Fundamental limits on communication for oblivious updates in storage networks. - Preetum Nakkiran; Nihar B. Shah; K. V. Rashmi (GLOBECOM, 2014)

Having Your Cake and Eating It Too: Jointly Optimal Erasure Codes for I/O, Storage, and Network-bandwidth. - K. V. Rashmi; Preetum Nakkiran; Jingyan Wang 001; Nihar B. Shah; Kannan Ramchandran (FAST, 2015)

Automatic gain control and multi-style training for robust small-footprint keyword spotting with deep neural networks. - Rohit Prabhavalkar; Raziel Alvarez; Carolina Parada; Preetum Nakkiran; Tara N. Sainath (ICASSP, 2015)

Compressing deep neural networks using a rank-constrained topology. - Preetum Nakkiran; Raziel Alvarez; Rohit Prabhavalkar; Carolina Parada (INTERSPEECH, 2015)

Near-Optimal UGC-hardness of Approximating Max k-CSP_R. - Pasin Manurangsi; Preetum Nakkiran; Luca Trevisan 001 (APPROX-RANDOM, 2016)

Optimal systematic distributed storage codes with fast encoding. - Preetum Nakkiran; K. V. Rashmi; Kannan Ramchandran (ISIT, 2016)

Predicting Positive and Negative Links with Noisy Queries: Theory & Practice. - Charalampos E. Tsourakakis; Michael Mitzenmacher; Jaroslaw Blasiok; Ben Lawson; Preetum Nakkiran; Vasileios Nakos (CoRR, 2017)

The Generic Holdout: Preventing False-Discoveries in Adaptive Data Science. - Preetum Nakkiran; Jaroslaw Blasiok (CoRR, 2018)

General Strong Polarization. - Jaroslaw Blasiok; Venkatesan Guruswami; Preetum Nakkiran; Atri Rudra; Madhu Sudan 001 (Electron. Colloquium Comput. Complex., 2018)

Tracking the l2 Norm with Constant Update Time. - Chi-Ning Chou; Zhixian Lei; Preetum Nakkiran (APPROX-RANDOM, 2019)

Computational Limitations in Robust Classification and Win-Win Results. - Akshay Degwekar; Preetum Nakkiran; Vinod Vaikuntanathan (COLT, 2019)

Algorithmic Polarization for Hidden Markov Models. - Venkatesan Guruswami; Preetum Nakkiran; Madhu Sudan 001 (ITCS, 2019)

SGD on Neural Networks Learns Functions of Increasing Complexity. - Preetum Nakkiran; Gal Kaplun; Dimitris Kalimeris; Tristan Yang; Benjamin L. Edelman; Fred Zhang; Boaz Barak (NeurIPS, 2019)

Adversarial Robustness May Be at Odds With Simplicity. - Preetum Nakkiran (CoRR, 2019)

More Data Can Hurt for Linear Regression: Sample-wise Double Descent. - Preetum Nakkiran (CoRR, 2019)

Deep Double Descent: Where Bigger Models and More Data Hurt. - Preetum Nakkiran; Gal Kaplun; Yamini Bansal; Tristan Yang; Boaz Barak; Ilya Sutskever (ICLR, 2020)

Learning Rate Annealing Can Provably Help Generalization, Even for Convex Problems. - Preetum Nakkiran (CoRR, 2020)

Distributional Generalization: A New Kind of Generalization. - Preetum Nakkiran; Yamini Bansal (CoRR, 2020)

The Deep Bootstrap: Good Online Learners are Good Offline Generalizers. - Preetum Nakkiran; Behnam Neyshabur; Hanie Sedghi (CoRR, 2020)

The Deep Bootstrap Framework: Good Online Learners are Good Offline Generalizers. - Preetum Nakkiran; Behnam Neyshabur; Hanie Sedghi (ICLR, 2021)

Optimal Regularization can Mitigate Double Descent. - Preetum Nakkiran; Prayaag Venkat; Sham M. Kakade; Tengyu Ma 001 (ICLR, 2021)

Revisiting Model Stitching to Compare Neural Representations. - Yamini Bansal; Preetum Nakkiran; Boaz Barak (NeurIPS, 2021)

Turing-Universal Learners with Optimal Scaling Laws. - Preetum Nakkiran (CoRR, 2021)

Knowledge Distillation: Bad Models Can Be Good Role Models. - Gal Kaplun; Eran Malach; Preetum Nakkiran; Shai Shalev-Shwartz (NeurIPS, 2022)

What You See is What You Get: Principled Deep Learning via Distributional Generalization. - Bogdan Kulynych; Yao-Yuan Yang; Yaodong Yu; Jaroslaw Blasiok; Preetum Nakkiran (NeurIPS, 2022)

Benign, Tempered, or Catastrophic: Toward a Refined Taxonomy of Overfitting. - Neil Mallinar; James B. Simon; Amirhesam Abedsoltan; Parthe Pandit; Mikhail Belkin; Preetum Nakkiran (NeurIPS, 2022)

Limitations of Neural Collapse for Understanding Generalization in Deep Learning. - Like Hui; Mikhail Belkin; Preetum Nakkiran (CoRR, 2022)

What You See is What You Get: Distributional Generalization for Algorithm Design in Deep Learning. - Bogdan Kulynych; Yao-Yuan Yang; Yaodong Yu; Jaroslaw Blasiok; Preetum Nakkiran (CoRR, 2022)

