Anit Kumar Sahu

Publication Activity (10 Years)

Years Active: 2012-2024
Publications (10 Years): 68

Top Topics

Empirical Risk Minimization

Speech Recognition

Hypothesis Testing

Likelihood Ratio Test

Top Venues

IEEE Trans. Signal Process.

Publications

Feiyang Kang, Hoang Anh Just, Yifan Sun, Himanshu Jahagirdar, Yuanzhi Zhang, Rongxing Du, Anit Kumar Sahu, Ruoxi Jia
Get more for less: Principled Data Selection for Warming Up Fine-Tuning in LLMs. CoRR (2024)
Hoang Anh Just, Ming Jin, Anit Kumar Sahu, Huy Phan, Ruoxi Jia
Data-Centric Human Preference Optimization with Rationales. CoRR (2024)
Feiyang Kang, Hoang Anh Just, Yifan Sun, Himanshu Jahagirdar, Yuanzhi Zhang, Rongxing Du, Anit Kumar Sahu, Ruoxi Jia
Get more for less: Principled Data Selection for Warming Up Fine-Tuning in LLMs. ICLR (2024)
Sunwoo Lee, Anit Kumar Sahu, Chaoyang He, Salman Avestimehr
Partial model averaging in Federated Learning: Performance guarantees and benefits. Neurocomputing 556 (2023)
Aakriti Agrawal, Milind Rao, Anit Kumar Sahu, Gopinath Chennupati, Andreas Stolcke
Learning When to Trust Which Teacher for Weakly Supervised ASR. INTERSPEECH (2023)
Feiyang Kang, Hoang Anh Just, Anit Kumar Sahu, Ruoxi Jia
Performance Scaling via Optimal Transport: Enabling Data Selection from Partially Revealed Sources. CoRR (2023)
Aakriti Agrawal, Milind Rao, Anit Kumar Sahu, Gopinath Chennupati, Andreas Stolcke
Learning When to Trust Which Teacher for Weakly Supervised ASR. CoRR (2023)
Dusan Jakovetic, Manojlo Vukovic, Dragana Bajovic, Anit Kumar Sahu, Soummya Kar
Distributed Recursive Estimation under Heavy-Tail Communication Noise. SIAM J. Control. Optim. 61 (3) (2023)
Nemanja Petrovic, Dragana Bajovic, Soummya Kar, Dusan Jakovetic, Anit Kumar Sahu
Large Deviations for Products of Non-Identically Distributed Network Matrices With Applications to Communication-Efficient Distributed Learning and Inference. IEEE Trans. Signal Process. 71 (2023)
Feiyang Kang, Hoang Anh Just, Anit Kumar Sahu, Ruoxi Jia
Performance Scaling via Optimal Transport: Enabling Data Selection from Partially Revealed Sources. NeurIPS (2023)
Milind Rao, Gopinath Chennupati, Gautam Tiwari, Anit Kumar Sahu, Anirudh Raju, Ariya Rastrow, Jasha Droppo
Federated Self-Learning with Weak Supervision for Speech Recognition. CoRR (2023)
Guruprasad V. Ramesh, Gopinath Chennupati, Milind Rao, Anit Kumar Sahu, Ariya Rastrow, Jasha Droppo
Federated Representation Learning for Automatic Speech Recognition. CoRR (2023)
Dusan Jakovetic, Dragana Bajovic, Anit Kumar Sahu, Soummya Kar, Nemanja Milosevic, Dusan Stamenkovic
Nonlinear Gradient Mappings and Stochastic Optimization: A General Framework with Applications to Heavy-Tail Noise. SIAM J. Optim. 33 (2) (2023)
Marco Bornstein, Amrit Singh Bedi, Anit Kumar Sahu, Furqan Khan, Furong Huang
RealFM: A Realistic Mechanism to Incentivize Data Contribution and Device Participation. CoRR (2023)
Milind Rao, Gopinath Chennupati, Gautam Tiwari, Anit Kumar Sahu, Anirudh Raju, Ariya Rastrow, Jasha Droppo
Federated Self-Learning with Weak Supervision for Speech Recognition. ICASSP (2023)
Amrit Singh Bedi, Chen Fan, Alec Koppel, Anit Kumar Sahu, Brian M. Sadler, Furong Huang, Dinesh Manocha
FedBC: Calibrating Global and Local Models via Federated Learning Beyond Consensus. CoRR (2022)
Jianyu Wang, Anit Kumar Sahu, Gauri Joshi, Soummya Kar
Matcha: A Matching-Based Link Scheduling Strategy to Speed up Distributed Optimization. IEEE Trans. Signal Process. 70 (2022)
Sunwoo Lee, Anit Kumar Sahu, Chaoyang He, Salman Avestimehr
Partial Model Averaging in Federated Learning: Performance Guarantees and Benefits. CoRR (2022)
Jie Ding, Eric Tramel, Anit Kumar Sahu, Shuang Wu, Salman Avestimehr, Tao Zhang
Federated Learning Challenges and Opportunities: An Outlook. ICASSP (2022)
Jie Ding, Eric Tramel, Anit Kumar Sahu, Shuang Wu, Salman Avestimehr, Tao Zhang
Federated Learning Challenges and Opportunities: An Outlook. CoRR (2022)
