Fast and Accurate Frequency Estimation Using Sliding DFT - Anit Kumar Sahu; Mrityunjoy Chakraborty (CoRR, 2012)

Distributed sequential detection for Gaussian binary hypothesis testing: Heterogeneous networks. - Anit Kumar Sahu; Soummya Kar (ACSSC, 2014)

Distributed Sequential Detection for Gaussian Binary Hypothesis Testing. - Anit Kumar Sahu; Soummya Kar (CoRR, 2014)

Queue-based broadcast gossip algorithm for consensus. - Soummya Kar; Rohit Negi; Majid Mahzoon; Anit Kumar Sahu (Allerton, 2016)

Distributed sequence prediction: A consensus+innovations approach. - Anit Kumar Sahu; Soummya Kar (GlobalSIP, 2016)

Distributed generalized likelihood ratio tests: Fundamental limits and tradeoffs. - Anit Kumar Sahu; Soummya Kar (ICASSP, 2016)

Distributed recursive composite hypothesis testing: Imperfect communication. - Anit Kumar Sahu; Soummya Kar (ISIT, 2016)

Recursive Distributed Detection for Composite Hypothesis Testing: Algorithms and Asymptotics. - Anit Kumar Sahu; Soummya Kar (CoRR, 2016)

Distributed Constrained Recursive Nonlinear Least-Squares Estimation: Algorithms and Asymptotics. - Anit Kumar Sahu; Soummya Kar; José M. F. Moura; H. Vincent Poor (IEEE Trans. Signal Inf. Process. over Networks, 2016)

Distributed Sequential Detection for Gaussian Shift-in-Mean Hypothesis Testing. - Anit Kumar Sahu; Soummya Kar (IEEE Trans. Signal Process., 2016)

Dist-Hedge: A partial information setting based distributed non-stochastic sequence prediction algorithm. - Anit Kumar Sahu; Soummya Kar (GlobalSIP, 2017)

Recursive Distributed Detection for Composite Hypothesis Testing: Nonlinear Observation Models in Additive Gaussian Noise. - Anit Kumar Sahu; Soummya Kar (IEEE Trans. Inf. Theory, 2017)

Data-driven Thermal Model Inference with ARMAX, in Smart Environments, based on Normalized Mutual Information. - Zhanhong Jiang; Jonathan Francis; Anit Kumar Sahu; Sirajum Munir; Charles Shelton; Anthony Rowe 001; Mario Berges (ACC, 2018)

Convergence Rates for Distributed Stochastic Optimization Over Random Networks. - Dusan Jakovetic; Dragana Bajovic; Anit Kumar Sahu; Soummya Kar (CDC, 2018)

Distributed Zeroth Order Optimization Over Random Networks: A Kiefer-Wolfowitz Stochastic Approximation Approach. - Anit Kumar Sahu; Dusan Jakovetic; Dragana Bajovic; Soummya Kar (CDC, 2018)

Non-Asymptotic Rates for Communication Efficient Distributed Zeroth Order Strongly Convex Optimization. - Anit Kumar Sahu; Dusan Jakovetic; Dragana Bajovic; Soummya Kar (GlobalSIP, 2018)

Large Deviations for Products of Non-I.i.d. Stochastic Matrices with Application to Distributed Detection. - Dragana Bajovic; Dusan Jakovetic; Anit Kumar Sahu; Soummya Kar (ISIT, 2018)

CREDO: A Communication-Efficient Distributed Estimation Algorithm. - Anit Kumar Sahu; Dusan Jakovetic; Soummya Kar (ISIT, 2018)

Managing App Install Ad Campaigns in RTB: A Q-Learning Approach. - Anit Kumar Sahu; Shaunak Mishra; Narayan Bhamidipati (CoRR, 2018)

On the Convergence of Federated Optimization in Heterogeneous Networks. - Anit Kumar Sahu; Tian Li 005; Maziar Sanjabi; Manzil Zaheer; Ameet Talwalkar; Virginia Smith (CoRR, 2018)

Communication efficient distributed weighted non-linear least squares estimation. - Anit Kumar Sahu; Dusan Jakovetic; Dragana Bajovic; Soummya Kar (EURASIP J. Adv. Signal Process., 2018)

