Natesh S. Pillai

According to our database, Natesh S. Pillai authored at least 19 papers between 2007 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

BP-Seg: A graphical model approach to unsupervised and non-contiguous text segmentation using belief propagation.

Fengyi Li, Kayhan Behdin, Natesh S. Pillai, Xiaofeng Wang, Zhipeng Wang, Ercan Yildiz

CoRR, May, 2025

Efficient AI in Practice: Training and Deployment of Efficient LLMs for Industry Applications.

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

CoRR, February, 2025

360Brew: A Decoder-only Foundation Model for Personalized Ranking and Recommendation.

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

CoRR, January, 2025

AlphaPO - Reward shape matters for LLM alignment.

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

CoRR, January, 2025

AlphaPO: Reward Shape Matters for LLM Alignment.

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

Proceedings of the Forty-second International Conference on Machine Learning, 2025

2024

From Models to Systems: A Comprehensive Fairness Framework for Compositional Recommender Systems.

Brian Hsu, Cyrus DiCiccio, Natesh Sivasubramoniapillai, Hongseok Namkoong

CoRR, 2024

Privacy-Preserving Race/Ethnicity Estimation for Algorithmic Bias Measurement in the U.S.

Saikrishna Badrinarayanan, Osonde Osoba, Miao Cheng, Ryan Rogers, Sakshi Jain, Rahul Tandra, Natesh S. Pillai

CoRR, 2024

Policy Gradients for Optimal Parallel Tempering MCMC.

Daniel Zhao, Natesh S. Pillai

CoRR, 2024

2023

Towards Understanding the Dynamics of Gaussian-Stein Variational Gradient Descent.

Tianle Liu, Promit Ghosal, Krishnakumar Balasubramanian, Natesh S. Pillai

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Rate-optimal refinement strategies for local approximation MCMC.

Andrew D. Davis, Youssef M. Marzouk, Aaron Smith, Natesh S. Pillai

Stat. Comput., 2022

Weak Separation in Mixture Models and Implications for Principal Stratification.

Nhat Ho, Avi Feller, Evan Greif, Luke Miratrix, Natesh S. Pillai

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Simple conditions for metastability of continuous Markov chains.

Oren Mangoubi, Natesh S. Pillai, Aaron Smith

J. Appl. Probab., 2021

2020

Kac meets Johnson and Lindenstrauss: a memory-optimal, fast Johnson-Lindenstrauss transform.

Vishesh Jain, Natesh S. Pillai, Aaron Smith

CoRR, 2020

2018

Parallel Local Approximation MCMC for Expensive Models.

Patrick R. Conrad, Andrew D. Davis, Youssef M. Marzouk, Natesh S. Pillai, Aaron Smith

SIAM/ASA J. Uncertain. Quantification, 2018

Does Hamiltonian Monte Carlo mix faster than a random walk on multimodal densities?

Oren Mangoubi, Natesh S. Pillai, Aaron Smith

CoRR, 2018

2017

The use of a single pseudo-sample in approximate Bayesian computation.

Luke Bornn, Natesh S. Pillai, Aaron Smith, Dawn Woodard

Stat. Comput., 2017

2016

Inefficiency of Data Augmentation for Large Sample Imbalanced Data.

James E. Johndrow, Aaron Smith, Natesh S. Pillai, David B. Dunson

CoRR, 2016

Parallel Markov Chain Monte Carlo via Spectral Clustering.

Guillaume W. Basse, Aaron Smith, Natesh S. Pillai

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2007

Characterizing the Function Space for Bayesian Kernel Models.

Natesh S. Pillai, Qiang Wu, Feng Liang, Sayan Mukherjee, Robert L. Wolpert

J. Mach. Learn. Res., 2007