no code implementations • 11 Dec 2022 • Bhaskar P Mukhoty, Debojyoti Dey, Purushottam Kar
This paper presents SVAM (Sequential Variance-Altered MLE), a unified framework for learning generalized linear models under adversarial label corruption in training data.
no code implementations • 28 Oct 2022 • Naishadh Parmar, Raunak Shah, Tushar Goswamy, Vatsalya Tandon, Ravi Sahu, Ronak Sutaria, Purushottam Kar, Sachchida Nand Tripathi
The identification and control of human factors in climate change is a rapidly growing concern and robust, real-time air-quality monitoring and forecasting plays a critical role in allowing effective policy formulation and implementation.
no code implementations • 10 Jul 2022 • Kunal Dahiya, Nilesh Gupta, Deepak Saini, Akshay Soni, Yajun Wang, Kushal Dave, Jian Jiao, Gururaj K, Prasenjit Dey, Amit Singh, Deepesh Hada, Vidit Jain, Bhawna Paliwal, Anshul Mittal, Sonu Mehta, Ramachandran Ramjee, Sumeet Agarwal, Purushottam Kar, Manik Varma
This paper identifies that memory overheads of popular negative mining techniques often force mini-batch sizes to remain small and slow training down.
no code implementations • CVPR 2022 • Anshul Mittal, Kunal Dahiya, Shreya Malani, Janani Ramaswamy, Seba Kuruvilla, Jitendra Ajmera, Keng-hao Chang, Sumeet Agarwal, Purushottam Kar, Manik Varma
On the other hand, XC methods utilize classifier architectures to offer superior accuracies than embedding-only methods but mostly focus on text-based categorization tasks.
1 code implementation • 6 Nov 2021 • Debojyoti Dey, Bhaskar Mukhoty, Purushottam Kar
This paper presents AGGLIO (Accelerated Graduated Generalized LInear-model Optimization), a stage-wise, graduated optimization technique that offers global convergence guarantees for non-convex optimization problems whose objectives offer only local convexity and may fail to be even quasi-convex at a global scale.
1 code implementation • ICLR 2022 • S Deepak Narayanan, Aditya Sinha, Prateek Jain, Purushottam Kar, