structured Elliptical Losses
The core of the difficulty is the non-convexity of the objective function, implying that standard optimization algorithms may converge to sub-optimal critical points.
Spectral Algorithm for Low-rank Multitask Regression
In this scenario sharing a low-rank component between the tasks translates to a shared spectral reflection of the water, which is a true underlying physical model.
ML for Flood Forecasting at Scale
We propose to build on these strengths and develop ML systems for timely and accurate riverine flood prediction.
Towards Global Remote Discharge Estimation: Using the Few to Estimate The Many
We demonstrate the efficacy of our approach for the problem of discharge estimation using simulations.
Learning to Detect
In this paper we consider Multiple-Input-Multiple-Output (MIMO) detection using deep neural networks.
Deep MIMO Detection
In this paper, we consider the use of deep neural networks in the context of Multiple-Input-Multiple-Output (MIMO) detection.
Joint Inverse Covariances Estimation with Mutual Linear Structure
We consider the problem of joint estimation of structured inverse covariance matrices.
Tyler's Covariance Matrix Estimator in Elliptical Models with Convex Structure
We address structured covariance estimation in elliptical distributions by assuming that the covariance is a priori known to belong to a given convex set, e. g., the set of Toeplitz or banded matrices.
Group Symmetry and non-Gaussian Covariance Estimation
We consider robust covariance estimation with group symmetry constraints.
Marginal Likelihoods for Distributed Parameter Estimation of Gaussian Graphical Models
In this paper, we propose a general framework for distributed estimation based on a maximum marginal likelihood (MML) approach.
