Anit Kumar Sahu

Orcid: 0000-0002-4083-0418

According to our database1, Anit Kumar Sahu authored at least 53 papers between 2012 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

On csauthors.net:

Bibliography

2023
Partial model averaging in Federated Learning: Performance guarantees and benefits.
Neurocomputing, November, 2023

Distributed Recursive Estimation under Heavy-Tail Communication Noise.
SIAM J. Control. Optim., June, 2023

Large Deviations for Products of Non-Identically Distributed Network Matrices With Applications to Communication-Efficient Distributed Learning and Inference.
IEEE Trans. Signal Process., 2023

Nonlinear Gradient Mappings and Stochastic Optimization: A General Framework with Applications to Heavy-Tail Noise.
SIAM J. Optim., 2023

RealFM: A Realistic Mechanism to Incentivize Data Contribution and Device Participation.
CoRR, 2023

Federated Representation Learning for Automatic Speech Recognition.
CoRR, 2023

Learning When to Trust Which Teacher for Weakly Supervised ASR.
CoRR, 2023

Performance Scaling via Optimal Transport: Enabling Data Selection from Partially Revealed Sources.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Federated Self-Learning with Weak Supervision for Speech Recognition.
Proceedings of the IEEE International Conference on Acoustics, 2023

2022
Matcha: A Matching-Based Link Scheduling Strategy to Speed up Distributed Optimization.
IEEE Trans. Signal Process., 2022

FedBC: Calibrating Global and Local Models via Federated Learning Beyond Consensus.
CoRR, 2022

Self-Aware Personalized Federated Learning.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

ILASR: Privacy-Preserving Incremental Learning for Automatic Speech Recognition at Production Scale.
Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

Federated Learning Challenges and Opportunities: An Outlook.
Proceedings of the IEEE International Conference on Acoustics, 2022

2021
You Only Query Once: Effective Black Box Adversarial Attacks with Minimal Repeated Queries.
CoRR, 2021

Simple and Efficient Hard Label Black-box Adversarial Attacks in Low Query Budget Regimes.
Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

Multiplicative Filter Networks.
Proceedings of the 9th International Conference on Learning Representations, 2021

2020
Federated Learning: Challenges, Methods, and Future Directions.
IEEE Signal Process. Mag., 2020

Decentralized Zeroth-Order Constrained Stochastic Optimization Algorithms: Frank-Wolfe and Variants With Applications to Black-Box Adversarial Attacks.
Proc. IEEE, 2020

Gaussian MRF Covariance Modeling for Efficient Black-Box Adversarial Attacks.
CoRR, 2020

Hard Label Black-box Adversarial Attacks in Low Query Budget Regimes.
CoRR, 2020

Federated Optimization in Heterogeneous Networks.
Proceedings of Machine Learning and Systems 2020, 2020

Exploring the Error-Runtime Trade-off in Decentralized Optimization.
Proceedings of the 54th Asilomar Conference on Signals, Systems, and Computers, 2020

2019
Black-box Adversarial Attacks with Bayesian Optimization.
CoRR, 2019

Learning in Confusion: Batch Active Learning with Noisy Oracle.
CoRR, 2019

MATCHA: Speeding Up Decentralized SGD via Matching Decomposition Sampling.
CoRR, 2019

Communication Efficient Distributed Estimation Over Directed Random Graphs.
Proceedings of the IEEE EUROCON 2019, 2019

Distributed stochastic optimization with gradient tracking over strongly-connected networks.
Proceedings of the 58th IEEE Conference on Decision and Control, 2019

Towards Gradient Free and Projection Free Stochastic Optimization.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

Distributed empirical risk minimization over directed graphs.
Proceedings of the 53rd Asilomar Conference on Signals, Systems, and Computers, 2019

FedDANE: A Federated Newton-Type Method.
Proceedings of the 53rd Asilomar Conference on Signals, Systems, and Computers, 2019

2018
CIRFE: A Distributed Random Fields Estimator.
IEEE Trans. Signal Process., 2018

Communication efficient distributed weighted non-linear least squares estimation.
EURASIP J. Adv. Signal Process., 2018

On the Convergence of Federated Optimization in Heterogeneous Networks.
CoRR, 2018

Managing App Install Ad Campaigns in RTB: A Q-Learning Approach.
CoRR, 2018

CREDO: A Communication-Efficient Distributed Estimation Algorithm.
Proceedings of the 2018 IEEE International Symposium on Information Theory, 2018

Large Deviations for Products of Non-I.i.d. Stochastic Matrices with Application to Distributed Detection.
Proceedings of the 2018 IEEE International Symposium on Information Theory, 2018

Non-Asymptotic Rates for Communication Efficient Distributed Zeroth Order Strongly Convex Optimization.
Proceedings of the 2018 IEEE Global Conference on Signal and Information Processing, 2018

Distributed Zeroth Order Optimization Over Random Networks: A Kiefer-Wolfowitz Stochastic Approximation Approach.
Proceedings of the 57th IEEE Conference on Decision and Control, 2018

Convergence Rates for Distributed Stochastic Optimization Over Random Networks.
Proceedings of the 57th IEEE Conference on Decision and Control, 2018

Data-driven Thermal Model Inference with ARMAX, in Smart Environments, based on Normalized Mutual Information.
Proceedings of the 2018 Annual American Control Conference, 2018

2017
Recursive Distributed Detection for Composite Hypothesis Testing: Nonlinear Observation Models in Additive Gaussian Noise.
IEEE Trans. Inf. Theory, 2017

Dist-Hedge: A partial information setting based distributed non-stochastic sequence prediction algorithm.
Proceedings of the 2017 IEEE Global Conference on Signal and Information Processing, 2017

2016
Distributed Sequential Detection for Gaussian Shift-in-Mean Hypothesis Testing.
IEEE Trans. Signal Process., 2016

Distributed Constrained Recursive Nonlinear Least-Squares Estimation: Algorithms and Asymptotics.
IEEE Trans. Signal Inf. Process. over Networks, 2016

Recursive Distributed Detection for Composite Hypothesis Testing: Algorithms and Asymptotics.
CoRR, 2016

Distributed recursive composite hypothesis testing: Imperfect communication.
Proceedings of the IEEE International Symposium on Information Theory, 2016

Distributed generalized likelihood ratio tests: Fundamental limits and tradeoffs.
Proceedings of the 2016 IEEE International Conference on Acoustics, 2016

Distributed sequence prediction: A consensus+innovations approach.
Proceedings of the 2016 IEEE Global Conference on Signal and Information Processing, 2016

Queue-based broadcast gossip algorithm for consensus.
Proceedings of the 54th Annual Allerton Conference on Communication, 2016

2014
Distributed Sequential Detection for Gaussian Binary Hypothesis Testing.
CoRR, 2014

Distributed sequential detection for Gaussian binary hypothesis testing: Heterogeneous networks.
Proceedings of the 48th Asilomar Conference on Signals, Systems and Computers, 2014

2012
Fast and Accurate Frequency Estimation Using Sliding DFT
CoRR, 2012


  Loading...