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ORCIDKannan Ramchandran
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- affiliation: University of California, Berkeley, USA
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2020 – today
- 2023
[j117]Orhan Öçal, Swanand Kadhe
, Xiao Li, Kannan Ramchandran:
Minimum-Rate Spectrum-Blind Sampling Based on Sparse-Graph Codes. IEEE Trans. Signal Process. 71: 587-600 (2023)- [i114]Yigit Efe Erginbas, Justin Singh Kang, Amirali Aghazadeh, Kannan Ramchandran:
Efficiently Computing Sparse Fourier Transforms of q-ary Functions. CoRR abs/2301.06200 (2023) - [i113]Justin Singh Kang, Ramtin Pedarsani, Kannan Ramchandran:
The Fair Value of Data Under Heterogeneous Privacy Constraints. CoRR abs/2301.13336 (2023) - [i112]Nived Rajaraman, Yanjun Han, Jiantao Jiao, Kannan Ramchandran:
Beyond UCB: Statistical Complexity and Optimal Algorithms for Non-linear Ridge Bandits. CoRR abs/2302.06025 (2023) - [i111]Nived Rajaraman, Devvrit, Aryan Mokhtari, Kannan Ramchandran:
Greedy Pruning with Group Lasso Provably Generalizes for Matrix Sensing and Neural Networks with Quadratic Activations. CoRR abs/2303.11453 (2023) - 2022
- [j116]Avishek Ghosh, Ashwin Pananjady, Adityanand Guntuboyina, Kannan Ramchandran:
Max-Affine Regression: Parameter Estimation for Gaussian Designs. IEEE Trans. Inf. Theory 68(3): 1851-1885 (2022) - [j115]Kangwook Lee, Nihar B. Shah, Longbo Huang, Kannan Ramchandran:
Addendum and Erratum to "The MDS Queue: Analysing the Latency Performance of Erasure Codes". IEEE Trans. Inf. Theory 68(9): 5850-5851 (2022) - [j114]Avishek Ghosh, Jichan Chung, Dong Yin, Kannan Ramchandran:
An Efficient Framework for Clustered Federated Learning. IEEE Trans. Inf. Theory 68(12): 8076-8091 (2022) - [c284]Zhengming Zhang, Ashwinee Panda, Linyue Song, Yaoqing Yang, Michael W. Mahoney, Prateek Mittal, Kannan Ramchandran, Joseph Gonzalez:
Neurotoxin: Durable Backdoors in Federated Learning. ICML 2022: 26429-26446 - [c283]Avishek Ghosh, Abishek Sankararaman, Kannan Ramchandran:
Multi-agent Heterogeneous Stochastic Linear Bandits. ECML/PKDD (4) 2022: 300-316 - [i110]Yaoqing Yang, Ryan Theisen, Liam Hodgkinson, Joseph E. Gonzalez, Kannan Ramchandran, Charles H. Martin, Michael W. Mahoney:
Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data. CoRR abs/2202.02842 (2022) - [i109]Gokul Swamy, Nived Rajaraman, Matthew Peng, Sanjiban Choudhury, J. Andrew Bagnell, Zhiwei Steven Wu, Jiantao Jiao, Kannan Ramchandran:
Minimax Optimal Online Imitation Learning via Replay Estimation. CoRR abs/2205.15397 (2022) - [i108]Avishek Ghosh, Abishek Sankararaman, Kannan Ramchandran, Tara Javidi, Arya Mazumdar:
Decentralized Competing Bandits in Non-Stationary Matching Markets. CoRR abs/2206.00120 (2022) - [i107]Zhengming Zhang, Ashwinee Panda, Linyue Song, Yaoqing Yang, Michael W. Mahoney, Joseph E. Gonzalez, Kannan Ramchandran, Prateek Mittal:
Neurotoxin: Durable Backdoors in Federated Learning. CoRR abs/2206.10341 (2022) - [i106]Yigit Efe Erginbas, Soham R. Phade, Kannan Ramchandran:
