Search Results for author:
Found 29 papers, 8 papers with code
A Joint Data Compression and Time-Delay Estimation Method For Distributed Systems via Extremum Encoding
Motivated by the proliferation of mobile devices, we consider a basic form of the ubiquitous problem of time-delay estimation (TDE), but with communication constraints between two non co-located sensors.
Improved Evidential Deep Learning via a Mixture of Dirichlet Distributions
This paper explores a modern predictive uncertainty estimation approach, called evidential deep learning (EDL), in which a single neural network model is trained to learn a meta distribution over the predictive distribution by minimizing a specific objective function.
Operator SVD with Neural Networks via Nested Low-Rank Approximation
Computing eigenvalue decomposition (EVD) of a given linear operator, or finding its leading eigenvalues and eigenfunctions, is a fundamental task in many machine learning and scientific computing problems.
On Computationally Efficient Learning of Exponential Family Distributions
While the traditional maximum likelihood estimator for this class of exponential family is consistent, asymptotically normal, and asymptotically efficient, evaluating it is computationally hard.
Score-based Source Separation with Applications to Digital Communication Signals
We propose a new method for separating superimposed sources using diffusion-based generative models.
Gibbs-Based Information Criteria and the Over-Parameterized Regime
Double-descent refers to the unexpected drop in test loss of a learning algorithm beyond an interpolating threshold with over-parameterization, which is not predicted by information criteria in their classical forms due to the limitations in the standard asymptotic approach.
Online Segmented Recursive Least-Squares for Multipath Doppler Tracking
Underwater communication signals typically suffer from distortion due to motion-induced Doppler.
Towards Robust Data-Driven Underwater Acoustic Localization: A Deep CNN Solution with Performance Guarantees for Model Mismatch
Key challenges in developing underwater acoustic localization methods are related to the combined effects of high reverberation in intricate environments.
A Bilateral Bound on the Mean-Square Error for Estimation in Model Mismatch
A bilateral (i. e., upper and lower) bound on the mean-square error under a general model mismatch is developed.
On Neural Architectures for Deep Learning-based Source Separation of Co-Channel OFDM Signals
We study the single-channel source separation problem involving orthogonal frequency-division multiplexing (OFDM) signals, which are ubiquitous in many modern-day digital communication systems.
Group Fairness with Uncertainty in Sensitive Attributes
To overcome this limitation, we propose a bootstrap-based algorithm that achieves the target level of fairness despite the uncertainty in sensitive attributes.
On counterfactual inference with unobserved confounding
Given an observational study with $n$ independent but heterogeneous units, our goal is to learn the counterfactual distribution for each unit using only one $p$-dimensional sample per unit containing covariates, interventions, and outcomes.
Can Shadows Reveal Biometric Information?
We transfer what we learn from the synthetic data to the real data using domain adaptation in a completely unsupervised way.
Data-Driven Blind Synchronization and Interference Rejection for Digital Communication Signals
We study the potential of data-driven deep learning methods for separation of two communication signals from an observation of their mixture.
Exploiting Temporal Structures of Cyclostationary Signals for Data-Driven Single-Channel Source Separation
We study the problem of single-channel source separation (SCSS), and focus on cyclostationary signals, which are particularly suitable in a variety of application domains.
Direct Localization in Underwater Acoustics via Convolutional Neural Networks: A Data-Driven Approach
Direct localization (DLOC) methods, which use the observed data to localize a source at an unknown position in a one-step procedure, generally outperform their indirect two-step counterparts (e. g., using time-difference of arrivals).
A Maximal Correlation Framework for Fair Machine Learning
As machine learning algorithms grow in popularity and diversify to many industries, ethical and legal concerns regarding their fairness have become increasingly relevant.
A Computationally Efficient Method for Learning Exponential Family Distributions
In this work, we propose a computationally efficient estimator that is consistent as well as asymptotically normal under mild conditions.
Selective Regression Under Fairness Criteria
Selective regression allows abstention from prediction if the confidence to make an accurate prediction is not sufficient.
A Semi-Blind Method for Localization of Underwater Acoustic Sources
We also derive the Cram\'er-Rao bound for this model, which can be used to guide the placement of collections of receivers so as to optimize localization accuracy.
What You Can Learn by Staring at a Blank Wall
We present a passive non-line-of-sight method that infers the number of people or activity of a person from the observation of a blank wall in an unknown room.
Blind Modulo Analog-to-Digital Conversion
In a growing number of applications, there is a need to digitize signals whose spectral characteristics are challenging for traditional Analog-to-Digital Converters (ADCs).
One-Bit Direct Position Determination of Narrowband Gaussian Signals
One of the main drawbacks of the well-known Direct Position Determination (DPD) method is the requirement that raw signal data be transferred to a common processor.
On Learning Continuous Pairwise Markov Random Fields
We consider learning a sparse pairwise Markov Random Field (MRF) with continuous-valued variables from i. i. d samples.
Computational Mirrors: Blind Inverse Light Transport by Deep Matrix Factorization
We recover a video of the motion taking place in a hidden scene by observing changes in indirect illumination in a nearby uncalibrated visible region.
On Universal Features for High-Dimensional Learning and Inference
We consider the problem of identifying universal low-dimensional features from high-dimensional data for inference tasks in settings involving learning.
An Information Theoretic Interpretation to Deep Neural Networks
It is commonly believed that the hidden layers of deep neural networks (DNNs) attempt to extract informative features for learning tasks.
Inferring Light Fields From Shadows
We present a method for inferring a 4D light field of a hidden scene from 2D shadows cast by a known occluder on a diffuse wall.
Turning Corners Into Cameras: Principles and Methods
We show that walls and other obstructions with edges can be exploited as naturally-occurring "cameras" that reveal the hidden scenes beyond them.