Huili Chen, Jie Ding, Eric Tramel, Shuang Wu, Anit Kumar Sahu, Salman Avestimehr, Tao Zhang
Self-Aware Personalized Federated Learning. CoRR (2022)
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, Zheng Wu, Pankaj Sitpure
ILASR: Privacy-Preserving Incremental Learning for Automatic Speech Recognition at Production Scale. KDD (2022)
Dusan Jakovetic, Dragana Bajovic, Anit Kumar Sahu, Soummya Kar, Nemanja Milosevic, Dusan Stamenkovic
Nonlinear gradient mappings and stochastic optimization: A general framework with applications to heavy-tail noise. CoRR (2022)
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, Zheng Wu, Pankaj Sitpure
ILASR: Privacy-Preserving Incremental Learning for Automatic Speech Recognition at Production Scale. CoRR (2022)
Huili Chen, Jie Ding, Eric W. Tramel, Shuang Wu, Anit Kumar Sahu, Salman Avestimehr, Tao Zhang
Self-Aware Personalized Federated Learning. NeurIPS (2022)
Rizal Fathony, Anit Kumar Sahu, Devin Willmott, J. Zico Kolter
Multiplicative Filter Networks. ICLR (2021)
Devin Willmott, Anit Kumar Sahu, Fatemeh Sheikholeslami, Filipe Condessa, J. Zico Kolter
You Only Query Once: Effective Black Box Adversarial Attacks with Minimal Repeated Queries. CoRR (2021)
Satya Narayan Shukla, Anit Kumar Sahu, Devin Willmott, J. Zico Kolter
Simple and Efficient Hard Label Black-box Adversarial Attacks in Low Query Budget Regimes. KDD (2021)
Anit Kumar Sahu, Soummya Kar
Decentralized Zeroth-Order Constrained Stochastic Optimization Algorithms: Frank-Wolfe and Variants With Applications to Black-Box Adversarial Attacks. Proc. IEEE 108 (11) (2020)
Zhanhong Jiang, Jonathan Francis, Anit Kumar Sahu, Sirajum Munir, Charles Shelton, Anthony Rowe, Mario Bergés
Data-driven Thermal Model Inference with ARMAX, in Smart Environments, based on Normalized Mutual Information. CoRR (2020)
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith
FedDANE: A Federated Newton-Type Method. CoRR (2020)
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, Virginia Smith
Federated Learning: Challenges, Methods, and Future Directions. IEEE Signal Process. Mag. 37 (3) (2020)
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith
Federated Optimization in Heterogeneous Networks. MLSys (2020)
Satya Narayan Shukla, Anit Kumar Sahu, Devin Willmott, J. Zico Kolter
Hard Label Black-box Adversarial Attacks in Low Query Budget Regimes. CoRR (2020)
Anit Kumar Sahu, Satya Narayan Shukla, J. Zico Kolter
Gaussian MRF Covariance Modeling for Efficient Black-Box Adversarial Attacks. CoRR (2020)
Jianyu Wang, Anit Kumar Sahu, Gauri Joshi, Soummya Kar
Exploring the Error-Runtime Trade-off in Decentralized Optimization. ACSSC (2020)
Gaurav Gupta, Anit Kumar Sahu, Wan-Yi Lin
Learning in Confusion: Batch Active Learning with Noisy Oracle. CoRR (2019)
Anit Kumar Sahu, Manzil Zaheer, Soummya Kar
Towards Gradient Free and Projection Free Stochastic Optimization. AISTATS (2019)
Jianyu Wang, Anit Kumar Sahu, Zhouyi Yang, Gauri Joshi, Soummya Kar
MATCHA: Speeding Up Decentralized SGD via Matching Decomposition Sampling. CoRR (2019)
Satya Narayan Shukla, Anit Kumar Sahu, Devin Willmott, J. Zico Kolter
Black-box Adversarial Attacks with Bayesian Optimization. CoRR (2019)
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith
FedDANE: A Federated Newton-Type Method. ACSSC (2019)
Anit Kumar Sahu, Dusan Jakovetic, Dragana Bajovic, Soummya Kar
Communication Efficient Distributed Estimation Over Directed Random Graphs. EUROCON (2019)
Ran Xin, Anit Kumar Sahu, Usman A. Khan, Soummya Kar
Distributed stochastic optimization with gradient tracking over strongly-connected networks. CoRR (2019)
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, Virginia Smith
Federated Learning: Challenges, Methods, and Future Directions. CoRR (2019)
Ran Xin, Anit Kumar Sahu, Usman A. Khan, Soummya Kar
Distributed stochastic optimization with gradient tracking over strongly-connected networks. CDC (2019)
