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06/22/2022
: Calibrating Global and Local Models via Federated Learning Beyond Consensus
In federated learning (FL), the objective of collaboratively learning a ...
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04/06/2022
Nonlinear gradient mappings and stochastic optimization: A general framework with applications to heavy-tail noise
We introduce a general framework for nonlinear stochastic gradient desce...
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01/11/2022
Partial Model Averaging in Federated Learning: Performance Guarantees and Benefits
Local Stochastic Gradient Descent (SGD) with periodic model averaging (F...
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01/29/2021
You Only Query Once: Effective Black Box Adversarial Attacks with Minimal Repeated Queries
Researchers have repeatedly shown that it is possible to craft adversari...
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10/08/2020
Gaussian MRF Covariance Modeling for Efficient Black-Box Adversarial Attacks
We study the problem of generating adversarial examples in a black-box s...
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07/13/2020
Hard Label Black-box Adversarial Attacks in Low Query Budget Regimes
We focus on the problem of black-box adversarial attacks, where the aim ...
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06/10/2020
Data-driven Thermal Model Inference with ARMAX, in Smart Environments, based on Normalized Mutual Information
Understanding the models that characterize the thermal dynamics in a sma...
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01/07/2020
FedDANE: A Federated Newton-Type Method
Federated learning aims to jointly learn statistical models over massive...
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09/30/2019
Black-box Adversarial Attacks with Bayesian Optimization
We focus on the problem of black-box adversarial attacks, where the aim ...
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09/27/2019
Learning in Confusion: Batch Active Learning with Noisy Oracle
We study the problem of training machine learning models incrementally u...
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08/21/2019
Federated Learning: Challenges, Methods, and Future Directions
Federated learning involves training statistical models over remote devi...
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05/23/2019
MATCHA: Speeding Up Decentralized SGD via Matching Decomposition Sampling
The trade-off between convergence error and communication delays in dece...
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03/18/2019
Distributed stochastic optimization with gradient tracking over strongly-connected networks
In this paper, we study distributed stochastic optimization to minimize ...
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12/14/2018
On the Convergence of Federated Optimization in Heterogeneous Networks
The burgeoning field of federated learning involves training machine lea...
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11/11/2018
Managing App Install Ad Campaigns in RTB: A Q-Learning Approach
Real time bidding (RTB) enables demand side platforms (bidders) to scale...
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10/08/2018
Towards Gradient Free and Projection Free Stochastic Optimization
This paper focuses on the problem of constrainedstochastic optimization....
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02/14/2018
CIRFE: A Distributed Random Fields Estimator
This paper presents a communication efficient distributed algorithm, CIR...
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01/12/2018
