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ORCIDPreetum Nakkiran
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2020 – today
- 2024
[c26]Jaroslaw Blasiok, Preetum Nakkiran:
Smooth ECE: Principled Reliability Diagrams via Kernel Smoothing. ICLR 2024- [c25]Noam Razin, Hattie Zhou, Omid Saremi, Vimal Thilak, Arwen Bradley, Preetum Nakkiran, Joshua M. Susskind, Etai Littwin:
Vanishing Gradients in Reinforcement Finetuning of Language Models. ICLR 2024 - [c24]Vimal Thilak, Chen Huang, Omid Saremi, Laurent Dinh, Hanlin Goh, Preetum Nakkiran, Joshua M. Susskind, Etai Littwin:
LiDAR: Sensing Linear Probing Performance in Joint Embedding SSL Architectures. ICLR 2024 - [c23]Hattie Zhou, Arwen Bradley, Etai Littwin, Noam Razin, Omid Saremi, Joshua M. Susskind, Samy Bengio, Preetum Nakkiran:
What Algorithms can Transformers Learn? A Study in Length Generalization. ICLR 2024 - [c22]Jaroslaw Blasiok, Parikshit Gopalan, Lunjia Hu, Adam Tauman Kalai, Preetum Nakkiran:
Loss Minimization Yields Multicalibration for Large Neural Networks. ITCS 2024: 17:1-17:21 - [i40]Dutch Hansen, Siddartha Devic, Preetum Nakkiran, Vatsal Sharan:
When is Multicalibration Post-Processing Necessary? CoRR abs/2406.06487 (2024) - [i39]Preetum Nakkiran, Arwen Bradley, Hattie Zhou, Madhu Advani:
Step-by-Step Diffusion: An Elementary Tutorial. CoRR abs/2406.08929 (2024) - [i38]Etai Littwin, Omid Saremi, Madhu Advani, Vimal Thilak, Preetum Nakkiran, Chen Huang, Joshua M. Susskind:
How JEPA Avoids Noisy Features: The Implicit Bias of Deep Linear Self Distillation Networks. CoRR abs/2407.03475 (2024) - [i37]Arwen Bradley, Preetum Nakkiran:
Classifier-Free Guidance is a Predictor-Corrector. CoRR abs/2408.09000 (2024) - [i36]Xinting Huang, Andy Yang, Satwik Bhattamishra, Yash Sarrof, Andreas Krebs, Hattie Zhou, Preetum Nakkiran, Michael Hahn:
A Formal Framework for Understanding Length Generalization in Transformers. CoRR abs/2410.02140 (2024) - 2023
- [j3]Nikhil Vyas, Yamini Bansal, Preetum Nakkiran:
Empirical Limitations of the NTK for Understanding Scaling Laws in Deep Learning. Trans. Mach. Learn. Res. 2023 (2023) - [c21]Gal Kaplun, Nikhil Ghosh, Saurabh Garg, Boaz Barak, Preetum Nakkiran:
Deconstructing Distributions: A Pointwise Framework of Learning. ICLR 2023 - [c20]Jaroslaw Blasiok, Parikshit Gopalan, Lunjia Hu, Preetum Nakkiran:
When Does Optimizing a Proper Loss Yield Calibration? NeurIPS 2023 - [c19]Jaroslaw Blasiok
, Parikshit Gopalan, Lunjia Hu, Preetum Nakkiran:
A Unifying Theory of Distance from Calibration. STOC 2023: 1727-1740 - [i35]Jaroslaw Blasiok, Parikshit Gopalan, Lunjia Hu, Adam Tauman Kalai, Preetum Nakkiran:
Loss minimization yields multicalibration for large neural networks. CoRR abs/2304.09424 (2023) - [i34]Jaroslaw Blasiok, Parikshit Gopalan, Lunjia Hu, Preetum Nakkiran:
When Does Optimizing a Proper Loss Yield Calibration? CoRR abs/2305.18764 (2023) - [i33]Jaroslaw Blasiok, Preetum Nakkiran:
