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2020 – today
- 2024
[c28]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- [i31]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 abs/2405.02774 (2024) - [i30]Hoang Anh Just, Ming Jin, Anit Kumar Sahu, Huy Phan, Ruoxi Jia:
Data-Centric Human Preference Optimization with Rationales. CoRR abs/2407.14477 (2024) - 2023
- [j12]Sunwoo Lee
, Anit Kumar Sahu, Chaoyang He, Salman Avestimehr:
Partial model averaging in Federated Learning: Performance guarantees and benefits. Neurocomputing 556: 126647 (2023) - [j11]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): 1582-1609 (2023) - [j10]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): 394-423 (2023) - [j9]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: 1319-1333 (2023) - [c27]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: 1-5 - [c26]Aakriti Agrawal, Milind Rao, Anit Kumar Sahu, Gopinath Chennupati, Andreas Stolcke:
Learning When to Trust Which Teacher for Weakly Supervised ASR. INTERSPEECH 2023: 381-385 - [c25]Feiyang Kang, Hoang Anh Just, Anit Kumar Sahu, Ruoxi Jia:
Performance Scaling via Optimal Transport: Enabling Data Selection from Partially Revealed Sources. NeurIPS 2023 - [i29]Aakriti Agrawal, Milind Rao, Anit Kumar Sahu, Gopinath Chennupati, Andreas Stolcke:
Learning When to Trust Which Teacher for Weakly Supervised ASR. CoRR abs/2306.12012 (2023) - [i28]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 abs/2306.12015 (2023) - [i27]Feiyang Kang, Hoang Anh Just, Anit Kumar Sahu, Ruoxi Jia:
Performance Scaling via Optimal Transport: Enabling Data Selection from Partially Revealed Sources. CoRR abs/2307.02460 (2023) - [i26]Guruprasad V. Ramesh, Gopinath Chennupati, Milind Rao, Anit Kumar Sahu, Ariya Rastrow, Jasha Droppo:
Federated Representation Learning for Automatic Speech Recognition. CoRR abs/2308.02013 (2023) - [i25]Marco Bornstein, Amrit Singh Bedi, Anit Kumar Sahu, Furqan Khan, Furong Huang:
RealFM: A Realistic Mechanism to Incentivize Data Contribution and Device Participation. CoRR abs/2310.13681 (2023) - 2022
- [j8]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: 5208-5221 (2022) - [c24]Jie Ding, Eric W. Tramel, Anit Kumar Sahu, Shuang Wu, Salman Avestimehr, Tao Zhang:
Federated Learning Challenges and Opportunities: An Outlook. ICASSP 2022: 8752-8756 - [c23]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: 2780-2788 - [c22]Huili Chen, Jie Ding, Eric W. Tramel, Shuang Wu, Anit Kumar Sahu, Salman Avestimehr, Tao Zhang:
Self-Aware Personalized Federated Learning. NeurIPS 2022 - [i24]Sunwoo Lee, Anit Kumar Sahu, Chaoyang He, Salman Avestimehr:
Partial Model Averaging in Federated Learning: Performance Guarantees and Benefits. CoRR abs/2201.03789 (2022) - [i23]Jie Ding, Eric W. Tramel, Anit Kumar Sahu, Shuang Wu, Salman Avestimehr, Tao Zhang:
Federated Learning Challenges and Opportunities: An Outlook. CoRR abs/2202.00807 (2022) - [i22]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 abs/2204.02593 (2022) - [i21]Huili Chen, Jie Ding, Eric W. Tramel, Shuang Wu, Anit Kumar Sahu, Salman Avestimehr, Tao Zhang:
Self-Aware Personalized Federated Learning. CoRR abs/2204.08069 (2022) - [i20]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 abs/2206.10815 (2022) - [i19]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 abs/2207.09078 (2022) - 2021
