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ORCIDNikhil Ghosh
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2020 – today
- 2024
[c7]James B. Simon, Dhruva Karkada, Nikhil Ghosh, Mikhail Belkin:
More is Better: when Infinite Overparameterization is Optimal and Overfitting is Obligatory. ICLR 2024- [c6]Soufiane Hayou, Nikhil Ghosh, Bin Yu:
LoRA+: Efficient Low Rank Adaptation of Large Models. ICML 2024 - [i10]Soufiane Hayou, Nikhil Ghosh, Bin Yu:
LoRA+: Efficient Low Rank Adaptation of Large Models. CoRR abs/2402.12354 (2024) - [i9]Soufiane Hayou, Nikhil Ghosh, Bin Yu:
The Impact of Initialization on LoRA Finetuning Dynamics. CoRR abs/2406.08447 (2024) - 2023
- [j1]Nikhil Ghosh, Mikhail Belkin:
A Universal Trade-off Between the Model Size, Test Loss, and Training Loss of Linear Predictors. SIAM J. Math. Data Sci. 5(4): 977-1004 (2023) - [c5]Nishanth Dikkala, Nikhil Ghosh, Raghu Meka, Rina Panigrahy, Nikhil Vyas, Xin Wang:
On the Benefits of Learning to Route in Mixture-of-Experts Models. EMNLP 2023: 9376-9396 - [c4]Gal Kaplun, Nikhil Ghosh, Saurabh Garg, Boaz Barak, Preetum Nakkiran:
Deconstructing Distributions: A Pointwise Framework of Learning. ICLR 2023 - [c3]Cenk Baykal, Dylan J. Cutler, Nishanth Dikkala, Nikhil Ghosh, Rina Panigrahy, Xin Wang:
Alternating Updates for Efficient Transformers. NeurIPS 2023 - [i8]Cenk Baykal, Dylan J. Cutler, Nishanth Dikkala, Nikhil Ghosh, Rina Panigrahy, Xin Wang:
Alternating Updates for Efficient Transformers. CoRR abs/2301.13310 (2023) - [i7]Cenk Baykal, Dylan J. Cutler, Nishanth Dikkala, Nikhil Ghosh, Rina Panigrahy, Xin Wang:
The Power of External Memory in Increasing Predictive Model Capacity. CoRR abs/2302.00003 (2023) - [i6]Nikhil Ghosh, Spencer Frei, Wooseok Ha, Bin Yu:
The Effect of SGD Batch Size on Autoencoder Learning: Sparsity, Sharpness, and Feature Learning. CoRR abs/2308.03215 (2023) - [i5]James B. Simon, Dhruva Karkada, Nikhil Ghosh, Mikhail Belkin:
More is Better in Modern Machine Learning: when Infinite Overparameterization is Optimal and Overfitting is Obligatory. CoRR abs/2311.14646 (2023) - 2022
- [c2]Nikhil Ghosh, Song Mei, Bin Yu:
The Three Stages of Learning Dynamics in High-dimensional Kernel Methods. ICLR 2022 - [i4]Gal Kaplun, Nikhil Ghosh, Saurabh Garg, Boaz Barak, Preetum Nakkiran:
Deconstructing Distributions: A Pointwise Framework of Learning. CoRR abs/2202.09931 (2022) - [i3]Nikhil Ghosh, Mikhail Belkin:
A Universal Trade-off Between the Model Size, Test Loss, and Training Loss of Linear Predictors. CoRR abs/2207.11621 (2022) - 2021
- [i2]Nikhil Ghosh, Song Mei, Bin Yu:
The Three Stages of Learning Dynamics in High-Dimensional Kernel Methods. CoRR abs/2111.07167 (2021)
2010 – 2019
- 2019
- [c1]Nikhil Ghosh, Yuxin Chen, Yisong Yue:
Landmark Ordinal Embedding. NeurIPS 2019: 11502-11511 - [i1]Nikhil Ghosh, Yuxin Chen, Yisong Yue:
Landmark Ordinal Embedding. CoRR abs/1910.12379 (2019)
Coauthor Index
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