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Venkata Pillutla

Venkata Krishna Pillutla

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affiliation: University of Washington, WA, USA

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Journal Articles

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2017
[j1]
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  - journals/bmcsb/RuffaloSPVB17
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  - https://dblp.org/rec/journals/bmcsb/RuffaloSPVB17
Matthew Ruffalo, Petar Stojanov, Venkata Krishna Pillutla, Rohan Varma, Ziv Bar-Joseph:
Reconstructing cancer drug response networks using multitask learning. BMC Syst. Biol. 11(1): 96:1-96:15 (2017)

Conference and Workshop Papers

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2018
[c5]
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  - conf/nips/PillutlaRKH18
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Venkata Krishna Pillutla, Vincent Roulet, Sham M. Kakade, Zaïd Harchaoui:
A Smoother Way to Train Structured Prediction Models. NeurIPS 2018: 4771-4783
2017
[c4]
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Travis Dick, Mu Li, Venkata Krishna Pillutla, Colin White, Maria-Florina Balcan, Alexander J. Smola:
Data Driven Resource Allocation for Distributed Learning. AAAI Workshops 2017
[c3]
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Travis Dick, Mu Li, Venkata Krishna Pillutla, Colin White, Nina Balcan, Alexander J. Smola:
Data Driven Resource Allocation for Distributed Learning. AISTATS 2017: 662-671
[c2]
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  - conf/fsttcs/JainKKNPS17
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Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Venkata Krishna Pillutla, Aaron Sidford:
A Markov Chain Theory Approach to Characterizing the Minimax Optimality of Stochastic Gradient Descent (for Least Squares). FSTTCS 2017: 2:1-2:10
2016
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Venkata Krishna Pillutla, Zhanpeng Fang, Pravallika Devineni, Christos Faloutsos, Danai Koutra, Jie Tang:
On Skewed Multi-dimensional Distributions: the FusionRP Model, Algorithms, and Discoveries. SDM 2016: 783-791

Informal and Other Publications

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2019
[i4]
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  - journals/corr/abs-1902-03228
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Venkata Krishna Pillutla, Vincent Roulet, Sham M. Kakade, Zaïd Harchaoui:
A Smoother Way to Train Structured Prediction Models. CoRR abs/1902.03228 (2019)
[i3]
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  - journals/corr/abs-1912-13445
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Venkata Krishna Pillutla, Sham M. Kakade, Zaïd Harchaoui:
Robust Aggregation for Federated Learning. CoRR abs/1912.13445 (2019)
2017
[i2]
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  - journals/corr/abs-1710-09430
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Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Venkata Krishna Pillutla, Aaron Sidford:
A Markov Chain Theory Approach to Characterizing the Minimax Optimality of Stochastic Gradient Descent (for Least Squares). CoRR abs/1710.09430 (2017)
2015
[i1]
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Travis Dick, Mu Li, Venkata Krishna Pillutla, Colin White, Maria-Florina Balcan, Alexander J. Smola:
Data Driven Resource Allocation for Distributed Learning. CoRR abs/1512.04848 (2015)

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