Smooth ECE: Principled Reliability Diagrams via Kernel Smoothing. CoRR abs/2309.12236 (2023) - [i32]Hattie Zhou, Arwen Bradley, Etai Littwin, Noam Razin, Omid Saremi, Josh M. Susskind, Samy Bengio, Preetum Nakkiran:
What Algorithms can Transformers Learn? A Study in Length Generalization. CoRR abs/2310.16028 (2023) - [i31]Noam Razin, Hattie Zhou, Omid Saremi, Vimal Thilak, Arwen Bradley, Preetum Nakkiran, Joshua M. Susskind, Etai Littwin:
Vanishing Gradients in Reinforcement Finetuning of Language Models. CoRR abs/2310.20703 (2023) - [i30]Vimal Thilak, Chen Huang, Omid Saremi, Laurent Dinh, Hanlin Goh, Preetum Nakkiran, Joshua M. Susskind, Etai Littwin:
LiDAR: Sensing Linear Probing Performance in Joint Embedding SSL Architectures. CoRR abs/2312.04000 (2023) - [i29]Micah Goldblum, Anima Anandkumar, Richard G. Baraniuk, Tom Goldstein, Kyunghyun Cho, Zachary C. Lipton, Melanie Mitchell, Preetum Nakkiran, Max Welling, Andrew Gordon Wilson:
Perspectives on the State and Future of Deep Learning - 2023. CoRR abs/2312.09323 (2023) - 2022
- [j2]Jaroslaw Blasiok, Venkatesan Guruswami, Preetum Nakkiran, Atri Rudra, Madhu Sudan:
General Strong Polarization. J. ACM 69(2): 11:1-11:67 (2022) - [j1]Pasin Manurangsi, Preetum Nakkiran, Luca Trevisan:
Near-Optimal NP-Hardness of Approximating Max k-CSPR. Adv. Math. Commun. 18: 1-29 (2022) - [c18]Gal Kaplun, Eran Malach, Preetum Nakkiran, Shai Shalev-Shwartz:
Knowledge Distillation: Bad Models Can Be Good Role Models. NeurIPS 2022 - [c17]Bogdan Kulynych, Yao-Yuan Yang, Yaodong Yu, Jaroslaw Blasiok, Preetum Nakkiran:
What You See is What You Get: Principled Deep Learning via Distributional Generalization. NeurIPS 2022 - [c16]Neil Mallinar, James B. Simon, Amirhesam Abedsoltan, Parthe Pandit, Misha Belkin, Preetum Nakkiran:
Benign, Tempered, or Catastrophic: Toward a Refined Taxonomy of Overfitting. NeurIPS 2022 - [i28]Like Hui, Mikhail Belkin, Preetum Nakkiran:
Limitations of Neural Collapse for Understanding Generalization in Deep Learning. CoRR abs/2202.08384 (2022) - [i27]Gal Kaplun, Nikhil Ghosh, Saurabh Garg, Boaz Barak, Preetum Nakkiran:
Deconstructing Distributions: A Pointwise Framework of Learning. CoRR abs/2202.09931 (2022) - [i26]Gal Kaplun, Eran Malach, Preetum Nakkiran, Shai Shalev-Shwartz:
Knowledge Distillation: Bad Models Can Be Good Role Models. CoRR abs/2203.14649 (2022) - [i25]Bogdan Kulynych, Yao-Yuan Yang, Yaodong Yu, Jaroslaw Blasiok, Preetum Nakkiran:
What You See is What You Get: Distributional Generalization for Algorithm Design in Deep Learning. CoRR abs/2204.03230 (2022) - [i24]Nikhil Vyas, Yamini Bansal, Preetum Nakkiran:
Limitations of the NTK for Understanding Generalization in Deep Learning. CoRR abs/2206.10012 (2022) - [i23]Neil Mallinar, James B. Simon, Amirhesam Abedsoltan, Parthe Pandit, Mikhail Belkin, Preetum Nakkiran:
Benign, Tempered, or Catastrophic: A Taxonomy of Overfitting. CoRR abs/2207.06569 (2022) - [i22]A. Michael Carrell, Neil Mallinar, James Lucas, Preetum Nakkiran:
