Nathan srebro phd thesis

Nathan srebro phd thesis

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Maximum Margin Matrix Factorization

Teacher's pet: understanding and mitigating biases in distillation Michal Lukasik, Srinadh Bhojanapalli, nathan srebro phd thesis, Aditya Krishna Menon, Sanjiv Kumar Preprint [Arxiv].

On the Reproducibility of Neural Network Predictions Srinadh Nathan srebro phd thesis, Kimberly Wilber, Andreas Veit, Ankit Singh Rawat, Seungyeon Kim, Aditya Menon, Sanjiv Kumar Preprint [Arxiv].

Modifying Memories in Transformer Models Chen Zhu, Ankit Singh Rawat, Manzil Zaheer, Srinadh Bhojanapalli, Daliang Li, Felix Yu, Sanjiv Kumar Preprint [Arxiv]. Coping with Label Shift via Distributionally Robust Optimisation Jingzhao Zhang, Aditya Menon, Andreas Veit, Srinadh Bhojanapalli, Sanjiv Kumar, Suvrit Sra ICLR [Openreview][Arxiv].

An efficient nonconvex reformulation of stagewise convex optimization problems Rudy Bunel, Oliver Hinder, Srinadh Bhojanapalli, Krishnamurthy Dj Dvijotham Neurips [Arxiv]. Semantic label smoothing for sequence to sequence problems Michal Lukasik, Himanshu Jain, Aditya Menon, Seungyeon Kim, Srinadh Bhojanapalli, Felix Yu and Sanjiv Kumar EMNLP [Arxiv]. O n Connections are Expressive Enough: Universal Approximability of Sparse Transformers Chulhee Yun, Yin-Wen Chang, Srinadh Bhojanapalli, Ankit Singh Rawat, Sashank J.

Reddi, Sanjiv Kumar Neurips [Arxiv]. Does label smoothing mitigate label noise? Michal Lukasik, Srinadh Bhojanapalli, Aditya Krishna Menon, Sanjiv Kumar ICML [Arxiv]. Low-Rank Bottleneck in Multi-head Attention Models Srinadh Bhojanapalli, Chulhee Yun, Ankit Singh Rawat, Sashank J. Reddi, Sanjiv Kumar ICML [Arxiv].

Are Transformers universal approximators of sequence-to-sequence functions? Chulhee Yun, Srinadh Bhojanapalli, Ankit Singh Rawat, Sashank Reddi, Sanjiv Kumar ICLR [Openreview]. Large Batch Optimization for Deep Learning: Training BERT in 76 minutes Yang You, Jing Li, Sashank Reddi, nathan srebro phd thesis, Jonathan Hseu, Sanjiv Kumar, Srinadh Bhojanapalli, Xiaodan Song, James Demmel, Kurt Keutzer, Cho-Jui Hsieh ICLR [Openreview][Arxiv].

The role of over-parametrization in generalization of neural networks Behnam Neyshabur, Nathan srebro phd thesis Li, Srinadh Bhojanapalli, Yann LeCun, Nathan Srebro ICLR [Openreview][Arxiv]. A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks Behnam Neyshabur, Srinadh Bhojanapalli, Nathan Srebro ICLR [Arxiv]. Exploring Generalization in Deep Learning Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, Nathan Srebro NIPS [Arxiv].

Implicit Regularization in Matrix Factorization Suriya Gunasekar, Blake Woodworth, Srinadh Bhojanapalli, Behnam Neyshabur, Nathan Srebro NIPS Stabilizing GAN Training with Multiple Random Projections Behnam Neyshabur, Srinadh Bhojanapalli, Ayan Chakrabarti preprint[Arxiv][Project website]. Single Pass PCA of Matrix Products Shanshan Wu, Srinadh Bhojanapalli, Sujay Sanghavi, Alex Dimakis NIPS [Arxiv][SPARK code].

Global Optimality of Local Search for Low Rank Matrix Recovery Srinadh Bhojanapalli, Behnam Neyshabur, Nathan Srebro NIPS [Arxiv]. Provable Burer-Monteiro factorization for a class of norm-constrained nathan srebro phd thesis problems Dohyung Park, Anastasios Kyrillidis, Srinadh Bhojanapalli, Constantine Caramanis, Sujay Sanghavi preprint[Arxiv].

Dropping Convexity for Faster Semi-definite Optimization Srinadh Bhojanapalli, Anastasios Kyrillidis, Sujay Sanghavi COLT [Arxiv][COLT][slides]. A New Sampling Technique for Tensors Srinadh Bhojanapalli, Sujay Sanghavi preprint[Arxiv][slides]. Tighter Low-rank Approximation via Sampling the Leveraged Element Srinadh Bhojanapalli, Prateek Jain, Sujay Sanghavi SODA [Arxiv]nathan srebro phd thesis, [SODA][slides].

Completing any Low-rank Matrix, Provably Yudong Chen, Srinadh Bhojanapalli, Sujay Sanghavi, Rachel Ward JMLR [Arxiv]nathan srebro phd thesis, [JMLR]. Universal Matrix Completion Srinadh Bhojanapalli, Prateek Jain ICML [Arxiv][ICML][slides][video].

Coherent Matrix Completion Yudong Chen, Srinadh Bhojanapalli, Sujay Sanghavi, Rachel Ward ICML [Arxiv][ICML][slides][video]. Large Scale Matrix Factorization with Guarantees: Sampling and Bi-linearity [pdf] UT Austin, Info Home Research Publications. Implicit Regularization in Matrix Factorization Suriya Gunasekar, Blake Woodworth, Srinadh Bhojanapalli, Behnam Neyshabur, Nathan Srebro NIPS Stabilizing GAN Training with Multiple Random Projections Behnam Neyshabur, Srinadh Bhojanapalli, Ayan Chakrabarti preprint[Arxiv][Project website].

PhD Thesis Large Scale Matrix Factorization with Guarantees: Sampling and Bi-linearity [pdf] UT Austin,

Optimization's Untold Gift to Learning: Implicit Regularization

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Nathan srebro thesis - blogger.com

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