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no code implementations • ICLR 2022 • Saurabh Garg, Sivaraman Balakrishnan, Zachary C. Lipton, Behnam Neyshabur, Hanie Sedghi

Real-world machine learning deployments are characterized by mismatches between the source (training) and target (test) distributions that may cause performance drops.

2 code implementations • ICLR 2022 • Rahim Entezari, Hanie Sedghi, Olga Saukh, Behnam Neyshabur

In this paper, we conjecture that if the permutation invariance of neural networks is taken into account, SGD solutions will likely have no barrier in the linear interpolation between them.

no code implementations • ICLR 2022 • Samira Abnar, Mostafa Dehghani, Behnam Neyshabur, Hanie Sedghi

Recent developments in large-scale machine learning suggest that by scaling up data, model size and training time properly, one might observe that improvements in pre-training would transfer favorably to most downstream tasks.

no code implementations • 29 Sep 2021 • Samira Abnar, Rianne van den Berg, Golnaz Ghiasi, Mostafa Dehghani, Nal Kalchbrenner, Hanie Sedghi

It is shown that under the following two assumptions: (a) access to samples from intermediate distributions, and (b) samples being annotated with the amount of change from the source distribution; self-training can be successfully applied on gradually shifted samples to adapt the model toward the target distribution.

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

It has been shown that under the following two assumptions: (a) access to samples from intermediate distributions, and (b) samples being annotated with the amount of change from the source distribution, self-training can be successfully applied on gradually shifted samples to adapt the model toward the target distribution.

no code implementations • ICLR 2021 • Preetum Nakkiran, Behnam Neyshabur, Hanie Sedghi

We propose a new framework for reasoning about generalization in deep learning.

2 code implementations • 16 Oct 2020 • Preetum Nakkiran, Behnam Neyshabur, Hanie Sedghi

We propose a new framework for reasoning about generalization in deep learning.

1 code implementation • NeurIPS 2020 • Behnam Neyshabur, Hanie Sedghi, Chiyuan Zhang

One desired capability for machines is the ability to transfer their knowledge of one domain to another where data is (usually) scarce.

no code implementations • ICLR 2020 • Niladri S. Chatterji, Behnam Neyshabur, Hanie Sedghi

We study the phenomenon that some modules of deep neural networks (DNNs) are more critical than others.

no code implementations • ICLR 2020 • Philip M. Long, Hanie Sedghi

We prove bounds on the generalization error of convolutional networks.

no code implementations • 29 Mar 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

Machine learning (ML) techniques are enjoying rapidly increasing adoption.

no code implementations • 7 Jan 2019 • Philip M. Long, Hanie Sedghi

We analyze the joint probability distribution on the lengths of the vectors of hidden variables in different layers of a fully connected deep network, when the weights and biases are chosen randomly according to Gaussian distributions, and the input is in $\{ -1, 1\}^N$.

1 code implementation • ICLR 2019 • Hanie Sedghi, Vineet Gupta, Philip M. Long

We characterize the singular values of the linear transformation associated with a standard 2D multi-channel convolutional layer, enabling their efficient computation.

no code implementations • TACL 2018 • Hanie Sedghi, Ashish Sabharwal

Given a knowledge base or KB containing (noisy) facts about common nouns or generics, such as "all trees produce oxygen" or "some animals live in forests", we consider the problem of inferring additional such facts at a precision similar to that of the starting KB.

no code implementations • 3 Mar 2016 • Hanie Sedghi, Anima Anandkumar

We consider the problem of training input-output recurrent neural networks (RNN) for sequence labeling tasks.

no code implementations • 28 Jun 2015 • Majid Janzamin, Hanie Sedghi, Anima Anandkumar

We propose a novel algorithm based on tensor decomposition for guaranteed training of two-layer neural networks.

no code implementations • 16 Mar 2015 • Anima Anandkumar, Hanie Sedghi

Community detection in graphs has been extensively studied both in theory and in applications.

no code implementations • 19 Dec 2014 • Majid Janzamin, Hanie Sedghi, Anima Anandkumar

In this paper, we consider a novel class of matrix and tensor-valued features, which can be pre-trained using unlabeled samples.

no code implementations • 9 Dec 2014 • Hanie Sedghi, Majid Janzamin, Anima Anandkumar

In contrast, we present a tensor decomposition method which is guaranteed to correctly recover the parameters.

no code implementations • 9 Dec 2014 • Majid Janzamin, Hanie Sedghi, Anima Anandkumar

In this paper, we consider a novel class of matrix and tensor-valued features, which can be pre-trained using unlabeled samples.

no code implementations • 8 Dec 2014 • Hanie Sedghi, Anima Anandkumar

We provide novel guaranteed approaches for training feedforward neural networks with sparse connectivity.

no code implementations • NeurIPS 2014 • Hanie Sedghi, Anima Anandkumar, Edmond Jonckheere

We first analyze the simple setting, where the optimization problem consists of a loss function and a single regularizer (e. g. sparse optimization), and then extend to the multi-block setting with multiple regularizers and multiple variables (e. g. matrix decomposition into sparse and low rank components).

no code implementations • 7 Mar 2014 • Hanie Sedghi, Edmond Jonckheere

We propose a decentralized false data injection detection scheme based on Markov graph of the bus phase angles.

2 code implementations • NeurIPS 2014 • Hanie Sedghi, Anima Anandkumar, Edmond Jonckheere

For sparse optimization, we establish that the modified ADMM method has an optimal convergence rate of $\mathcal{O}(s\log d/T)$, where $s$ is the sparsity level, $d$ is the data dimension and $T$ is the number of steps.

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