Search Results for author: Purushottam Kar

Found 29 papers, 5 papers with code

IGLU: Efficient GCN Training via Lazy Updates

no code implementations28 Sep 2021 S Deepak Narayanan, Aditya Sinha, Prateek Jain, Purushottam Kar, Sundararajan Sellamanickam

This enables IGLU to perform lazy updates that do not require updating a large number of node embeddings during descent which offers much faster convergence but does not significantly bias the gradients.

DECAF: Deep Extreme Classification with Label Features

1 code implementation1 Aug 2021 Anshul Mittal, Kunal Dahiya, Sheshansh Agrawal, Deepak Saini, Sumeet Agarwal, Purushottam Kar, Manik Varma

This paper develops the DECAF algorithm that addresses these challenges by learning models enriched by label metadata that jointly learn model parameters and feature representations using deep networks and offer accurate classification at the scale of millions of labels.

Classification Extreme Multi-Label Classification +6

ECLARE: Extreme Classification with Label Graph Correlations

1 code implementation31 Jul 2021 Anshul Mittal, Noveen Sachdeva, Sheshansh Agrawal, Sumeet Agarwal, Purushottam Kar, Manik Varma

This paper presents ECLARE, a scalable deep learning architecture that incorporates not only label text, but also label correlations, to offer accurate real-time predictions within a few milliseconds.

Classification Extreme Multi-Label Classification +6

Globally-convergent Iteratively Reweighted Least Squares for Robust Regression Problems

no code implementations25 Jun 2020 Bhaskar Mukhoty, Govind Gopakumar, Prateek Jain, Purushottam Kar

We provide the first global model recovery results for the IRLS (iteratively reweighted least squares) heuristic for robust regression problems.

MACER: A Modular Framework for Accelerated Compilation Error Repair

1 code implementation28 May 2020 Darshak Chhatbar, Umair Z. Ahmed, Purushottam Kar

Automated compilation error repair, the problem of suggesting fixes to buggy programs that fail to compile, has generated significant interest in recent years.

Code Repair

Epidemiologically and Socio-economically Optimal Policies via Bayesian Optimization

1 code implementation22 May 2020 Amit Chandak, Debojyoti Dey, Bhaskar Mukhoty, Purushottam Kar

Mass public quarantining, colloquially known as a lock-down, is a non-pharmaceutical intervention to check spread of disease.

Accelerating Extreme Classification via Adaptive Feature Agglomeration

1 code implementation28 May 2019 Ankit Jalan, Purushottam Kar

Extreme classification seeks to assign each data point, the most relevant labels from a universe of a million or more labels.

Classification General Classification

DANTE: Deep AlterNations for Training nEural networks

no code implementations1 Feb 2019 Vaibhav B Sinha, Sneha Kudugunta, Adepu Ravi Sankar, Surya Teja Chavali, Purushottam Kar, Vineeth N. Balasubramanian

We present DANTE, a novel method for training neural networks using the alternating minimization principle.

Optimizing Non-decomposable Measures with Deep Networks

no code implementations31 Jan 2018 Amartya Sanyal, Pawan Kumar, Purushottam Kar, Sanjay Chawla, Fabrizio Sebastiani

We present a class of algorithms capable of directly training deep neural networks with respect to large families of task-specific performance measures such as the F-measure and the Kullback-Leibler divergence that are structured and non-decomposable.

Training Autoencoders by Alternating Minimization

no code implementations ICLR 2018 Sneha Kudugunta, Adepu Shankar, Surya Chavali, Vineeth Balasubramanian, Purushottam Kar

We present DANTE, a novel method for training neural networks, in particular autoencoders, using the alternating minimization principle.

Non-convex Optimization for Machine Learning

no code implementations21 Dec 2017 Prateek Jain, Purushottam Kar

The goal of this monograph is to both, introduce the rich literature in this area, as well as equip the reader with the tools and techniques needed to analyze these simple procedures for non-convex problems.

Consistent Robust Regression

no code implementations NeurIPS 2017 Kush Bhatia, Prateek Jain, Parameswaran Kamalaruban, Purushottam Kar

We present the first efficient and provably consistent estimator for the robust regression problem.

On Context-Dependent Clustering of Bandits

no code implementations ICML 2017 Claudio Gentile, Shuai Li, Purushottam Kar, Alexandros Karatzoglou, Evans Etrue, Giovanni Zappella

We investigate a novel cluster-of-bandit algorithm CAB for collaborative recommendation tasks that implements the underlying feedback sharing mechanism by estimating the neighborhood of users in a context-dependent manner.

Efficient and Consistent Robust Time Series Analysis

no code implementations1 Jul 2016 Kush Bhatia, Prateek Jain, Parameswaran Kamalaruban, Purushottam Kar

We illustrate our methods on synthetic datasets and show that our methods indeed are able to consistently recover the optimal parameters despite a large fraction of points being corrupted.

