b'Vahid Tarokh'

research

∙ 06/20/2023

Individual Treatment Effects in Extreme Regimes

Understanding individual treatment effects in extreme regimes is importa...

0 Ahmed Aloui, et al. ∙

research

∙ 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...

0 Ziyang Jiang, et al. ∙

research

∙ 06/12/2023

Robust Reinforcement Learning through Efficient Adversarial Herding

Although reinforcement learning (RL) is considered the gold standard for...

0 Juncheng Dong, et al. ∙

research

∙ 06/08/2023

Robust Quickest Change Detection for Unnormalized Models

Detecting an abrupt and persistent change in the underlying distribution...

0 Suya Wu, et al. ∙

research

∙ 06/05/2023

Deep Generalized Green's Functions

In this study, we address the challenge of obtaining a Green's function ...

0 Rixi Peng, et al. ∙

research

∙ 06/01/2023

Inference and Sampling of Point Processes from Diffusion Excursions

Point processes often have a natural interpretation with respect to a co...

0 Ali Hasan, et al. ∙

research

∙ 05/19/2023

Few-Shot Continual Learning for Conditional Generative Adversarial Networks

In few-shot continual learning for generative models, a target mode must...

0 Cat P. Le, et al. ∙

research

∙ 04/11/2023

Neural Network Accelerated Process Design of Polycrystalline Microstructures

Computational experiments are exploited in finding a well-designed proce...

0 Junrong Lin, et al. ∙

research

∙ 03/14/2023

Subspace Perturbation Analysis for Data-Driven Radar Target Localization

Recent works exploring data-driven approaches to classical problems in a...

0 Shyam Venkatasubramanian, et al. ∙

research

∙ 02/11/2023

Pruning Deep Neural Networks from a Sparsity Perspective

In recent years, deep network pruning has attracted significant attentio...

0 Enmao Diao, et al. ∙

research

∙ 02/08/2023

PASTA: Pessimistic Assortment Optimization

We consider a class of assortment optimization problems in an offline da...

0 Juncheng Dong, et al. ∙

research

∙ 02/03/2023

Domain Adaptation via Rebalanced Sub-domain Alignment

Unsupervised domain adaptation (UDA) is a technique used to transfer kno...

0 Yiling Liu, et al. ∙

research

∙ 02/01/2023

Quickest Change Detection for Unnormalized Statistical Models

Classical quickest change detection algorithms require modeling pre-chan...

0 Suya Wu, et al. ∙

research

∙ 12/17/2022

Personalized Federated Recommender Systems with Private and Partially Federated AutoEncoders

Recommender Systems (RSs) have become increasingly important in many app...

0 Qi Le, et al. ∙

research

∙ 10/01/2022

Causal Knowledge Transfer from Task Affinity

Recent developments in deep representation models through counterfactual...

0 Ahmed Aloui, et al. ∙

research

∙ 09/07/2022

Toward Data-Driven Radar STAP

Catalyzed by the recent emergence of site-specific, high-fidelity radio ...

0 Shyam Venkatasubramanian, et al. ∙

research

∙ 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...

0 Wenjing Yang, et al. ∙

research

∙ 05/27/2022

Inference and Sampling for Archimax Copulas

Understanding multivariate dependencies in both the bulk and the tails o...

0 Yuting Ng, et al. ∙

research

∙ 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...

0 Mohammadreza Soltani, et al. ∙

research

∙ 01/26/2022

Toward Data-Driven STAP Radar

Using an amalgamation of techniques from classical radar, computer visio...

0 Shyam Venkatasubramanian, et al. ∙

research

∙ 01/22/2022

Multi-Agent Adversarial Attacks for Multi-Channel Communications

Recently Reinforcement Learning (RL) has been applied as an anti-adversa...

0 Juncheng Dong, et al. ∙

research

∙ 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...

25 Mohammadreza Momenifar, et al. ∙

research

∙ 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...

0 Mohammadreza Momenifar, et al. ∙

research

∙ 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...

0 Juncheng Dong, et al. ∙

research

∙ 11/25/2021

Characteristic Neural Ordinary Differential Equations

We propose Characteristic Neural Ordinary Differential Equations (C-NODE...

6 Xingzi Xu, et al. ∙

research

∙ 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...

0 Enmao Diao, et al. ∙

research

∙ 10/05/2021

Task Affinity with Maximum Bipartite Matching in Few-Shot Learning

We propose an asymmetric affinity score for representing the complexity ...

0 Cat P. Le, et al. ∙

research

∙ 06/03/2021

Semi-Empirical Objective Functions for MCMC Proposal Optimization

We introduce and demonstrate a semi-empirical procedure for determining ...

0 Chris Cannella, et al. ∙

research

∙ 06/02/2021

SemiFL: Communication Efficient Semi-Supervised Federated Learning with Unlabeled Clients

Federated Learning allows training machine learning models by using the ...

0 Enmao Diao, et al. ∙

research

∙ 06/02/2021

Gradient Assisted Learning

In distributed settings, collaborations between different entities, such...

0 Enmao Diao, et al. ∙

research

∙ 05/31/2021

Towards Explainable Convolutional Features for Music Audio Modeling

Audio signals are often represented as spectrograms and treated as 2D im...

32 Anna K. Yanchenko, et al. ∙

research

∙ 03/23/2021

Neural Architecture Search From Fréchet Task Distance

We formulate a Fréchet-type asymmetric distance between tasks based on F...

0 Cat P. Le, et al. ∙

research

∙ 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...

0 Jiali Xing, et al. ∙

research

∙ 02/27/2021

Neural Architecture Search From Task Similarity Measure

In this paper, we propose a neural architecture search framework based o...

0 Cat P. Le, et al. ∙

research

∙ 02/22/2021

Generative Archimedean Copulas

We propose a new generative modeling technique for learning multidimensi...

7 Yuting Ng, et al. ∙

research

∙ 02/17/2021

Deep Extreme Value Copulas for Estimation and Sampling

We propose a new method for modeling the distribution function of high d...

14 Ali Hasan, et al. ∙

research

∙ 12/24/2020

On Statistical Efficiency in Learning

A central issue of many statistical learning problems is to select an ap...

0 Jie Ding, et al. ∙

research

∙ 10/27/2020

Task-Aware Neural Architecture Search

The design of handcrafted neural networks requires a lot of time and res...

0 Cat P. Le, et al. ∙

research

∙ 10/03/2020

HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients

Federated Learning (FL) is a method of training machine learning models ...

0 Enmao Diao, et al. ∙

research

∙ 07/13/2020

An Interpretable Baseline for Time Series Classification Without Intensive Learning

Recent advances in time series classification have largely focused on me...

0 Robert J. Ravier, et al. ∙

research

∙ 07/13/2020

Deep Cross-Subject Mapping of Neural Activity

In this paper, we demonstrate that a neural decoder trained on neural ac...

0 Marko Angjelichinoski, et al. ∙

research

∙ 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...

0 Chris Cannella, et al. ∙

research

∙ 07/12/2020

Fisher Auto-Encoders

It has been conjectured that the Fisher divergence is more robust to mod...

43 Khalil Elkhalil, et al. ∙

research

∙ 07/12/2020

Learning latent stochastic differential equations with variational auto-encoders

We present a method for learning latent stochastic differential equation...

0 Ali Hasan, et al. ∙

research

∙ 05/15/2020

Model Linkage Selection for Cooperative Learning

Rapid developments in data collecting devices and computation platforms ...

0 Jiaying Zhou, et al. ∙

research

∙ 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...

0 Yi Zhou, et al. ∙

research

∙ 02/07/2020

Multimodal Controller for Generative Models

Class-conditional generative models are crucial tools for data generatio...

0 Enmao Diao, et al. ∙

research

∙ 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...

0 Yuting Ng, et al. ∙

research

∙ 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...

0 Yan Zhang, et al. ∙

research

∙ 11/08/2019

Cross-subject Decoding of Eye Movement Goals from Local Field Potentials

Objective. We consider the cross-subject decoding problem from local fie...

15 Marko Angjelichinoski, et al. ∙

Vahid Tarokh

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