Nikhil Ghosh - RankMe

23 papers A* 8Journal 15

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	GSM-Agent: Understanding Agentic Reasoning Using Controllable Environments. CoRR Hanlin Zhu, Tianyu Guo, Song Mei, Stuart Russell, Nikhil Ghosh, Alberto Bietti, Jiantao Jiao
2025	J	`jnl`	PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models. CoRR Soufiane Hayou, Nikhil Ghosh, Bin Yu
2025	J	`jnl`	The Effect of SGD Batch Size on Autoencoder Learning: Sparsity, Sharpness, and Feature Learning. J. Mach. Learn. Res. Nikhil Ghosh, Spencer Frei, Wooseok Ha, Bin Yu
2025	J	`jnl`	Understanding the Mechanisms of Fast Hyperparameter Transfer. CoRR Nikhil Ghosh, Denny Wu, Alberto Bietti
2024	A*	`conf`	LoRA+: Efficient Low Rank Adaptation of Large Models. ICML Soufiane Hayou, Nikhil Ghosh, Bin Yu
2024	J	`jnl`	LoRA+: Efficient Low Rank Adaptation of Large Models. CoRR Soufiane Hayou, Nikhil Ghosh, Bin Yu
2024	A*	`conf`	More is Better: when Infinite Overparameterization is Optimal and Overfitting is Obligatory. ICLR James B. Simon, Dhruva Karkada, Nikhil Ghosh, Mikhail Belkin
2024	A*	`conf`	The Impact of Initialization on LoRA Finetuning Dynamics. NeurIPS Soufiane Hayou, Nikhil Ghosh, Bin Yu
2024	J	`jnl`	The Impact of Initialization on LoRA Finetuning Dynamics. CoRR Soufiane Hayou, Nikhil Ghosh, Bin Yu
2023	J	`jnl`	A Universal Trade-off Between the Model Size, Test Loss, and Training Loss of Linear Predictors. SIAM J. Math. Data Sci. Nikhil Ghosh, Mikhail Belkin
2023	A*	`conf`	Alternating Updates for Efficient Transformers. NeurIPS Cenk Baykal, Dylan J. Cutler, Nishanth Dikkala, Nikhil Ghosh, Rina Panigrahy, Xin Wang
2023	J	`jnl`	Alternating Updates for Efficient Transformers. CoRR Cenk Baykal, Dylan J. Cutler, Nishanth Dikkala, Nikhil Ghosh, Rina Panigrahy, Xin Wang
2023	A*	`conf`	Deconstructing Distributions: A Pointwise Framework of Learning. ICLR Gal Kaplun, Nikhil Ghosh, Saurabh Garg, Boaz Barak, Preetum Nakkiran
2023	J	`jnl`	More is Better in Modern Machine Learning: when Infinite Overparameterization is Optimal and Overfitting is Obligatory. CoRR James B. Simon, Dhruva Karkada, Nikhil Ghosh, Mikhail Belkin
2023	A*	`conf`	On the Benefits of Learning to Route in Mixture-of-Experts Models. EMNLP Nishanth Dikkala, Nikhil Ghosh, Raghu Meka, Rina Panigrahy, Nikhil Vyas, Xin Wang
2023	J	`jnl`	The Effect of SGD Batch Size on Autoencoder Learning: Sparsity, Sharpness, and Feature Learning. CoRR Nikhil Ghosh, Spencer Frei, Wooseok Ha, Bin Yu
2023	J	`jnl`	The Power of External Memory in Increasing Predictive Model Capacity. CoRR Cenk Baykal, Dylan J. Cutler, Nishanth Dikkala, Nikhil Ghosh, Rina Panigrahy, Xin Wang
2022	J	`jnl`	A Universal Trade-off Between the Model Size, Test Loss, and Training Loss of Linear Predictors. CoRR Nikhil Ghosh, Mikhail Belkin
2022	J	`jnl`	Deconstructing Distributions: A Pointwise Framework of Learning. CoRR Gal Kaplun, Nikhil Ghosh, Saurabh Garg, Boaz Barak, Preetum Nakkiran
2022	A*	`conf`	The Three Stages of Learning Dynamics in High-dimensional Kernel Methods. ICLR Nikhil Ghosh, Song Mei, Bin Yu
2021	J	`jnl`	The Three Stages of Learning Dynamics in High-Dimensional Kernel Methods. CoRR Nikhil Ghosh, Song Mei, Bin Yu
2019	A*	`conf`	Landmark Ordinal Embedding. NeurIPS Nikhil Ghosh, Yuxin Chen, Yisong Yue
2019	J	`jnl`	Landmark Ordinal Embedding. CoRR Nikhil Ghosh, Yuxin Chen, Yisong Yue