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09/12/2023
On Computationally Efficient Learning of Exponential Family Distributions
We consider the classical problem of learning, with arbitrary accuracy, ...
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06/26/2023
Score-based Source Separation with Applications to Digital Communication Signals
We propose a new method for separating superimposed sources using diffus...
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06/08/2023
SGLD-Based Information Criteria and the Over-Parameterized Regime
Double-descent refers to the unexpected drop in test loss of a learning ...
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05/14/2023
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...
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03/11/2023
On Neural Architectures for Deep Learning-based Source Separation of Co-Channel OFDM Signals
We study the single-channel source separation problem involving orthogon...
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02/16/2023
Group Fairness with Uncertainty in Sensitive Attributes
We consider learning a fair predictive model when sensitive attributes a...
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11/14/2022
On counterfactual inference with unobserved confounding
Given an observational study with n independent but heterogeneous units ...
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10/18/2022
Information-theoretic Characterizations of Generalization Error for the Gibbs Algorithm
Various approaches have been developed to upper bound the generalization...
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09/21/2022
Can Shadows Reveal Biometric Information?
We study the problem of extracting biometric information of individuals ...
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09/11/2022
Data-Driven Blind Synchronization and Interference Rejection for Digital Communication Signals
We study the potential of data-driven deep learning methods for separati...
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08/22/2022
Exploiting Temporal Structures of Cyclostationary Signals for Data-Driven Single-Channel Source Separation
We study the problem of single-channel source separation (SCSS), and foc...
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07/20/2022
Direct Localization in Underwater Acoustics via Convolutional Neural Networks: A Data-Driven Approach
Direct localization (DLOC) methods, which use the observed data to local...
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10/28/2021
Selective Regression Under Fairness Criteria
Selective regression allows abstention from prediction if the confidence...
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10/28/2021
A Computationally Efficient Method for Learning Exponential Family Distributions
We consider the question of learning the natural parameters of a k param...
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10/12/2021
Blind Modulo Analog-to-Digital Conversion of Vector Processes
In a growing number of applications, there is a need to digitize a (poss...
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08/30/2021
What You Can Learn by Staring at a Blank Wall
We present a passive non-line-of-sight method that infers the number of ...
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10/28/2020
On Learning Continuous Pairwise Markov Random Fields
We consider learning a sparse pairwise Markov Random Field (MRF) with co...
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12/05/2019
Computational Mirrors: Blind Inverse Light Transport by Deep Matrix Factorization
We recover a video of the motion taking place in a hidden scene by obser...
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11/20/2019
On Universal Features for High-Dimensional Learning and Inference
We consider the problem of identifying universal low-dimensional feature...
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11/19/2019
Super-Nyquist Rateless Coding for Intersymbol Interference Channels
A rateless transmission architecture is developed for communication over...
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05/16/2019
An Information Theoretic Interpretation to Deep Neural Networks
It is commonly believed that the hidden layers of deep neural networks (...
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10/14/2018
Bregman Divergence Bounds and the Universality of the Logarithmic Loss
A loss function measures the discrepancy between the true values and the...
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06/23/2018
A Modulo-Based Architecture for Analog-to-Digital Conversion
Systems that capture and process analog signals must first acquire them ...
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05/10/2018
On the Universality of the Logistic Loss Function
A loss function measures the discrepancy between the true values (observ...
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02/10/2018