- [c21]Rizal Fathony, Anit Kumar Sahu, Devin Willmott, J. Zico Kolter:
Multiplicative Filter Networks. ICLR 2021 - [c20]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: 1461-1469 - [i18]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 abs/2102.00029 (2021) - 2020
- [j7]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): 1890-1905 (2020) - [j6]Tian Li, Anit Kumar Sahu, Ameet Talwalkar, Virginia Smith:
Federated Learning: Challenges, Methods, and Future Directions. IEEE Signal Process. Mag. 37(3): 50-60 (2020) - [c19]Jianyu Wang, Anit Kumar Sahu, Gauri Joshi, Soummya Kar:
Exploring the Error-Runtime Trade-off in Decentralized Optimization. ACSSC 2020: 910-914 - [c18]Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith:
Federated Optimization in Heterogeneous Networks. MLSys 2020 - [i17]Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith:
FedDANE: A Federated Newton-Type Method. CoRR abs/2001.01920 (2020) - [i16]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 abs/2006.06088 (2020) - [i15]Satya Narayan Shukla, Anit Kumar Sahu, Devin Willmott, J. Zico Kolter:
Hard Label Black-box Adversarial Attacks in Low Query Budget Regimes. CoRR abs/2007.07210 (2020) - [i14]Anit Kumar Sahu, Satya Narayan Shukla, J. Zico Kolter:
Gaussian MRF Covariance Modeling for Efficient Black-Box Adversarial Attacks. CoRR abs/2010.04205 (2020)
2010 – 2019
- 2019
- [c17]Ran Xin, Anit Kumar Sahu, Soummya Kar, Usman A. Khan:
Distributed empirical risk minimization over directed graphs. ACSSC 2019: 189-193 - [c16]Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith:
FedDANE: A Federated Newton-Type Method. ACSSC 2019: 1227-1231 - [c15]Anit Kumar Sahu, Manzil Zaheer, Soummya Kar:
Towards Gradient Free and Projection Free Stochastic Optimization. AISTATS 2019: 3468-3477 - [c14]Ran Xin, Anit Kumar Sahu, Usman A. Khan, Soummya Kar:
Distributed stochastic optimization with gradient tracking over strongly-connected networks. CDC 2019: 8353-8358 - [c13]Anit Kumar Sahu, Dusan Jakovetic, Dragana Bajovic, Soummya Kar:
Communication Efficient Distributed Estimation Over Directed Random Graphs. EUROCON 2019: 1-5 - [i13]Ran Xin, Anit Kumar Sahu, Usman A. Khan, Soummya Kar:
Distributed stochastic optimization with gradient tracking over strongly-connected networks. CoRR abs/1903.07266 (2019) - [i12]Jianyu Wang, Anit Kumar Sahu, Zhouyi Yang, Gauri Joshi, Soummya Kar:
MATCHA: Speeding Up Decentralized SGD via Matching Decomposition Sampling. CoRR abs/1905.09435 (2019) - [i11]Tian Li, Anit Kumar Sahu, Ameet Talwalkar, Virginia Smith:
Federated Learning: Challenges, Methods, and Future Directions. CoRR abs/1908.07873 (2019) - [i10]Gaurav Gupta, Anit Kumar Sahu, Wan-Yi Lin:
Learning in Confusion: Batch Active Learning with Noisy Oracle. CoRR abs/1909.12473 (2019) - [i9]Satya Narayan Shukla, Anit Kumar Sahu, Devin Willmott, J. Zico Kolter:
Black-box Adversarial Attacks with Bayesian Optimization. CoRR abs/1909.13857 (2019) - 2018
- [j5]Anit Kumar Sahu, Dusan Jakovetic, Dragana Bajovic, Soummya Kar:
Communication efficient distributed weighted non-linear least squares estimation. EURASIP J. Adv. Signal Process. 2018: 66 (2018) - [j4]Anit Kumar Sahu, Dusan Jakovetic, Soummya Kar:
CIRFE: A Distributed Random Fields Estimator. IEEE Trans. Signal Process. 66(18): 4980-4995 (2018) - [c12]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: 4634-4639 - [c11]Dusan Jakovetic, Dragana Bajovic, Anit Kumar Sahu, Soummya Kar:
Convergence Rates for Distributed Stochastic Optimization Over Random Networks. CDC 2018: 4238-4245 - [c10]Anit Kumar Sahu, Dusan Jakovetic, Dragana Bajovic, Soummya Kar:
Distributed Zeroth Order Optimization Over Random Networks: A Kiefer-Wolfowitz Stochastic Approximation Approach. CDC 2018: 4951-4958 - [c9]Anit Kumar Sahu, Dusan Jakovetic, Dragana Bajovic, Soummya Kar:
Non-Asymptotic Rates for Communication Efficient Distributed Zeroth Order Strongly Convex Optimization. GlobalSIP 2018: 628-632 - [c8]Anit Kumar Sahu, Dusan Jakovetic, Soummya Kar:
CREDO: A Communication-Efficient Distributed Estimation Algorithm. ISIT 2018: 516-520 - [c7]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: 1061-1065 - [i8]Anit Kumar Sahu, Dusan Jakovetic, Soummya Kar:
CIRFE: A Distributed Random Fields Estimator. CoRR abs/1802.04943 (2018) - [i7]Anit Kumar Sahu, Manzil Zaheer, Soummya Kar:
Towards Gradient Free and Projection Free Stochastic Optimization. CoRR abs/1810.03233 (2018) - [i6]Anit Kumar Sahu, Shaunak Mishra, Narayan Bhamidipati:
Managing App Install Ad Campaigns in RTB: A Q-Learning Approach. CoRR abs/1811.04475 (2018) - [i5]Anit Kumar Sahu, Tian Li, Maziar Sanjabi, Manzil Zaheer, Ameet Talwalkar, Virginia Smith:
On the Convergence of Federated Optimization in Heterogeneous Networks. CoRR abs/1812.06127 (2018) - 2017
- [j3]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): 4797-4828 (2017) - [c6]Anit Kumar Sahu, Soummya Kar:
Dist-Hedge: A partial information setting based distributed non-stochastic sequence prediction algorithm. GlobalSIP 2017: 528-532 - 2016
- [j2]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): 426-441 (2016) - [j1]Anit Kumar Sahu, Soummya Kar:
Distributed Sequential Detection for Gaussian Shift-in-Mean Hypothesis Testing. IEEE Trans. Signal Process. 64(1): 89-103 (2016) - [c5]Soummya Kar, Rohit Negi, Majid Mahzoon, Anit Kumar Sahu:
Queue-based broadcast gossip algorithm for consensus. Allerton 2016: 1259-1266 - [c4]Anit Kumar Sahu, Soummya Kar:
Distributed sequence prediction: A consensus+innovations approach. GlobalSIP 2016: 312-316 - [c3]Anit Kumar Sahu, Soummya Kar:
Distributed generalized likelihood ratio tests: Fundamental limits and tradeoffs. ICASSP 2016: 4573-4577 - [c2]Anit Kumar Sahu, Soummya Kar:
Distributed recursive composite hypothesis testing: Imperfect communication. ISIT 2016: 2679-2683 - [i4]Anit Kumar Sahu, Soummya Kar:
Recursive Distributed Detection for Composite Hypothesis Testing: Algorithms and Asymptotics. CoRR abs/1601.04779 (2016) - [i3]Anit Kumar Sahu, Soummya Kar, José M. F. Moura, H. Vincent Poor:
Distributed Constrained Recursive Nonlinear Least-Squares Estimation: Algorithms and Asymptotics. CoRR abs/1602.00382 (2016) - 2014
- [c1]Anit Kumar Sahu, Soummya Kar:
Distributed sequential detection for Gaussian binary hypothesis testing: Heterogeneous networks. ACSSC 2014: 723-727 - [i2]Anit Kumar Sahu, Soummya Kar:
Distributed Sequential Detection for Gaussian Binary Hypothesis Testing. CoRR abs/1411.7716 (2014) - 2012
- [i1]Anit Kumar Sahu, Mrityunjoy Chakraborty:
Fast and Accurate Frequency Estimation Using Sliding DFT. CoRR abs/1202.4446 (2012)
Coauthor Index
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