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06/20/2023
Individual Treatment Effects in Extreme Regimes
Understanding individual treatment effects in extreme regimes is importa...
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06/13/2023
Causal Mediation Analysis with Multi-dimensional and Indirectly Observed Mediators
Causal mediation analysis (CMA) is a powerful method to dissect the tota...
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06/12/2023
Robust Reinforcement Learning through Efficient Adversarial Herding
Although reinforcement learning (RL) is considered the gold standard for...
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06/08/2023
Robust Quickest Change Detection for Unnormalized Models
Detecting an abrupt and persistent change in the underlying distribution...
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06/05/2023
Deep Generalized Green's Functions
In this study, we address the challenge of obtaining a Green's function ...
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06/01/2023
Inference and Sampling of Point Processes from Diffusion Excursions
Point processes often have a natural interpretation with respect to a co...
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05/19/2023
Few-Shot Continual Learning for Conditional Generative Adversarial Networks
In few-shot continual learning for generative models, a target mode must...
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04/11/2023
Neural Network Accelerated Process Design of Polycrystalline Microstructures
Computational experiments are exploited in finding a well-designed proce...
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03/14/2023
Subspace Perturbation Analysis for Data-Driven Radar Target Localization
Recent works exploring data-driven approaches to classical problems in a...
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02/11/2023
Pruning Deep Neural Networks from a Sparsity Perspective
In recent years, deep network pruning has attracted significant attentio...
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02/08/2023
PASTA: Pessimistic Assortment Optimization
We consider a class of assortment optimization problems in an offline da...
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02/03/2023
Domain Adaptation via Rebalanced Sub-domain Alignment
Unsupervised domain adaptation (UDA) is a technique used to transfer kno...
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02/01/2023
Quickest Change Detection for Unnormalized Statistical Models
Classical quickest change detection algorithms require modeling pre-chan...
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12/17/2022
Personalized Federated Recommender Systems with Private and Partially Federated AutoEncoders
Recommender Systems (RSs) have become increasingly important in many app...
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10/01/2022
Causal Knowledge Transfer from Task Affinity
Recent developments in deep representation models through counterfactual...
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09/07/2022
Toward Data-Driven Radar STAP
Catalyzed by the recent emergence of site-specific, high-fidelity radio ...
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06/11/2022
A Theoretical Understanding of Neural Network Compression from Sparse Linear Approximation
The goal of model compression is to reduce the size of a large neural ne...
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05/27/2022
Inference and Sampling for Archimax Copulas
Understanding multivariate dependencies in both the bulk and the tails o...
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01/26/2022
On The Energy Statistics of Feature Maps in Pruning of Neural Networks with Skip-Connections
We propose a new structured pruning framework for compressing Deep Neura...
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01/26/2022
Toward Data-Driven STAP Radar
Using an amalgamation of techniques from classical radar, computer visio...
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01/22/2022
Multi-Agent Adversarial Attacks for Multi-Channel Communications
Recently Reinforcement Learning (RL) has been applied as an anti-adversa...
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01/10/2022
A Physics-Informed Vector Quantized Autoencoder for Data Compression of Turbulent Flow
Analyzing large-scale data from simulations of turbulent flows is memory...
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12/07/2021
Emulating Spatio-Temporal Realizations of Three-Dimensional Isotropic Turbulence via Deep Sequence Learning Models
We use a data-driven approach to model a three-dimensional turbulent flo...
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11/26/2021
Blaschke Product Neural Networks (BPNN): A Physics-Infused Neural Network for Phase Retrieval of Meromorphic Functions
Numerous physical systems are described by ordinary or partial different...
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11/25/2021
Characteristic Neural Ordinary Differential Equations
We propose Characteristic Neural Ordinary Differential Equations (C-NODE...
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10/26/2021
Privacy-Preserving Multi-Target Multi-Domain Recommender Systems with Assisted AutoEncoders
A long-standing challenge in Recommender Systems (RCs) is the data spars...
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10/05/2021
Task Affinity with Maximum Bipartite Matching in Few-Shot Learning
We propose an asymmetric affinity score for representing the complexity ...
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06/03/2021
Semi-Empirical Objective Functions for MCMC Proposal Optimization
We introduce and demonstrate a semi-empirical procedure for determining ...
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06/02/2021
SemiFL: Communication Efficient Semi-Supervised Federated Learning with Unlabeled Clients
Federated Learning allows training machine learning models by using the ...
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06/02/2021
Gradient Assisted Learning
In distributed settings, collaborations between different entities, such...
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05/31/2021
Towards Explainable Convolutional Features for Music Audio Modeling
Audio signals are often represented as spectrograms and treated as 2D im...
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03/23/2021
Neural Architecture Search From Fréchet Task Distance
We formulate a Fréchet-type asymmetric distance between tasks based on F...
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03/03/2021
Talaria: A Framework for Simulation of Permissioned Blockchains for Logistics and Beyond
In this paper, we present Talaria, a novel permissioned blockchain simul...
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02/27/2021
Neural Architecture Search From Task Similarity Measure
In this paper, we propose a neural architecture search framework based o...
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02/22/2021
Generative Archimedean Copulas
We propose a new generative modeling technique for learning multidimensi...
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02/17/2021
Deep Extreme Value Copulas for Estimation and Sampling
We propose a new method for modeling the distribution function of high d...
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12/24/2020
On Statistical Efficiency in Learning
A central issue of many statistical learning problems is to select an ap...
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10/27/2020
Task-Aware Neural Architecture Search
The design of handcrafted neural networks requires a lot of time and res...
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10/03/2020
HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients
Federated Learning (FL) is a method of training machine learning models ...
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07/13/2020
An Interpretable Baseline for Time Series Classification Without Intensive Learning
Recent advances in time series classification have largely focused on me...
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07/13/2020
Deep Cross-Subject Mapping of Neural Activity
In this paper, we demonstrate that a neural decoder trained on neural ac...
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07/13/2020
Projected Latent Markov Chain Monte Carlo: Conditional Inference with Normalizing Flows
We introduce Projected Latent Markov Chain Monte Carlo (PL-MCMC), a tech...
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07/12/2020
Fisher Auto-Encoders
It has been conjectured that the Fisher divergence is more robust to mod...
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07/12/2020
Learning latent stochastic differential equations with variational auto-encoders
We present a method for learning latent stochastic differential equation...
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05/15/2020
Model Linkage Selection for Cooperative Learning
Rapid developments in data collecting devices and computation platforms ...
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02/26/2020
Proximal Gradient Algorithm with Momentum and Flexible Parameter Restart for Nonconvex Optimization
Various types of parameter restart schemes have been proposed for accele...
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02/07/2020
Multimodal Controller for Generative Models
Class-conditional generative models are crucial tools for data generatio...
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01/02/2020
Robust Marine Buoy Placement for Ship Detection Using Dropout K-Means
Marine buoys aid in the battle against Illegal, Unreported and Unregulat...
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11/12/2019
A Distributed Online Convex Optimization Algorithm with Improved Dynamic Regret
In this paper, we consider the problem of distributed online convex opti...
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11/08/2019
