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Parameswaran Raman
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2020 – today
- 2024
- [c8]Ruichen Jiang, Parameswaran Raman, Shoham Sabach, Aryan Mokhtari, Mingyi Hong, Volkan Cevher:
Krylov Cubic Regularized Newton: A Subspace Second-Order Method with Dimension-Free Convergence Rate. AISTATS 2024: 4411-4419 - [c7]Tanmay Gautam, Youngsuk Park, Hao Zhou, Parameswaran Raman, Wooseok Ha:
Variance-reduced Zeroth-Order Methods for Fine-Tuning Language Models. ICML 2024 - [c6]Kaan Ozkara, Can Karakus, Parameswaran Raman, Mingyi Hong, Shoham Sabach, Branislav Kveton, Volkan Cevher:
MADA: Meta-Adaptive Optimizers Through Hyper-Gradient Descent. ICML 2024 - [c5]Chung-Yiu Yau, Hoi-To Wai, Parameswaran Raman, Soumajyoti Sarkar, Mingyi Hong:
EMC2: Efficient MCMC Negative Sampling for Contrastive Learning with Global Convergence. ICML 2024 - [i10]Ruichen Jiang, Parameswaran Raman, Shoham Sabach, Aryan Mokhtari, Mingyi Hong, Volkan Cevher:
Krylov Cubic Regularized Newton: A Subspace Second-Order Method with Dimension-Free Convergence Rate. CoRR abs/2401.03058 (2024) - [i9]Kaan Ozkara, Can Karakus, Parameswaran Raman, Mingyi Hong, Shoham Sabach, Branislav Kveton, Volkan Cevher:
MADA: Meta-Adaptive Optimizers through hyper-gradient Descent. CoRR abs/2401.08893 (2024) - [i8]Tanmay Gautam, Youngsuk Park, Hao Zhou, Parameswaran Raman, Wooseok Ha:
Variance-reduced Zeroth-Order Methods for Fine-Tuning Language Models. CoRR abs/2404.08080 (2024) - [i7]Chung-Yiu Yau, Hoi-To Wai, Parameswaran Raman, Soumajyoti Sarkar, Mingyi Hong:
EMC2: Efficient MCMC Negative Sampling for Contrastive Learning with Global Convergence. CoRR abs/2404.10575 (2024) - [i6]Haozheng Fan, Hao Zhou, Guangtai Huang, Parameswaran Raman, Xinwei Fu, Gaurav Gupta, Dhananjay Ram, Yida Wang, Jun Huan:
HLAT: High-quality Large Language Model Pre-trained on AWS Trainium. CoRR abs/2404.10630 (2024) - 2023
- [i5]Bingcong Li, Shuai Zheng, Parameswaran Raman, Anshumali Shrivastava, Georgios B. Giannakis:
Contractive error feedback for gradient compression. CoRR abs/2312.08538 (2023) - 2020
- [i4]Parameswaran Raman, S. V. N. Vishwanathan:
DS-FACTO: Doubly Separable Factorization Machines. CoRR abs/2004.13940 (2020)
2010 – 2019
- 2019
- [c4]Jiong Zhang, Parameswaran Raman, Shihao Ji, Hsiang-Fu Yu, S. V. N. Vishwanathan, Inderjit S. Dhillon:
Extreme Stochastic Variational Inference: Distributed Inference for Large Scale Mixture Models. AISTATS 2019: 935-943 - [c3]Parameswaran Raman, Sriram Srinivasan, Shin Matsushima, Xinhua Zhang, Hyokun Yun, S. V. N. Vishwanathan:
Scaling Multinomial Logistic Regression via Hybrid Parallelism. KDD 2019: 1460-1470 - [i3]Parameswaran Raman, Jiasen Yang:
Optimization on the Surface of the (Hyper)-Sphere. CoRR abs/1909.06463 (2019) - 2016
- [i2]Parameswaran Raman, Shin Matsushima, Xinhua Zhang, Hyokun Yun, S. V. N. Vishwanathan:
DS-MLR: Exploiting Double Separability for Scaling up Distributed Multinomial Logistic Regression. CoRR abs/1604.04706 (2016) - 2014
- [c2]Mariheida Cordova-Sanchez, Parameswaran Raman, Luo Si, Jason Fish:
Relevancy prediction of micro-blog questions in an educational setting. EDM 2014: 415-416 - [c1]Hyokun Yun, Parameswaran Raman, S. V. N. Vishwanathan:
Ranking via Robust Binary Classification. NIPS 2014: 2582-2590 - [i1]Hyokun Yun, Parameswaran Raman, S. V. N. Vishwanathan:
Ranking via Robust Binary Classification and Parallel Parameter Estimation in Large-Scale Data. CoRR abs/1402.2676 (2014)
Coauthor Index
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