CIRFE: A Distributed Random Fields Estimator. - Anit Kumar Sahu; Dusan Jakovetic; Soummya Kar (IEEE Trans. Signal Process., 2018)

FedDANE: A Federated Newton-Type Method. - Tian Li 005; Anit Kumar Sahu; Manzil Zaheer; Maziar Sanjabi; Ameet Talwalkar; Virginia Smith (ACSSC, 2019)

Distributed empirical risk minimization over directed graphs. - Ran Xin; Anit Kumar Sahu; Soummya Kar; Usman A. Khan (ACSSC, 2019)

Towards Gradient Free and Projection Free Stochastic Optimization. - Anit Kumar Sahu; Manzil Zaheer; Soummya Kar (AISTATS, 2019)

Distributed stochastic optimization with gradient tracking over strongly-connected networks. - Ran Xin; Anit Kumar Sahu; Usman A. Khan; Soummya Kar (CDC, 2019)

Communication Efficient Distributed Estimation Over Directed Random Graphs. - Anit Kumar Sahu; Dusan Jakovetic; Dragana Bajovic; Soummya Kar (EUROCON, 2019)

MATCHA: Speeding Up Decentralized SGD via Matching Decomposition Sampling. - Jianyu Wang; Anit Kumar Sahu; Zhouyi Yang; Gauri Joshi; Soummya Kar (CoRR, 2019)

Learning in Confusion: Batch Active Learning with Noisy Oracle. - Gaurav Gupta; Anit Kumar Sahu; Wan-Yi Lin (CoRR, 2019)

Black-box Adversarial Attacks with Bayesian Optimization. - Satya Narayan Shukla; Anit Kumar Sahu; Devin Willmott; J. Zico Kolter (CoRR, 2019)

Exploring the Error-Runtime Trade-off in Decentralized Optimization. - Jianyu Wang; Anit Kumar Sahu; Gauri Joshi; Soummya Kar (ACSSC, 2020)

Federated Optimization in Heterogeneous Networks. - Tian Li 005; Anit Kumar Sahu; Manzil Zaheer; Maziar Sanjabi; Ameet Talwalkar; Virginia Smith (MLSys, 2020)

Hard Label Black-box Adversarial Attacks in Low Query Budget Regimes. - Satya Narayan Shukla; Anit Kumar Sahu; Devin Willmott; J. Zico Kolter (CoRR, 2020)

Gaussian MRF Covariance Modeling for Efficient Black-Box Adversarial Attacks. - Anit Kumar Sahu; Satya Narayan Shukla; J. Zico Kolter (CoRR, 2020)

Decentralized Zeroth-Order Constrained Stochastic Optimization Algorithms: Frank-Wolfe and Variants With Applications to Black-Box Adversarial Attacks. - Anit Kumar Sahu; Soummya Kar (Proc. IEEE, 2020)

Federated Learning: Challenges, Methods, and Future Directions. - Tian Li 005; Anit Kumar Sahu; Ameet Talwalkar; Virginia Smith (IEEE Signal Process. Mag., 2020)

Multiplicative Filter Networks. - Rizal Fathony; Anit Kumar Sahu; Devin Willmott; J. Zico Kolter (ICLR, 2021)

Simple and Efficient Hard Label Black-box Adversarial Attacks in Low Query Budget Regimes. - Satya Narayan Shukla; Anit Kumar Sahu; Devin Willmott; J. Zico Kolter (KDD, 2021)

You Only Query Once: Effective Black Box Adversarial Attacks with Minimal Repeated Queries. - Devin Willmott; Anit Kumar Sahu; Fatemeh Sheikholeslami; Filipe Condessa; J. Zico Kolter (CoRR, 2021)

Federated Learning Challenges and Opportunities: An Outlook. - Jie Ding 002; Eric W. Tramel; Anit Kumar Sahu; Shuang Wu; Salman Avestimehr; Tao Zhang (ICASSP, 2022)