The Calibration Generalization Gap. CoRR abs/2210.01964 (2022) - [i21]Elan Rosenfeld, Preetum Nakkiran, Hadi Pouransari, Oncel Tuzel, Fartash Faghri:
APE: Aligning Pretrained Encoders to Quickly Learn Aligned Multimodal Representations. CoRR abs/2210.03927 (2022) - [i20]Jaroslaw Blasiok, Parikshit Gopalan, Lunjia Hu, Preetum Nakkiran:
A Unifying Theory of Distance from Calibration. CoRR abs/2211.16886 (2022) - 2021
- [c15]Preetum Nakkiran, Behnam Neyshabur, Hanie Sedghi:
The Deep Bootstrap Framework: Good Online Learners are Good Offline Generalizers. ICLR 2021 - [c14]Preetum Nakkiran, Prayaag Venkat, Sham M. Kakade, Tengyu Ma:
Optimal Regularization can Mitigate Double Descent. ICLR 2021 - [c13]Yamini Bansal, Preetum Nakkiran, Boaz Barak:
Revisiting Model Stitching to Compare Neural Representations. NeurIPS 2021: 225-236 - [i19]Yamini Bansal, Preetum Nakkiran, Boaz Barak:
Revisiting Model Stitching to Compare Neural Representations. CoRR abs/2106.07682 (2021) - [i18]Preetum Nakkiran:
Turing-Universal Learners with Optimal Scaling Laws. CoRR abs/2111.05321 (2021) - 2020
- [c12]Preetum Nakkiran, Gal Kaplun, Yamini Bansal, Tristan Yang, Boaz Barak, Ilya Sutskever:
Deep Double Descent: Where Bigger Models and More Data Hurt. ICLR 2020 - [i17]Preetum Nakkiran, Prayaag Venkat, Sham M. Kakade, Tengyu Ma:
Optimal Regularization Can Mitigate Double Descent. CoRR abs/2003.01897 (2020) - [i16]Preetum Nakkiran:
Learning Rate Annealing Can Provably Help Generalization, Even for Convex Problems. CoRR abs/2005.07360 (2020) - [i15]Preetum Nakkiran, Yamini Bansal:
Distributional Generalization: A New Kind of Generalization. CoRR abs/2009.08092 (2020) - [i14]Preetum Nakkiran, Behnam Neyshabur, Hanie Sedghi:
The Deep Bootstrap: Good Online Learners are Good Offline Generalizers. CoRR abs/2010.08127 (2020)
2010 – 2019
- 2019
- [c11]Chi-Ning Chou, Zhixian Lei, Preetum Nakkiran:
Tracking the l2 Norm with Constant Update Time. APPROX-RANDOM 2019: 2:1-2:15 - [c10]Akshay Degwekar, Preetum Nakkiran, Vinod Vaikuntanathan:
Computational Limitations in Robust Classification and Win-Win Results. COLT 2019: 994-1028 - [c9]Venkatesan Guruswami, Preetum Nakkiran, Madhu Sudan:
Algorithmic Polarization for Hidden Markov Models. ITCS 2019: 39:1-39:19 - [c8]Preetum Nakkiran, Gal Kaplun, Dimitris Kalimeris, Tristan Yang, Benjamin L. Edelman, Fred Zhang, Boaz Barak:
SGD on Neural Networks Learns Functions of Increasing Complexity. NeurIPS 2019: 3491-3501 - [i13]Preetum Nakkiran:
Adversarial Robustness May Be at Odds With Simplicity. CoRR abs/1901.00532 (2019) - [i12]Preetum Nakkiran, Gal Kaplun, Dimitris Kalimeris, Tristan Yang, Benjamin L. Edelman, Fred Zhang, Boaz Barak:
SGD on Neural Networks Learns Functions of Increasing Complexity. CoRR abs/1905.11604 (2019) - [i11]Preetum Nakkiran, Gal Kaplun, Yamini Bansal, Tristan Yang, Boaz Barak, Ilya Sutskever:
Deep Double Descent: Where Bigger Models and More Data Hurt. CoRR abs/1912.02292 (2019) - [i10]Preetum Nakkiran:
More Data Can Hurt for Linear Regression: Sample-wise Double Descent. CoRR abs/1912.07242 (2019) - 2018
- [c7]Jaroslaw Blasiok, Venkatesan Guruswami, Preetum Nakkiran, Atri Rudra, Madhu Sudan:
General strong polarization. STOC 2018: 485-492 - [i9]Jaroslaw Blasiok, Venkatesan Guruswami, Preetum Nakkiran, Atri Rudra, Madhu Sudan:
General Strong Polarization. CoRR abs/1802.02718 (2018) - [i8]Chi-Ning Chou, Zhixian Lei, Preetum Nakkiran:
Tracking the 𝓁2 Norm with Constant Update Time. CoRR abs/1807.06479 (2018) - [i7]Preetum Nakkiran, Jaroslaw Blasiok:
The Generic Holdout: Preventing False-Discoveries in Adaptive Data Science. CoRR abs/1809.05596 (2018) - [i6]Venkatesan Guruswami, Preetum Nakkiran, Madhu Sudan:
Algorithmic Polarization for Hidden Markov Models. CoRR abs/1810.01969 (2018) - [i5]Jaroslaw Blasiok, Venkatesan Guruswami, Preetum Nakkiran, Atri Rudra, Madhu Sudan:
General Strong Polarization. Electron. Colloquium Comput. Complex. TR18 (2018) - 2017
- [i4]Charalampos E. Tsourakakis, Michael Mitzenmacher, Jaroslaw Blasiok, Ben Lawson, Preetum Nakkiran, Vasileios Nakos:
Predicting Positive and Negative Links with Noisy Queries: Theory & Practice. CoRR abs/1709.07308 (2017) - 2016
- [c6]Pasin Manurangsi, Preetum Nakkiran, Luca Trevisan:
Near-Optimal UGC-hardness of Approximating Max k-CSP_R. APPROX-RANDOM 2016: 15:1-15:28 - [c5]Preetum Nakkiran, K. V. Rashmi, Kannan Ramchandran:
Optimal systematic distributed storage codes with fast encoding. ISIT 2016: 430-434 - 2015
- [c4]K. V. Rashmi, Preetum Nakkiran, Jingyan Wang, Nihar B. Shah, Kannan Ramchandran:
Having Your Cake and Eating It Too: Jointly Optimal Erasure Codes for I/O, Storage, and Network-bandwidth. FAST 2015: 81-94 - [c3]Rohit Prabhavalkar, Raziel Alvarez, Carolina Parada, Preetum Nakkiran, Tara N. Sainath:
Automatic gain control and multi-style training for robust small-footprint keyword spotting with deep neural networks. ICASSP 2015: 4704-4708 - [c2]Preetum Nakkiran, Raziel Alvarez, Rohit Prabhavalkar, Carolina Parada:
Compressing deep neural networks using a rank-constrained topology. INTERSPEECH 2015: 1473-1477 - [i3]Preetum Nakkiran, K. V. Rashmi, Kannan Ramchandran:
Optimal Systematic Distributed Storage Codes with Fast Encoding. CoRR abs/1509.01858 (2015) - [i2]Pasin Manurangsi, Preetum Nakkiran, Luca Trevisan:
Near-Optimal UGC-hardness of Approximating Max k-CSP_R. CoRR abs/1511.06558 (2015) - 2014
- [c1]Preetum Nakkiran, Nihar B. Shah, K. V. Rashmi:
Fundamental limits on communication for oblivious updates in storage networks. GLOBECOM 2014: 2363-2368 - [i1]Preetum Nakkiran, Nihar B. Shah, K. V. Rashmi:
Fundamental Limits on Communication for Oblivious Updates in Storage Networks. CoRR abs/1409.1666 (2014)
Coauthor Index
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last updated on 2024-12-12 21:59 CET by the dblp team
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