Interactive Recommendations for Optimal Allocations in Markets with Constraints. CoRR abs/2207.04143 (2022) - [i105]Amirali Aghazadeh, Nived Rajaraman, Tony Tu, Kannan Ramchandran:
Spectral Regularization Allows Data-frugal Learning over Combinatorial Spaces. CoRR abs/2210.02604 (2022) - [i104]Yigit Efe Erginbas, Soham R. Phade, Kannan Ramchandran:
Interactive Learning with Pricing for Optimal and Stable Allocations in Markets. CoRR abs/2212.06891 (2022) - 2021
- [j113]Avishek Ghosh, Raj Kumar Maity, Swanand Kadhe, Arya Mazumdar, Kannan Ramchandran:
Communication-Efficient and Byzantine-Robust Distributed Learning With Error Feedback. IEEE J. Sel. Areas Inf. Theory 2(3): 942-953 (2021) - [j112]Zihan Liu, Kannan Ramchandran:
Adaptive Douglas-Rachford Splitting Algorithm from a Yosida Approximation Standpoint. SIAM J. Optim. 31(3): 1971-1998 (2021) - [c282]Avishek Ghosh, Abishek Sankararaman, Kannan Ramchandran:
Problem-Complexity Adaptive Model Selection for Stochastic Linear Bandits. AISTATS 2021: 1396-1404 - [c281]Zhengming Zhang, Yaoqing Yang, Zhewei Yao, Yujun Yan, Joseph E. Gonzalez, Kannan Ramchandran, Michael W. Mahoney:
Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of Models. IEEE BigData 2021: 1214-1225 - [c280]Swanand Kadhe, Nived Rajaraman, Kannan Ramchandran:
FastShare: Scalable Secret Sharing by Leveraging Locality. ISIT 2021: 1118-1123 - [c279]Vipul Gupta, Dhruv Choudhary, Ping Tak Peter Tang, Xiaohan Wei, Xing Wang, Yuzhen Huang, Arun Kejariwal, Kannan Ramchandran, Michael W. Mahoney:
Training Recommender Systems at Scale: Communication-Efficient Model and Data Parallelism. KDD 2021: 2928-2936 - [c278]Amirali Aghazadeh, Vipul Gupta, Alex DeWeese, Onur Ozan Koyluoglu, Kannan Ramchandran:
BEAR: Sketching BFGS Algorithm for Ultra-High Dimensional Feature Selection in Sublinear Memory. MSML 2021: 75-92 - [c277]Nived Rajaraman, Yanjun Han, Lin Yang, Jingbo Liu, Jiantao Jiao, Kannan Ramchandran:
On the Value of Interaction and Function Approximation in Imitation Learning. NeurIPS 2021: 1325-1336 - [c276]Yaoqing Yang, Liam Hodgkinson, Ryan Theisen, Joe Zou, Joseph E. Gonzalez, Kannan Ramchandran, Michael W. Mahoney:
Taxonomizing local versus global structure in neural network loss landscapes. NeurIPS 2021: 18722-18733 - [c275]Vipul Gupta, Avishek Ghosh, Michal Derezinski, Rajiv Khanna, Kannan Ramchandran, Michael W. Mahoney:
LocalNewton: Reducing communication rounds for distributed learning. UAI 2021: 632-642 - [i103]Nived Rajaraman, Yanjun Han, Lin F. Yang, Kannan Ramchandran, Jiantao Jiao:
Provably Breaking the Quadratic Error Compounding Barrier in Imitation Learning, Optimally. CoRR abs/2102.12948 (2021) - [i102]Avishek Ghosh, Raj Kumar Maity, Arya Mazumdar, Kannan Ramchandran:
Escaping Saddle Points in Distributed Newton's Method with Communication efficiency and Byzantine Resilience. CoRR abs/2103.09424 (2021) - [i101]Vipul Gupta, Avishek Ghosh, Michal Derezinski, Rajiv Khanna, Kannan Ramchandran, Michael W. Mahoney:
LocalNewton: Reducing Communication Bottleneck for Distributed Learning. CoRR abs/2105.07320 (2021) - [i100]Avishek Ghosh, Abishek Sankararaman, Kannan Ramchandran:
Collaborative Learning and Personalization in Multi-Agent Stochastic Linear Bandits. CoRR abs/2106.08902 (2021) - [i99]Avishek Ghosh, Abishek Sankararaman, Kannan Ramchandran:
Model Selection for Generic Contextual Bandits. CoRR abs/2107.03455 (2021) - [i98]Avishek Ghosh, Sayak Ray Chowdhury, Kannan Ramchandran:
Model Selection with Near Optimal Rates for Reinforcement Learning with General Model Classes. CoRR abs/2107.05849 (2021) - [i97]Yaoqing Yang, Liam Hodgkinson, Ryan Theisen, Joe Zou, Joseph E. Gonzalez, Kannan Ramchandran, Michael W. Mahoney:
Taxonomizing local versus global structure in neural network loss landscapes. CoRR abs/2107.11228 (2021) - 2020
- [j111]Amirali Aghazadeh, Orhan Ocal, Kannan Ramchandran:
CRISPRL and: Interpretable large-scale inference of DNA repair landscape based on a spectral approach. Bioinform. 36(Supplement-1): i560-i568 (2020) - [c274]Yaoqing Yang, Jichan Chung, Guanhua Wang, Vipul Gupta, Adarsh Karnati, Kenan Jiang, Ion Stoica, Joseph Gonzalez, Kannan Ramchandran:
Robust Class Parallelism - Error Resilient Parallel Inference with Low Communication Cost. ACSSC 2020: 1064-1065 - [c273]Avishek Ghosh, Kannan Ramchandran:
Alternating Minimization Converges Super-Linearly for Mixed Linear Regression. AISTATS 2020: 1093-1103 - [c272]Vipul Gupta, Swanand Kadhe, Thomas A. Courtade, Michael W. Mahoney, Kannan Ramchandran:
OverSketched Newton: Fast Convex Optimization for Serverless Systems. IEEE BigData 2020: 288-297 - [c271]Steven Cao, Swanand Kadhe, Kannan Ramchandran:
CoVer: Collaborative Light-Node-Only Verification and Data Availability for Blockchains. Blockchain 2020: 45-52 - [c270]Haewon Jeong, Yaoqing Yang, Vipul Gupta, Christian Engelmann, Tze Meng Low, Viveck R. Cadambe, Kannan Ramchandran, Pulkit Grover:
3D Coded SUMMA: Communication-Efficient and Robust Parallel Matrix Multiplication. Euro-Par 2020: 392-407 - [c269]Vipul Gupta, Dominic Carrano, Yaoqing Yang, Vaishaal Shankar, Thomas A. Courtade, Kannan Ramchandran:
Serverless Straggler Mitigation using Error-Correcting Codes. ICDCS 2020: 135-145 - [c268]Roel Dobbe, Ye Pu, Jingge Zhu, Kannan Ramchandran, Claire J. Tomlin:
Local Differential Privacy for Multi-Agent Distributed Optimal Power Flow. ISGT-Europe 2020: 265-269 - [c267]Avishek Ghosh, Kannan Ramchandran:
Some Performance Guarantees of Global LASSO with Local Assumptions for Convolutional Sparse Design Matrices. ISIT 2020: 1391-1396 - [c266]Orhan Ocal, Kannan Ramchandran:
Fast Compressive Large-Scale Matrix-Matrix Multiplication Using Product Codes. ISIT 2020: 1426-1431 - [c265]Avishek Ghosh, Raj Kumar Maity, Arya Mazumdar, Kannan Ramchandran:
Communication Efficient Distributed Approximate Newton Method. ISIT 2020: 2539-2544 - [c264]Avishek Ghosh, Raj Kumar Maity, Swanand Kadhe, Arya Mazumdar, Kannan Ramchandran:
Communication Efficient and Byzantine Tolerant Distributed Learning. ISIT 2020: 2545-2550 - [c263]Swanand Kadhe, Onur Ozan Koyluoglu, Kannan Ramchandran:
Communication-Efficient Gradient Coding for Straggler Mitigation in Distributed Learning. ISIT 2020: 2634-2639 - [c262]Avishek Ghosh, Ashwin Pananjady, Aditya Guntuboyina, Kannan Ramchandran:
Max-affine regression with universal parameter estimation for small-ball designs. ISIT 2020: 2706-2710 - [c261]Avishek Ghosh, Raj Kumar Maity, Swanand Kadhe, Arya Mazumdar, Kannan Ramchandran:
Communication-Efficient and Byzantine-Robust Distributed Learning. ITA 2020: 1-28 - [c260]Avishek Ghosh, Jichan Chung, Dong Yin, Kannan Ramchandran:
An Efficient Framework for Clustered Federated Learning. NeurIPS 2020 - [c259]Nived Rajaraman, Lin F. Yang, Jiantao Jiao, Kannan Ramchandran:
Toward the Fundamental Limits of Imitation Learning. NeurIPS 2020 - [c258]Yaoqing Yang, Rajiv Khanna, Yaodong Yu, Amir Gholami, Kurt Keutzer, Joseph E. Gonzalez, Kannan Ramchandran, Michael W. Mahoney:
Boundary thickness and robustness in learning models. NeurIPS 2020 - [c257]Kangwook Lee, Changho Suh, Kannan Ramchandran:
Reprogramming GANs via Input Noise Design. ECML/PKDD (2) 2020: 256-271 - [i96]Vipul Gupta, Dominic Carrano, Yaoqing Yang, Vaishaal Shankar, Thomas A. Courtade, Kannan Ramchandran:
Serverless Straggler Mitigation using Local Error-Correcting Codes. CoRR abs/2001.07490 (2020) - [i95]Avishek Ghosh, Kannan Ramchandran:
Alternating Minimization Converges Super-Linearly for Mixed Linear Regression. CoRR abs/2004.10914 (2020) - [i94]Swanand Kadhe, Onur Ozan Koyluoglu, Kannan Ramchandran:
Communication-Efficient Gradient Coding for Straggler Mitigation in Distributed Learning. CoRR abs/2005.07184 (2020) - [i93]Avishek Ghosh, Abishek Sankararaman, Kannan Ramchandran:
Problem-Complexity Adaptive Model Selection for Stochastic Linear Bandits. CoRR abs/2006.02612 (2020) - [i92]Avishek Ghosh, Jichan Chung, Dong Yin, Kannan Ramchandran:
An Efficient Framework for Clustered Federated Learning. CoRR abs/2006.04088 (2020) - [i91]Yaoqing Yang, Rajiv Khanna, Yaodong Yu, Amir Gholami, Kurt Keutzer, Joseph E. Gonzalez, Kannan Ramchandran, Michael W. Mahoney:
Boundary thickness and robustness in learning models. CoRR abs/2007.05086 (2020) - [i90]Vipul Gupta, Soham R. Phade, Thomas A. Courtade, Kannan Ramchandran:
Utility-based Resource Allocation and Pricing for Serverless Computing. CoRR abs/2008.07793 (2020) - [i89]Nived Rajaraman, Lin F. Yang, Jiantao Jiao, Kannan Ramchandran:
Toward the Fundamental Limits of Imitation Learning. CoRR abs/2009.05990 (2020) - [i88]Swanand Kadhe, Nived Rajaraman, Onur Ozan Koyluoglu, Kannan Ramchandran:
FastSecAgg: Scalable Secure Aggregation for Privacy-Preserving Federated Learning. CoRR abs/2009.11248 (2020) - [i87]Steven Cao, Swanand Kadhe, Kannan Ramchandran:
CoVer: Collaborative Light-Node-Only Verification and Data Availability for Blockchains. CoRR abs/2010.00217 (2020) - [i86]Vipul Gupta, Dhruv Choudhary, Ping Tak Peter Tang, Xiaohan Wei, Xing Wang, Yuzhen Huang, Arun Kejariwal, Kannan Ramchandran, Michael W. Mahoney:
Fast Distributed Training of Deep Neural Networks: Dynamic Communication Thresholding for Model and Data Parallelism. CoRR abs/2010.08899 (2020) - [i85]Amirali Aghazadeh, Vipul Gupta, Alex DeWeese, Onur Ozan Koyluoglu, Kannan Ramchandran:
BEAR: Sketching BFGS Algorithm for Ultra-High Dimensional Feature Selection in Sublinear Memory. CoRR abs/2010.13829 (2020)
2010 – 2019