Ran Xin, Anit Kumar Sahu, Soummya Kar, Usman A. Khan
Distributed empirical risk minimization over directed graphs. ACSSC (2019)
Anit Kumar Sahu, Dusan Jakovetic, Dragana Bajovic, Soummya Kar
Non-Asymptotic Rates for Communication Efficient Distributed Zeroth Order Strongly Convex Optimization. GlobalSIP (2018)
Anit Kumar Sahu, Dusan Jakovetic, Soummya Kar
CIRFE: A Distributed Random Fields Estimator. CoRR (2018)
Anit Kumar Sahu, Dusan Jakovetic, Soummya Kar
CIRFE: A Distributed Random Fields Estimator. IEEE Trans. Signal Process. 66 (18) (2018)
Anit Kumar Sahu, Shaunak Mishra, Narayan Bhamidipati
Managing App Install Ad Campaigns in RTB: A Q-Learning Approach. CoRR (2018)
Anit Kumar Sahu, Manzil Zaheer, Soummya Kar
Towards Gradient Free and Projection Free Stochastic Optimization. CoRR (2018)
Anit Kumar Sahu, Dusan Jakovetic, Soummya Kar
CREDO: A Communication-Efficient Distributed Estimation Algorithm. ISIT (2018)
Anit Kumar Sahu, Dusan Jakovetic, Dragana Bajovic, Soummya Kar
Communication efficient distributed weighted non-linear least squares estimation. EURASIP J. Adv. Signal Process. 2018 (2018)
Dusan Jakovetic, Dragana Bajovic, Anit Kumar Sahu, Soummya Kar
Convergence Rates for Distributed Stochastic Optimization Over Random Networks. CDC (2018)
Zhanhong Jiang, Jonathan Francis, Anit Kumar Sahu, Sirajum Munir, Charles Shelton, Anthony Rowe, Mario Berges
Data-driven Thermal Model Inference with ARMAX, in Smart Environments, based on Normalized Mutual Information. ACC (2018)
Anit Kumar Sahu, Dusan Jakovetic, Dragana Bajovic, Soummya Kar
Distributed Zeroth Order Optimization Over Random Networks: A Kiefer-Wolfowitz Stochastic Approximation Approach. CDC (2018)
Anit Kumar Sahu, Tian Li, Maziar Sanjabi, Manzil Zaheer, Ameet Talwalkar, Virginia Smith
On the Convergence of Federated Optimization in Heterogeneous Networks. CoRR (2018)
Dragana Bajovic, Dusan Jakovetic, Anit Kumar Sahu, Soummya Kar
Large Deviations for Products of Non-I.i.d. Stochastic Matrices with Application to Distributed Detection. ISIT (2018)
Anit Kumar Sahu, Soummya Kar
Recursive Distributed Detection for Composite Hypothesis Testing: Nonlinear Observation Models in Additive Gaussian Noise. IEEE Trans. Inf. Theory 63 (8) (2017)
Anit Kumar Sahu, Soummya Kar
Dist-Hedge: A partial information setting based distributed non-stochastic sequence prediction algorithm. GlobalSIP (2017)
Anit Kumar Sahu, Soummya Kar
Distributed recursive composite hypothesis testing: Imperfect communication. ISIT (2016)
Anit Kumar Sahu, Soummya Kar
Distributed Sequential Detection for Gaussian Shift-in-Mean Hypothesis Testing. IEEE Trans. Signal Process. 64 (1) (2016)
Soummya Kar, Rohit Negi, Majid Mahzoon, Anit Kumar Sahu
Queue-based broadcast gossip algorithm for consensus. Allerton (2016)
Anit Kumar Sahu, Soummya Kar
Distributed sequence prediction: A consensus+innovations approach. GlobalSIP (2016)
Anit Kumar Sahu, Soummya Kar
Distributed generalized likelihood ratio tests: Fundamental limits and tradeoffs. ICASSP (2016)
Anit Kumar Sahu, Soummya Kar, José M. F. Moura, H. Vincent Poor
Distributed Constrained Recursive Nonlinear Least-Squares Estimation: Algorithms and Asymptotics. IEEE Trans. Signal Inf. Process. over Networks 2 (4) (2016)
Anit Kumar Sahu, Soummya Kar, José M. F. Moura, H. Vincent Poor
Distributed Constrained Recursive Nonlinear Least-Squares Estimation: Algorithms and Asymptotics. CoRR (2016)
Anit Kumar Sahu, Soummya Kar
Recursive Distributed Detection for Composite Hypothesis Testing: Algorithms and Asymptotics. CoRR (2016)
Anit Kumar Sahu, Soummya Kar
Distributed sequential detection for Gaussian binary hypothesis testing: Heterogeneous networks. ACSSC (2014)
Anit Kumar Sahu, Soummya Kar
Distributed Sequential Detection for Gaussian Binary Hypothesis Testing. CoRR (2014)
Anit Kumar Sahu, Mrityunjoy Chakraborty
Fast and Accurate Frequency Estimation Using Sliding DFT CoRR (2012)