Author of the publication

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

Extreme Stochastic Variational Inference: Distributed Inference for Large Scale Mixture Models.

J. Zhang, P. Raman, S. Ji, H. Yu, S. Vishwanathan, and I. Dhillon. AISTATS, volume 89 of Proceedings of Machine Learning Research, page 935-943. PMLR, (2019)

Please choose a person to relate this publication to

To differ between persons with the same name, the academic degree and the title of an important publication will be displayed. You can also use the button next to the name to display some publications already assigned to the person.

Rupa Parameswaran

Sreenath Ramanath

Anupama Parameswaran

Lalitha Parameswaran

Aravamudhan Raman

Other publications of authors with the same name

DS-FACTO: Doubly Separable Factorization Machines.P. Raman, and S. Vishwanathan. CoRR, (2020)Krylov Cubic Regularized Newton: A Subspace Second-Order Method with Dimension-Free Convergence Rate.R. Jiang, P. Raman, S. Sabach, A. Mokhtari, M. Hong, and V. Cevher. AISTATS, volume 238 of Proceedings of Machine Learning Research, page 4411-4419. PMLR, (2024)HLAT: High-quality Large Language Model Pre-trained on AWS Trainium.H. Fan, H. Zhou, G. Huang, P. Raman, X. Fu, G. Gupta, D. Ram, Y. Wang, and J. Huan. CoRR, (2024)Scaling Multinomial Logistic Regression via Hybrid Parallelism.P. Raman, S. Srinivasan, S. Matsushima, X. Zhang, H. Yun, and S. Vishwanathan. KDD, page 1460-1470. ACM, (2019)DS-MLR: Exploiting Double Separability for Scaling up Distributed Multinomial Logistic Regression.P. Raman, S. Matsushima, X. Zhang, H. Yun, and S. Vishwanathan. CoRR, (2016)Ranking via Robust Binary Classification.H. Yun, P. Raman, and S. Vishwanathan. NIPS, page 2582-2590. (2014)Relevancy prediction of micro-blog questions in an educational setting.M. Cordova-Sanchez, P. Raman, L. Si, and J. Fish. EDM, page 415-416. International Educational Data Mining Society (IEDMS), (2014)MADA: Meta-Adaptive Optimizers Through Hyper-Gradient Descent.K. Ozkara, C. Karakus, P. Raman, M. Hong, S. Sabach, B. Kveton, and V. Cevher. ICML, OpenReview.net, (2024)EMC2: Efficient MCMC Negative Sampling for Contrastive Learning with Global Convergence.C. Yau, H. Wai, P. Raman, S. Sarkar, and M. Hong. ICML, OpenReview.net, (2024)Extreme Stochastic Variational Inference: Distributed Inference for Large Scale Mixture Models.J. Zhang, P. Raman, S. Ji, H. Yu, S. Vishwanathan, and I. Dhillon. AISTATS, volume 89 of Proceedings of Machine Learning Research, page 935-943. PMLR, (2019)

BibSonomy

Disambiguation of "Raman, Parameswaran"

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

Extreme Stochastic Variational Inference: Distributed Inference for Large Scale Mixture Models.

Please choose a person to relate this publication to

Rupa Parameswaran

Sreenath Ramanath

Anupama Parameswaran

Lalitha Parameswaran

Aravamudhan Raman

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Raman, Parameswaran"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Extreme Stochastic Variational Inference: Distributed Inference for Large Scale Mixture Models.

Please choose a person to relate this publication to

Rupa Parameswaran

Sreenath Ramanath

Anupama Parameswaran

Lalitha Parameswaran

Aravamudhan Raman

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

Extreme Stochastic Variational Inference: Distributed Inference for Large Scale Mixture Models.