# Posts by Collection

## Montreal, Canada during NeurIPS 2018

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Visited Montreal, Canada with Microsoft Research Labmates to attend and present at NeurIPS 2018 Read more

## Melbourne, Australia during WSDM 2019

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Visited Melbourne, Australia to attend and present at WSDM 2019 Read more

## Vancouver, Canada during NeurIPS 2019

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Visited Vancouver, Canada to attend NeurIPS 2019 and present at SEDL 2019 Read more

## Mini Search Engine

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We used data structures like Hash Tables, Balanced Trees in order to design a text search engine that gives the frequency of the searched word in a given folder of files. Read more

## Modelling Economic Policy Uncertainty Index using Text Classification

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Using Soft Margin Kernel Support Vector Machine to classify newspaper articles to model an Economic Policy Uncertainty Index for India. Read more

## Some Approaches of Building Recommendation Systems

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The project aims at using different recommendation methods for different kinds of real world data like rating matrices, images and text, using Deep Learning and Optimization. Read more

## A case study of Empirical Bayes in Recommendation system

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We provide a formulation of empirical bayes described by Atchadé (2011) to tune the hyperparameters of priors used in Bayesian set up of collaborative filter. Read more

## Clustered Monotone Transforms for Rating Factorization

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We propose Clustered Monotone Transforms for Rating Factorization (CMTRF), a novel approach to perform regression up to unknown monotonic transforms over unknown population segments. For recommendation systems, the technique searches for monotonic transformations of the rating scales resulting in a better fit. This is combined with an underlying matrix factorization regression model that couples the user-wise ratings to exploit shared low dimensional structure. The rating scale transformations can be generated for each user (N-CMTRF), for a cluster of users (CMTRF), or for all the users at once (1-CMTRF), forming the basis of three simple and efficient algorithms proposed, all of which alternate between transformation of the rating scales and matrix factorization regression. Despite the non-convexity, CMTRF is theoretically shown to recover a unique solution under mild conditions. Read more

## Sparse Regression and Support Recovery bounds for Orthogonal Matching Pursuit

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We study the problem of sparse regression where the goal is to learn a sparse vector that best optimizes a given objective function. Under the assumption that the objective function satisfies restricted strong convexity (RSC), we analyze Orthogonal Matching Pursuit (OMP) and obtain support recovery result as well as a tight generalization error bound for OMP. Furthermore, we obtain lower bounds for OMP, showing that both our results on support recovery and generalization error are tight up to logarithmic factors. To the best of our knowledge, these support recovery and generalization bounds are the first such matching upper and lower bounds (up to logarithmic factors) for any sparse regression algorithm under the RSC assumption. Read more

## Universality Patterns in the Training of Neural Networks

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This work proposes and demonstrates a surprising pattern in the training of neural networks: there is a one to one relation between the values of any pair of losses (such as cross entropy, mean squared error, $0/1$ error etc.) evaluated for a model arising at (any point of) a training run. This pattern is universal in the sense that this one to one relationship is identical across architectures (such as VGG, Resnet, Densenet etc.), algorithms (SGD and SGD with momentum) and training loss functions (cross entropy and mean squared error). Read more

## Connections between Stochasticity of SGD and Generalizability

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This is an attempt to understand how stochasticity in an optimization algorithm affect generalization properties of a Neural Network. Read more

## Clustered Monotone Transforms for Rating Factorization

Raghav Somani*, Gaurush Hiranandani*, Sanmi Koyejo & Sreangsu Acharyya
Published at: Web Search and Data Mining (WSDM), 2019

The paper has been accepted for an oral persentation (84/511 submissions ≈ 16% Acceptance Rate). Read more

[paper] [arXiv] [bib] [code]

## Support Recovery for Orthogonal Matching Pursuit: Upper and Lower bounds

Raghav Somani*, Chirag Gupta*, Prateek Jain & Praneeth Netrapalli
Published at: Neural Information Processing Systems (NeurIPS), 2018

The paper has been accepted for Spotlight presentation (168/4856 submissions ≈ 3.5% Acceptance Rate). Read more

[paper] [bib]

## Non-Gaussianity of Stochastic Gradient Noise

Abhishek Panigrahi, Raghav Somani, Navin Goyal & Praneeth Netrapalli
Published at: Science meets Engineering of Deep Learning (SEDL) workshop, Neural Information Processing Systems (NeurIPS), 2019

We study the distribution of the Stochastic Gradient Noise during the training and observe that for batch sizes $256$ and above, the distribution is best described as Gaussian at-least in the early phases of training. Read more

[arXiv] [bib]

## Soft Threshold Weight Reparameterization for Learnable Sparsity

Published at: International Conference on Machine Learning (ICML), 2020

The paper has been accepted for a presentation. Read more

[paper] [arXiv] [bib] [code]

## Meta-learning for Mixed Linear Regression

Weihao Kong, Raghav Somani, Zhao Song, Sham Kakade, Sewoong Oh
Published at: International Conference on Machine Learning (ICML), 2020

The paper has been accepted for a presentation. Read more

[paper] [arXiv] [bib] [code]

## Robust Meta-learning for Mixed Linear Regression with Small Batches

Weihao Kong, Raghav Somani, Sham Kakade, Sewoong Oh
Published at: Neural Information Processing Systems (NeurIPS), 2020

The paper has been accepted for a poster. Read more

[paper] [arXiv] [bib] [code]