Anit Kumar Sahu | DeepAI

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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 ...

2 Amrit Singh Bedi, et al. ∙

∙ 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...

0 Dusan Jakovetic, et al. ∙

∙ 01/11/2022

Partial Model Averaging in Federated Learning: Performance Guarantees and Benefits

Local Stochastic Gradient Descent (SGD) with periodic model averaging (F...

0 Sunwoo Lee, et al. ∙

∙ 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...

0 Devin Willmott, et al. ∙

∙ 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...

0 Anit Kumar Sahu, et al. ∙

∙ 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 ...

0 Satya Narayan Shukla, et al. ∙

∙ 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...

0 Zhanhong Jiang, et al. ∙

∙ 01/07/2020

FedDANE: A Federated Newton-Type Method

Federated learning aims to jointly learn statistical models over massive...

0 Tian Li, et al. ∙

∙ 09/30/2019

Black-box Adversarial Attacks with Bayesian Optimization

We focus on the problem of black-box adversarial attacks, where the aim ...

0 Satya Narayan Shukla, et al. ∙

∙ 09/27/2019

Learning in Confusion: Batch Active Learning with Noisy Oracle

We study the problem of training machine learning models incrementally u...

0 Gaurav Gupta, et al. ∙

∙ 08/21/2019

Federated Learning: Challenges, Methods, and Future Directions

Federated learning involves training statistical models over remote devi...

9 Tian Li, et al. ∙

∙ 05/23/2019

MATCHA: Speeding Up Decentralized SGD via Matching Decomposition Sampling

The trade-off between convergence error and communication delays in dece...

0 Jianyu Wang, et al. ∙

∙ 03/18/2019

Distributed stochastic optimization with gradient tracking over strongly-connected networks

In this paper, we study distributed stochastic optimization to minimize ...

0 Ran Xin, et al. ∙

∙ 12/14/2018

On the Convergence of Federated Optimization in Heterogeneous Networks

The burgeoning field of federated learning involves training machine lea...

0 Anit Kumar Sahu, et al. ∙

∙ 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...

0 Anit Kumar Sahu, et al. ∙

∙ 10/08/2018

Towards Gradient Free and Projection Free Stochastic Optimization

This paper focuses on the problem of constrainedstochastic optimization....

0 Anit Kumar Sahu, et al. ∙

∙ 02/14/2018

CIRFE: A Distributed Random Fields Estimator

This paper presents a communication efficient distributed algorithm, CIR...

0 Anit Kumar Sahu, et al. ∙

∙ 01/12/2018

Communication Optimality Trade-offs For Distributed Estimation

This paper proposes Communication efficient REcursive Distributed estima...

0 Anit Kumar Sahu, et al. ∙

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