- 2019
- [j110]Angie Wang, Woo-Rham Bae, Jaeduk Han, Stevo Bailey, Orhan Ocal, Paul Rigge, Zhongkai Wang, Kannan Ramchandran, Elad Alon, Borivoje Nikolic:
A Real-Time, 1.89-GHz Bandwidth, 175-kHz Resolution Sparse Spectral Analysis RISC-V SoC in 16-nm FinFET. IEEE J. Solid State Circuits 54(7): 1993-2008 (2019) - [j109]Dong Yin, Ramtin Pedarsani, Yudong Chen, Kannan Ramchandran:
Learning Mixtures of Sparse Linear Regressions Using Sparse Graph Codes. IEEE Trans. Inf. Theory 65(3): 1430-1451 (2019) - [j108]Xiao Li, Dong Yin, Sameer Pawar, Ramtin Pedarsani, Kannan Ramchandran:
Sub-Linear Time Support Recovery for Compressed Sensing Using Sparse-Graph Codes. IEEE Trans. Inf. Theory 65(10): 6580-6619 (2019) - [j107]Kangwook Lee, Kabir Chandrasekher, Ramtin Pedarsani, Kannan Ramchandran:
SAFFRON: A Fast, Efficient, and Robust Framework for Group Testing Based on Sparse-Graph Codes. IEEE Trans. Signal Process. 67(17): 4649-4664 (2019) - [c256]Sinho Chewi, Forest Yang, Avishek Ghosh, Abhay Parekh, Kannan Ramchandran:
Matching Observations to Distributions: Efficient Estimation via Sparsified Hungarian Algorithm. Allerton 2019: 368-375 - [c255]Orhan Ocal, Oguz H. Elibol, Gokce Keskin, Cory Stephenson, Anil Thomas, Kannan Ramchandran:
Adversarially Trained Autoencoders for Parallel-data-free Voice Conversion. ICASSP 2019: 2777-2781 - [c254]Frank Ong, Reinhard Heckel, Kannan Ramchandran:
A Fast and Robust Paradigm for Fourier Compressed Sensing Based on Coded Sampling. ICASSP 2019: 5117-5121 - [c253]Dong Yin, Yudong Chen, Kannan Ramchandran, Peter L. Bartlett:
Defending Against Saddle Point Attack in Byzantine-Robust Distributed Learning. ICML 2019: 7074-7084 - [c252]Dong Yin, Kannan Ramchandran, Peter L. Bartlett:
Rademacher Complexity for Adversarially Robust Generalization. ICML 2019: 7085-7094 - [c251]Kangwook Lee, Hoon Kim, Kyungmin Lee, Changho Suh, Kannan Ramchandran:
Synthesizing Differentially Private Datasets using Random Mixing. ISIT 2019: 542-546 - [c250]Orhan Ocal, Swanand Kadhe, Kannan Ramchandran:
Low-degree Pseudo-Boolean Function Recovery Using Codes. ISIT 2019: 1207-1211 - [c249]Swanand Kadhe, Onur Ozan Koyluoglu, Kannan Ramchandran:
Gradient Coding Based on Block Designs for Mitigating Adversarial Stragglers. ISIT 2019: 2813-2817 - [i84]Kamil Nar, Orhan Ocal, S. Shankar Sastry, Kannan Ramchandran:
Cross-Entropy Loss and Low-Rank Features Have Responsibility for Adversarial Examples. CoRR abs/1901.08360 (2019) - [i83]Vipul Gupta, Swanand Kadhe, Thomas A. Courtade, Michael W. Mahoney, Kannan Ramchandran:
OverSketched Newton: Fast Convex Optimization for Serverless Systems. CoRR abs/1903.08857 (2019) - [i82]Swanand Kadhe, Onur Ozan Koyluoglu, Kannan Ramchandran:
Gradient Coding Based on Block Designs for Mitigating Adversarial Stragglers. CoRR abs/1904.13373 (2019) - [i81]Orhan Ocal, Oguz H. Elibol, Gokce Keskin, Cory Stephenson, Anil Thomas, Kannan Ramchandran:
Adversarially Trained Autoencoders for Parallel-Data-Free Voice Conversion. CoRR abs/1905.03864 (2019) - [i80]Avishek Ghosh, Justin Hong, Dong Yin, Kannan Ramchandran:
