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Hanie Sedghi

Leveraging Unlabeled Data to Predict Out-of-Distribution Performance


Jan 11, 2022
Saurabh Garg, Sivaraman Balakrishnan, Zachary C. Lipton, Behnam Neyshabur, Hanie Sedghi

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The Role of Permutation Invariance in Linear Mode Connectivity of Neural Networks


Oct 12, 2021
Rahim Entezari, Hanie Sedghi, Olga Saukh, Behnam Neyshabur

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Exploring the Limits of Large Scale Pre-training


Oct 05, 2021
Samira Abnar, Mostafa Dehghani, Behnam Neyshabur, Hanie Sedghi

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Gradual Domain Adaptation in the Wild:When Intermediate Distributions are Absent


Jun 10, 2021
Samira Abnar, Rianne van den Berg, Golnaz Ghiasi, Mostafa Dehghani, Nal Kalchbrenner, Hanie Sedghi

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The Deep Bootstrap: Good Online Learners are Good Offline Generalizers


Oct 16, 2020
Preetum Nakkiran, Behnam Neyshabur, Hanie Sedghi

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What is being transferred in transfer learning?


Aug 26, 2020
Behnam Neyshabur, Hanie Sedghi, Chiyuan Zhang

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* Equal contribution, authors ordered randomly 
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The intriguing role of module criticality in the generalization of deep networks


Dec 04, 2019
Niladri S. Chatterji, Behnam Neyshabur, Hanie Sedghi

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Size-free generalization bounds for convolutional neural networks


Jun 12, 2019
Philip M. Long, Hanie Sedghi

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SysML: The New Frontier of Machine Learning Systems


May 01, 2019
Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Jennifer Chayes, Eric Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros Dimakis, Pradeep Dubey, Charles Elkan, Grigori Fursin, Gregory R. Ganger, Lise Getoor, Phillip B. Gibbons, Garth A. Gibson, Joseph E. Gonzalez, Justin Gottschlich, Song Han, Kim Hazelwood, Furong Huang, Martin Jaggi, Kevin Jamieson, Michael I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konečný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Aparna Lakshmiratan, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Murray, Kunle Olukotun, Dimitris Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar


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On the effect of the activation function on the distribution of hidden nodes in a deep network


Jan 07, 2019
Philip M. Long, Hanie Sedghi

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The Singular Values of Convolutional Layers


May 26, 2018
Hanie Sedghi, Vineet Gupta, Philip M. Long

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Knowledge Completion for Generics using Guided Tensor Factorization


Mar 28, 2018
Hanie Sedghi, Ashish Sabharwal

* To appear in TACL 
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Training Input-Output Recurrent Neural Networks through Spectral Methods


Oct 31, 2016
Hanie Sedghi, Anima Anandkumar

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Provable Tensor Methods for Learning Mixtures of Generalized Linear Models


Jan 13, 2016
Hanie Sedghi, Majid Janzamin, Anima Anandkumar

* To appear in Proceeding of AI and Statistics (AISTATS) 2016 
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Beating the Perils of Non-Convexity: Guaranteed Training of Neural Networks using Tensor Methods


Jan 12, 2016
Majid Janzamin, Hanie Sedghi, Anima Anandkumar

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* The tensor decomposition analysis is expanded, and the analysis of ridge regression is added for recovering the parameters of last layer of neural network 
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Multi-Step Stochastic ADMM in High Dimensions: Applications to Sparse Optimization and Noisy Matrix Decomposition


Jul 07, 2015
Hanie Sedghi, Anima Anandkumar, Edmond Jonckheere

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* appeared in Neural Information Processing Systems(NIPS) 2014. arXiv admin note: text overlap with arXiv:1207.4421 by other authors 
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Provable Methods for Training Neural Networks with Sparse Connectivity


Apr 28, 2015
Hanie Sedghi, Anima Anandkumar

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* Accepted for presentation at Neural Information Processing Systems(NIPS) 2014 Deep Learning workshop and Accepted as a workshop contribution at ICLR 2015 
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Learning Mixed Membership Community Models in Social Tagging Networks through Tensor Methods


Apr 22, 2015
Anima Anandkumar, Hanie Sedghi

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Score Function Features for Discriminative Learning


Apr 19, 2015
Majid Janzamin, Hanie Sedghi, Anima Anandkumar

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* Accepted as a workshop contribution at ICLR 2015. A longer version of this work is also available on arXiv: http://arxiv.org/abs/1412.2863 
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Score Function Features for Discriminative Learning: Matrix and Tensor Framework


Dec 11, 2014
Majid Janzamin, Hanie Sedghi, Anima Anandkumar

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* 29 pages 
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Statistical Structure Learning, Towards a Robust Smart Grid


Mar 07, 2014
Hanie Sedghi, Edmond Jonckheere

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