Natesh S. Pillai

21 papers A* 2A 2Journal 15Unranked 2

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	AlphaPO - Reward shape matters for LLM alignment. CoRR 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
2025	A*	`conf`	AlphaPO: Reward Shape Matters for LLM Alignment. ICML 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
2025	J	`jnl`	BP-Seg: A graphical model approach to unsupervised and non-contiguous text segmentation using belief propagation. CoRR Fengyi Li, Kayhan Behdin, Natesh S. Pillai, Xiaofeng Wang, Zhipeng Wang, Ercan Yildiz
2025	J	`jnl`	Efficient AI in Practice: Training and Deployment of Efficient LLMs for Industry Applications. CoRR 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
2025	—	`conf`	Scaling Down, Serving Fast: Compressing and Deploying Efficient LLMs for Recommendation Systems. EMNLP (Industry Track) 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
2024	—	`conf`	From Models to Systems: A Comprehensive Framework for AI System Fairness in Compositional Recommender Systems. AFME Brian Hsu, Cyrus DiCiccio, Natesh S. Pillai, Hongseok Namkoong
2024	J	`jnl`	Optimal Scaling for the Proximal Langevin Algorithm in High Dimensions. J. Mach. Learn. Res. Natesh S. Pillai
2024	J	`jnl`	Policy Gradients for Optimal Parallel Tempering MCMC. CoRR Daniel Zhao, Natesh S. Pillai
2024	J	`jnl`	Privacy-Preserving Race/Ethnicity Estimation for Algorithmic Bias Measurement in the U.S. CoRR Saikrishna Badrinarayanan, Osonde Osoba, Miao Cheng, Ryan Rogers, Sakshi Jain, Rahul Tandra, Natesh S. Pillai
2023	A*	`conf`	Towards Understanding the Dynamics of Gaussian-Stein Variational Gradient Descent. NeurIPS Tianle Liu, Promit Ghosal, Krishnakumar Balasubramanian, Natesh S. Pillai
2023	J	`jnl`	Towards Understanding the Dynamics of Gaussian-Stein Variational Gradient Descent. CoRR Tianle Liu, Promit Ghosal, Krishnakumar Balasubramanian, Natesh S. Pillai
2022	J	`jnl`	Rate-optimal refinement strategies for local approximation MCMC. Stat. Comput. Andrew D. Davis, Youssef M. Marzouk, Aaron Smith, Natesh S. Pillai
2022	A	`conf`	Weak Separation in Mixture Models and Implications for Principal Stratification. AISTATS Nhat Ho, Avi Feller, Evan Greif, Luke Miratrix, Natesh S. Pillai
2021	J	`jnl`	Simple conditions for metastability of continuous Markov chains. J. Appl. Probab. Oren Mangoubi, Natesh S. Pillai, Aaron Smith
2020	J	`jnl`	Kac meets Johnson and Lindenstrauss: a memory-optimal, fast Johnson-Lindenstrauss transform. CoRR Vishesh Jain, Natesh S. Pillai, Aaron Smith
2018	J	`jnl`	Does Hamiltonian Monte Carlo mix faster than a random walk on multimodal densities? CoRR Oren Mangoubi, Natesh S. Pillai, Aaron Smith
2018	J	`jnl`	Parallel Local Approximation MCMC for Expensive Models. SIAM/ASA J. Uncertain. Quantification Patrick R. Conrad, Andrew D. Davis, Youssef M. Marzouk, Natesh S. Pillai, Aaron Smith
2017	J	`jnl`	The use of a single pseudo-sample in approximate Bayesian computation. Stat. Comput. Luke Bornn, Natesh S. Pillai, Aaron Smith, Dawn Woodard
2016	J	`jnl`	Inefficiency of Data Augmentation for Large Sample Imbalanced Data. CoRR James E. Johndrow, Aaron Smith, Natesh S. Pillai, David B. Dunson
2016	A	`conf`	Parallel Markov Chain Monte Carlo via Spectral Clustering. AISTATS Guillaume W. Basse, Aaron Smith, Natesh S. Pillai
2007	J	`jnl`	Characterizing the Function Space for Bayesian Kernel Models. J. Mach. Learn. Res. Natesh S. Pillai, Qiang Wu, Feng Liang, Sayan Mukherjee, Robert L. Wolpert