Robust Federated Learning in a Heterogeneous Environment. CoRR abs/1906.06629 (2019) - [i79]Avishek Ghosh, Ashwin Pananjady, Adityanand Guntuboyina, Kannan Ramchandran:
Max-Affine Regression: Provable, Tractable, and Near-Optimal Statistical Estimation. CoRR abs/1906.09255 (2019) - [i78]Swanand Kadhe, Jichan Chung, Kannan Ramchandran:
SeF: A Secure Fountain Architecture for Slashing Storage Costs in Blockchains. CoRR abs/1906.12140 (2019) - [i77]Avishek Ghosh, Raj Kumar Maity, Swanand Kadhe, Arya Mazumdar, Kannan Ramchandran:
Communication-Efficient and Byzantine-Robust Distributed Learning. CoRR abs/1911.09721 (2019) - 2018
- [j106]Sameer Pawar, Kannan Ramchandran:
FFAST: An Algorithm for Computing an Exactly k-Sparse DFT in O(k log k) Time. IEEE Trans. Inf. Theory 64(1): 429-450 (2018) - [j105]Sameer Pawar, Kannan Ramchandran:
R-FFAST: A Robust Sub-Linear Time Algorithm for Computing a Sparse DFT. IEEE Trans. Inf. Theory 64(1): 451-466 (2018) - [j104]Kangwook Lee, Maximilian Lam, Ramtin Pedarsani, Dimitris S. Papailiopoulos, Kannan Ramchandran:
Speeding Up Distributed Machine Learning Using Codes. IEEE Trans. Inf. Theory 64(3): 1514-1529 (2018) - [j103]K. V. Rashmi, Nihar B. Shah, Kannan Ramchandran, P. Vijay Kumar:
Information-Theoretically Secure Erasure Codes for Distributed Storage. IEEE Trans. Inf. Theory 64(3): 1621-1646 (2018) - [c248]Reinhard Heckel, Max Simchowitz, Kannan Ramchandran, Martin J. Wainwright:
Approximate ranking from pairwise comparisons. AISTATS 2018: 1057-1066 - [c247]Dong Yin, Ashwin Pananjady, Maximilian Lam, Dimitris S. Papailiopoulos, Kannan Ramchandran, Peter L. Bartlett:
Gradient Diversity: a Key Ingredient for Scalable Distributed Learning. AISTATS 2018: 1998-2007 - [c246]Avishek Ghosh, Kannan Ramchandran:
Online Scoring with Delayed Information: A Convex Optimization Viewpoint. Allerton 2018: 454-461 - [c245]Avishek Ghosh, Kannan Ramchandran:
Faster Data-access in Large-scale Systems: Network-scale Latency Analysis under General Service-time Distributions. Allerton 2018: 757-764 - [c244]Ye Pu, Jingge Zhu, Karl Henrik Johansson, Kannan Ramchandran, Claire J. Tomlin:
Coded Control over Lossy Networks. ACC 2018: 3602-3608 - [c243]Vipul Gupta, Shusen Wang, Thomas A. Courtade, Kannan Ramchandran:
OverSketch: Approximate Matrix Multiplication for the Cloud. IEEE BigData 2018: 298-304 - [c242]Angie Wang, Woo-Rham Bae, Jaeduk Han, Stevo Bailey, Paul Rigge, Orhan Ocal, Zhongkai Wang, Kannan Ramchandran, Elad Alon, Borivoje Nikolic:
A Real-Time, Analog/Digital Co-Designed 1.89-GHz Bandwidth, 175-kHz Resolution Sparse Spectral Analysis RISC-V SoC in 16-nm FinFET. ESSCIRC 2018: 322-325 - [c241]Kangwook Lee, Kyungmin Lee, Hoon Kim, Changho Suh, Kannan Ramchandran:
SGD on Random Mixtures: Private Machine Learning under Data Breach Threats. ICLR (Workshop) 2018 - [c240]Dong Yin, Yudong Chen, Kannan Ramchandran, Peter L. Bartlett:
Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates. ICML 2018: 5636-5645 - [c239]Tavor Baharav, Kangwook Lee, Orhan Ocal, Kannan Ramchandran:
Straggler-Proofing Massive-Scale Distributed Matrix Multiplication with D-Dimensional Product Codes. ISIT 2018: 1993-1997 - [i76]Reinhard Heckel, Max Simchowitz, Kannan Ramchandran, Martin J. Wainwright:
Approximate Ranking from Pairwise Comparisons. CoRR abs/1801.01253 (2018) - [i75]Dong Yin, Yudong Chen, Kannan Ramchandran, Peter L. Bartlett:
Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates. CoRR abs/1803.01498 (2018) - [i74]Dong Yin, Yudong Chen, Kannan Ramchandran, Peter L. Bartlett:
Defending Against Saddle Point Attack in Byzantine-Robust Distributed Learning. CoRR abs/1806.05358 (2018) - [i73]Roel Dobbe, Ye Pu, Jingge Zhu, Kannan Ramchandran, Claire J. Tomlin:
Customized Local Differential Privacy for Multi-Agent Distributed Optimization. CoRR abs/1806.06035 (2018) - [i72]Sinho Chewi, Forest Yang, Avishek Ghosh, Abhay Parekh, Kannan Ramchandran:
Online Absolute Ranking with Partial Information: A Bipartite Graph Matching Approach. CoRR abs/1806.06766 (2018) - [i71]Avishek Ghosh, Kannan Ramchandran:
Faster Data-access in Large-scale Systems: Network-scale Latency Analysis under General Service-time Distributions. CoRR abs/1807.02253 (2018) - [i70]Avishek Ghosh, Kannan Ramchandran:
Online Scoring with Delayed Information: A Convex Optimization Viewpoint. CoRR abs/1807.03379 (2018) - [i69]Dong Yin, Kannan Ramchandran, Peter L. Bartlett:
Rademacher Complexity for Adversarially Robust Generalization. CoRR abs/1810.11914 (2018) - [i68]Vipul Gupta, Shusen Wang, Thomas A. Courtade, Kannan Ramchandran:
OverSketch: Approximate Matrix Multiplication for the Cloud. CoRR abs/1811.02653 (2018) - [i67]Gary Cheng, Armin Askari, Laurent El Ghaoui, Kannan Ramchandran:
Frank-Wolfe Algorithm for Exemplar Selection. CoRR abs/1811.02702 (2018) - 2017
- [j102]Horia Mania, Xinghao Pan, Dimitris S. Papailiopoulos, Benjamin Recht, Kannan Ramchandran, Michael I. Jordan:
Perturbed Iterate Analysis for Asynchronous Stochastic Optimization. SIAM J. Optim. 27(4): 2202-2229 (2017) - [j101]Kangwook Lee, Nihar B. Shah, Longbo Huang, Kannan Ramchandran:
The MDS Queue: Analysing the Latency Performance of Erasure Codes. IEEE Trans. Inf. Theory 63(5): 2822-2842 (2017) - [j100]Ramtin Pedarsani, Dong Yin, Kangwook Lee, Kannan Ramchandran:
PhaseCode: Fast and Efficient Compressive Phase Retrieval Based on Sparse-Graph Codes. IEEE Trans. Inf. Theory 63(6): 3663-3691 (2017) - [j99]K. V. Rashmi, Nihar B. Shah, Kannan Ramchandran:
A Piggybacking Design Framework for Read-and Download-Efficient Distributed Storage Codes. IEEE Trans. Inf. Theory 63(9): 5802-5820 (2017) - [j98]Giulia Fanti, Peter Kairouz, Sewoong Oh, Kannan Ramchandran, Pramod Viswanath:
Hiding the Rumor Source. IEEE Trans. Inf. Theory 63(10): 6679-6713 (2017) - [j97]Kangwook Lee, Ramtin Pedarsani, Kannan Ramchandran:
On Scheduling Redundant Requests With Cancellation Overheads. IEEE/ACM Trans. Netw. 25(2): 1279-1290 (2017) - [c238]Dong Yin, Ramtin Pedarsani, Yudong Chen, Kannan Ramchandran:
Learning mixtures of sparse linear regressions using sparse graph codes. Allerton 2017: 588-595 - [c237]Jingge Zhu, Ye Pu, Vipul Gupta, Claire J. Tomlin, Kannan Ramchandran:
A sequential approximation framework for coded distributed optimization. Allerton 2017: 1240-1247 - [c236]