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ORCIDNatesh S. Pillai
Person information
- affiliation: LinkedIn, USA
- affiliation: Harvard University, CA, USA
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2020 – today
- 2025
[c5]Kayhan Behdin, Ata Fatahi Baarzi, Qingquan Song, Yun Dai, Aman Gupta, Zhipeng Wang, Hejian Sang, Shao Tang, Gregory Dexter, Sirou Zhu, Siyu Zhu, Tejas Dharamsi, Vignesh Kothapalli, Zhoutong Fu, Yihan Cao, Pin-Lun Hsu, Fedor Borisyuk, Natesh S. Pillai, Luke Simon, Rahul Mazumder:
Scaling Down, Serving Fast: Compressing and Deploying Efficient LLMs for Recommendation Systems. EMNLP (Industry Track) 2025: 1687-1702- [c4]Aman Gupta, Shao Tang, Qingquan Song, Sirou Zhu, Jiwoo Hong, Ankan Saha, Viral Gupta, Noah Lee, Eunki Kim, Siyu Zhu, Parag Agrawal, Natesh S. Pillai, S. Sathiya Keerthi:
AlphaPO: Reward Shape Matters for LLM Alignment. ICML 2025 - [i11]Aman Gupta, Shao Tang, Qingquan Song, Sirou Zhu, Jiwoo Hong, Ankan Saha, Viral Gupta, Noah Lee, Eunki Kim, Jason Zhu, Natesh S. Pillai, S. Sathiya Keerthi:
AlphaPO - Reward shape matters for LLM alignment. CoRR abs/2501.03884 (2025) - [i10]Hamed Firooz, Maziar Sanjabi, Adrian Englhardt, Aman Gupta, Ben Levine, Dre Olgiati, Gungor Polatkan, Iuliia Melnychuk, Karthik Ramgopal, Kirill Talanine, Kutta Srinivasan, Luke Simon, Natesh Sivasubramoniapillai, Necip Fazil Ayan, Qingquan Song, Samira Sriram, Souvik Ghosh, Tao Song, Vignesh Kothapalli, Xiaoling Zhai, Ya Xu, Yu Wang, Yun Dai:
360Brew: A Decoder-only Foundation Model for Personalized Ranking and Recommendation. CoRR abs/2501.16450 (2025) - [i9]Kayhan Behdin, Yun Dai, Ata Fatahi Baarzi, Aman Gupta, Qingquan Song, Shao Tang, Hejian Sang, Gregory Dexter, Sirou Zhu, Siyu Zhu, Tejas Dharamsi, Maziar Sanjabi, Vignesh Kothapalli, Hamed Firooz, Zhoutong Fu, Yihan Cao, Pin-Lun Hsu, Fedor Borisyuk, Zhipeng Wang, Rahul Mazumder, Natesh S. Pillai, Luke Simon:
Efficient AI in Practice: Training and Deployment of Efficient LLMs for Industry Applications. CoRR abs/2502.14305 (2025) - [i8]Fengyi Li, Kayhan Behdin, Natesh S. Pillai, Xiaofeng Wang, Zhipeng Wang, Ercan Yildiz:
BP-Seg: A graphical model approach to unsupervised and non-contiguous text segmentation using belief propagation. CoRR abs/2505.16965 (2025) - 2024
- [j6]Natesh S. Pillai:
Optimal Scaling for the Proximal Langevin Algorithm in High Dimensions. J. Mach. Learn. Res. 25: 404:1-404:32 (2024) - [i7]Daniel Zhao, Natesh S. Pillai:
Policy Gradients for Optimal Parallel Tempering MCMC. CoRR abs/2409.01574 (2024) - [i6]Saikrishna Badrinarayanan, Osonde Osoba, Miao Cheng, Ryan Rogers, Sakshi Jain, Rahul Tandra, Natesh S. Pillai:
Privacy-Preserving Race/Ethnicity Estimation for Algorithmic Bias Measurement in the U.S. CoRR abs/2409.04652 (2024) - [i5]Brian Hsu, Cyrus DiCiccio, Natesh Sivasubramoniapillai, Hongseok Namkoong:
From Models to Systems: A Comprehensive Fairness Framework for Compositional Recommender Systems. CoRR abs/2412.04655 (2024) - 2023
- [c3]Tianle Liu, Promit Ghosal, Krishnakumar Balasubramanian, Natesh S. Pillai:
Towards Understanding the Dynamics of Gaussian-Stein Variational Gradient Descent. NeurIPS 2023 - [i4]Tianle Liu, Promit Ghosal, Krishnakumar Balasubramanian, Natesh S. Pillai:
Towards Understanding the Dynamics of Gaussian-Stein Variational Gradient Descent. CoRR abs/2305.14076 (2023) - 2022
- [j5]Andrew D. Davis
, Youssef M. Marzouk, Aaron Smith, Natesh S. Pillai:
Rate-optimal refinement strategies for local approximation MCMC. Stat. Comput. 32(4): 60 (2022) - [c2]Nhat Ho, Avi Feller, Evan Greif, Luke Miratrix, Natesh S. Pillai:
Weak Separation in Mixture Models and Implications for Principal Stratification. AISTATS 2022: 5416-5458 - 2021
- [j4]Oren Mangoubi, Natesh S. Pillai, Aaron Smith:
Simple conditions for metastability of continuous Markov chains. J. Appl. Probab. 58(1): 83-105 (2021) - 2020
- [i3]Vishesh Jain, Natesh S. Pillai, Aaron Smith:
Kac meets Johnson and Lindenstrauss: a memory-optimal, fast Johnson-Lindenstrauss transform. CoRR abs/2003.10069 (2020)
2010 – 2019
- 2018
- [j3]Patrick R. Conrad, Andrew D. Davis, Youssef M. Marzouk, Natesh S. Pillai, Aaron Smith:
Parallel Local Approximation MCMC for Expensive Models. SIAM/ASA J. Uncertain. Quantification 6(1): 339-373 (2018) - [i2]Oren Mangoubi, Natesh S. Pillai, Aaron Smith:
Does Hamiltonian Monte Carlo mix faster than a random walk on multimodal densities? CoRR abs/1808.03230 (2018) - 2017
- [j2]Luke Bornn, Natesh S. Pillai, Aaron Smith, Dawn Woodard:
The use of a single pseudo-sample in approximate Bayesian computation. Stat. Comput. 27(3): 583-590 (2017) - 2016
- [c1]Guillaume W. Basse, Aaron Smith, Natesh S. Pillai:
Parallel Markov Chain Monte Carlo via Spectral Clustering. AISTATS 2016: 1318-1327 - [i1]James E. Johndrow, Aaron Smith, Natesh S. Pillai, David B. Dunson:
Inefficiency of Data Augmentation for Large Sample Imbalanced Data. CoRR abs/1605.05798 (2016)
2000 – 2009
- 2007
- [j1]Natesh S. Pillai, Qiang Wu, Feng Liang, Sayan Mukherjee, Robert L. Wolpert:
Characterizing the Function Space for Bayesian Kernel Models. J. Mach. Learn. Res. 8: 1769-1797 (2007)
Coauthor Index
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