Limitations of the NTK for Understanding Generalization in Deep Learning. - Nikhil Vyas 001; Yamini Bansal; Preetum Nakkiran (CoRR, 2022)

Benign, Tempered, or Catastrophic: A Taxonomy of Overfitting. - Neil Mallinar; James B. Simon; Amirhesam Abedsoltan; Parthe Pandit; Mikhail Belkin; Preetum Nakkiran (CoRR, 2022)

The Calibration Generalization Gap. - A. Michael Carrell; Neil Mallinar; James Lucas; Preetum Nakkiran (CoRR, 2022)

APE: Aligning Pretrained Encoders to Quickly Learn Aligned Multimodal Representations. - Elan Rosenfeld; Preetum Nakkiran; Hadi Pouransari; Oncel Tuzel; Fartash Faghri (CoRR, 2022)

Near-Optimal NP-Hardness of Approximating Max k-CSPR. - Pasin Manurangsi; Preetum Nakkiran; Luca Trevisan 001 (Adv. Math. Commun., 2022)

Deconstructing Distributions: A Pointwise Framework of Learning. - Gal Kaplun; Nikhil Ghosh; Saurabh Garg; Boaz Barak; Preetum Nakkiran (ICLR, 2023)

When Does Optimizing a Proper Loss Yield Calibration? - Jaroslaw Blasiok; Parikshit Gopalan; Lunjia Hu; Preetum Nakkiran (NeurIPS, 2023)

A Unifying Theory of Distance from Calibration. - Jaroslaw Blasiok; Parikshit Gopalan; Lunjia Hu; Preetum Nakkiran (STOC, 2023)

Perspectives on the State and Future of Deep Learning - 2023. - Micah Goldblum; Anima Anandkumar; Richard G. Baraniuk; Tom Goldstein; Kyunghyun Cho; Zachary C. Lipton; Melanie Mitchell; Preetum Nakkiran; Max Welling; Andrew Gordon Wilson (CoRR, 2023)

Empirical Limitations of the NTK for Understanding Scaling Laws in Deep Learning. - Nikhil Vyas 001; Yamini Bansal; Preetum Nakkiran (Trans. Mach. Learn. Res., 2023)

Smooth ECE: Principled Reliability Diagrams via Kernel Smoothing. - Jaroslaw Blasiok; Preetum Nakkiran (ICLR, 2024)

Vanishing Gradients in Reinforcement Finetuning of Language Models. - Noam Razin; Hattie Zhou; Omid Saremi; Vimal Thilak; Arwen Bradley; Preetum Nakkiran; Joshua M. Susskind; Etai Littwin (ICLR, 2024)

LiDAR: Sensing Linear Probing Performance in Joint Embedding SSL Architectures. - Vimal Thilak; Chen Huang 001; Omid Saremi; Laurent Dinh; Hanlin Goh; Preetum Nakkiran; Joshua M. Susskind; Etai Littwin (ICLR, 2024)

What Algorithms can Transformers Learn? A Study in Length Generalization. - Hattie Zhou; Arwen Bradley; Etai Littwin; Noam Razin; Omid Saremi; Joshua M. Susskind; Samy Bengio; Preetum Nakkiran (ICLR, 2024)

Loss Minimization Yields Multicalibration for Large Neural Networks. - Jaroslaw Blasiok; Parikshit Gopalan; Lunjia Hu; Adam Tauman Kalai; Preetum Nakkiran (ITCS, 2024)

When is Multicalibration Post-Processing Necessary? - Dutch Hansen; Siddartha Devic; Preetum Nakkiran; Vatsal Sharan (NeurIPS, 2024)

How JEPA Avoids Noisy Features: The Implicit Bias of Deep Linear Self Distillation Networks. - Etai Littwin; Omid Saremi; Madhu Advani; Vimal Thilak; Preetum Nakkiran; Chen Huang 001; Joshua M. Susskind (NeurIPS, 2024)

Normalizing Flows are Capable Generative Models. - Shuangfei Zhai; Ruixiang Zhang; Preetum Nakkiran; David Berthelot; Jiatao Gu; Huangjie Zheng; Tianrong Chen; Miguel Ángel Bautista 001; Navdeep Jaitly; Joshua M. Susskind (CoRR, 2024)

Composition and Control with Distilled Energy Diffusion Models and Sequential Monte Carlo. - James Thornton; Louis Béthune; Ruixiang Zhang; Arwen Bradley; Preetum Nakkiran; Shuangfei Zhai (AISTATS, 2025)

A Formal Framework for Understanding Length Generalization in Transformers. - Xinting Huang; Andy Yang; Satwik Bhattamishra; Yash Raj Sarrof; Andreas Krebs; Hattie Zhou; Preetum Nakkiran; Michael Hahn 001 (ICLR, 2025)

Step-by-Step Diffusion: An Elementary Tutorial. - Preetum Nakkiran; Arwen Bradley; Hattie Zhou; Madhu Advani (Found. Trends Comput. Graph. Vis., 2025)

Classifier-Free Guidance is a Predictor-Corrector. - Arwen Bradley; Preetum Nakkiran (Trans. Mach. Learn. Res., 2025)

Mechanisms of Projective Composition of Diffusion Models. - Arwen Bradley; Preetum Nakkiran; David Berthelot; James Thornton; Joshua M. Susskind (CoRR, 2025)