ILASR: Privacy-Preserving Incremental Learning for Automatic Speech Recognition at Production Scale. - Gopinath Chennupati; Milind Rao; Gurpreet Chadha; Aaron Eakin; Anirudh Raju; Gautam Tiwari; Anit Kumar Sahu; Ariya Rastrow; Jasha Droppo; Andy Oberlin; Buddha Nandanoor; Prahalad Venkataramanan; et al. (KDD, 2022)

Self-Aware Personalized Federated Learning. - Huili Chen; Jie Ding 002; Eric W. Tramel; Shuang Wu; Anit Kumar Sahu; Salman Avestimehr; Tao Zhang (NeurIPS, 2022)

FedBC: Calibrating Global and Local Models via Federated Learning Beyond Consensus. - Amrit Singh Bedi; Chen Fan; Alec Koppel; Anit Kumar Sahu; Brian M. Sadler; Furong Huang; Dinesh Manocha (CoRR, 2022)

Matcha: A Matching-Based Link Scheduling Strategy to Speed up Distributed Optimization. - Jianyu Wang; Anit Kumar Sahu; Gauri Joshi; Soummya Kar (IEEE Trans. Signal Process., 2022)

Federated Self-Learning with Weak Supervision for Speech Recognition. - Milind Rao; Gopinath Chennupati; Gautam Tiwari; Anit Kumar Sahu; Anirudh Raju; Ariya Rastrow; Jasha Droppo (ICASSP, 2023)

Learning When to Trust Which Teacher for Weakly Supervised ASR. - Aakriti Agrawal; Milind Rao; Anit Kumar Sahu; Gopinath Chennupati; Andreas Stolcke (INTERSPEECH, 2023)

Performance Scaling via Optimal Transport: Enabling Data Selection from Partially Revealed Sources. - Feiyang Kang; Hoang Anh Just; Anit Kumar Sahu; Ruoxi Jia 001 (NeurIPS, 2023)

Federated Representation Learning for Automatic Speech Recognition. - Guruprasad V. Ramesh; Gopinath Chennupati; Milind Rao; Anit Kumar Sahu; Ariya Rastrow; Jasha Droppo (CoRR, 2023)

RealFM: A Realistic Mechanism to Incentivize Data Contribution and Device Participation. - Marco Bornstein; Amrit Singh Bedi; Anit Kumar Sahu; Furqan Khan; Furong Huang (CoRR, 2023)

Nonlinear Gradient Mappings and Stochastic Optimization: A General Framework with Applications to Heavy-Tail Noise. - Dusan Jakovetic; Dragana Bajovic; Anit Kumar Sahu; Soummya Kar; Nemanja Milosevic; Dusan Stamenkovic (SIAM J. Optim., 2023)

Large Deviations for Products of Non-Identically Distributed Network Matrices With Applications to Communication-Efficient Distributed Learning and Inference. - Nemanja Petrovic; Dragana Bajovic; Soummya Kar; Dusan Jakovetic; Anit Kumar Sahu (IEEE Trans. Signal Process., 2023)

Distributed Recursive Estimation under Heavy-Tail Communication Noise. - Dusan Jakovetic; Manojlo Vukovic; Dragana Bajovic; Anit Kumar Sahu; Soummya Kar (SIAM J. Control. Optim., 2023)

Partial model averaging in Federated Learning: Performance guarantees and benefits. - Sunwoo Lee 001; Anit Kumar Sahu; Chaoyang He 001; Salman Avestimehr (Neurocomputing, 2023)

Get more for less: Principled Data Selection for Warming Up Fine-Tuning in LLMs. - Feiyang Kang; Hoang Anh Just; Yifan Sun 010; Himanshu Jahagirdar; Yuanzhi Zhang; Rongxing Du; Anit Kumar Sahu; Ruoxi Jia 001 (ICLR, 2024)

Data-Centric Human Preference Optimization with Rationales. - Hoang Anh Just; Ming Jin 002; Anit Kumar Sahu; Huy Phan; Ruoxi Jia 001 (CoRR, 2024)

Hierarchical Preference Optimization: Learning to achieve goals via feasible subgoals prediction. - Utsav Singh; Souradip Chakraborty; Wesley A. Suttle; Brian M. Sadler; Anit Kumar Sahu; Mubarak Shah; Vinay P. Namboodiri; Amrit Singh Bedi (CoRR, 2024)