Time Series Time Series Analysis

Online Optimization Methods for the Quantification Problem

no code implementations13 May 2016 Purushottam Kar, Shuai Li, Harikrishna Narasimhan, Sanjay Chawla, Fabrizio Sebastiani

The estimation of class prevalence, i. e., the fraction of a population that belongs to a certain class, is a very useful tool in data analytics and learning, and finds applications in many domains such as sentiment analysis, epidemiology, etc.

Epidemiology Sentiment Analysis

Sparse Local Embeddings for Extreme Multi-label Classification

no code implementations NeurIPS 2015 Kush Bhatia, Himanshu Jain, Purushottam Kar, Manik Varma, Prateek Jain

The objective in extreme multi-label learning is to train a classifier that can automatically tag a novel data point with the most relevant subset of labels from an extremely large label set.

Classification Extreme Multi-Label Classification +3

Context-Aware Bandits

no code implementations12 Oct 2015 Shuai Li, Purushottam Kar

We propose an efficient Context-Aware clustering of Bandits (CAB) algorithm, which can capture collaborative effects.

Multi-Armed Bandits

Locally Non-linear Embeddings for Extreme Multi-label Learning

no code implementations9 Jul 2015 Kush Bhatia, Himanshu Jain, Purushottam Kar, Prateek Jain, Manik Varma

Embedding based approaches make training and prediction tractable by assuming that the training label matrix is low-rank and hence the effective number of labels can be reduced by projecting the high dimensional label vectors onto a low dimensional linear subspace.

Extreme Multi-Label Classification General Classification +2

Robust Regression via Hard Thresholding

no code implementations NeurIPS 2015 Kush Bhatia, Prateek Jain, Purushottam Kar

In this work, we study a simple hard-thresholding algorithm called TORRENT which, under mild conditions on X, can recover w* exactly even if b corrupts the response variables in an adversarial manner, i. e. both the support and entries of b are selected adversarially after observing X and w*.

Optimizing Non-decomposable Performance Measures: A Tale of Two Classes

no code implementations26 May 2015 Harikrishna Narasimhan, Purushottam Kar, Prateek Jain

Modern classification problems frequently present mild to severe label imbalance as well as specific requirements on classification characteristics, and require optimizing performance measures that are non-decomposable over the dataset, such as F-measure.

General Classification

Surrogate Functions for Maximizing Precision at the Top

no code implementations26 May 2015 Purushottam Kar, Harikrishna Narasimhan, Prateek Jain

At the heart of our results is a family of truly upper bounding surrogates for prec@k. These surrogates are motivated in a principled manner and enjoy attractive properties such as consistency to prec@k under various natural margin/noise conditions.

Multi-Label Classification

Online and Stochastic Gradient Methods for Non-decomposable Loss Functions

no code implementations NeurIPS 2014 Purushottam Kar, Harikrishna Narasimhan, Prateek Jain

In this work we initiate a study of online learning techniques for such non-decomposable loss functions with an aim to enable incremental learning as well as design scalable solvers for batch problems.

Incremental Learning

On Iterative Hard Thresholding Methods for High-dimensional M-Estimation

no code implementations NeurIPS 2014 Prateek Jain, Ambuj Tewari, Purushottam Kar

Our results rely on a general analysis framework that enables us to analyze several popular hard thresholding style algorithms (such as HTP, CoSaMP, SP) in the high dimensional regression setting.

On the Generalization Ability of Online Learning Algorithms for Pairwise Loss Functions

no code implementations11 May 2013 Purushottam Kar, Bharath K. Sriperumbudur, Prateek Jain, Harish C Karnick

We are also able to analyze a class of memory efficient online learning algorithms for pairwise learning problems that use only a bounded subset of past training samples to update the hypothesis at each step.

Generalization Bounds Metric Learning

Supervised Learning with Similarity Functions

no code implementations NeurIPS 2012 Purushottam Kar, Prateek Jain

a given supervised learning task and then adapt a well-known landmarking technique to provide efficient algorithms for supervised learning using ''good'' similarity functions.

General Classification

Similarity-based Learning via Data Driven Embeddings

no code implementations NeurIPS 2011 Purushottam Kar, Prateek Jain

We propose a landmarking-based approach to obtaining a classifier from such learned goodness criteria.

Random Projection Trees Revisited

no code implementations NeurIPS 2010 Aman Dhesi, Purushottam Kar

The Random Projection Tree (RPTree) structures proposed in [Dasgupta-Freund-STOC-08] are space partitioning data structures that automatically adapt to various notions of intrinsic dimensionality of data.

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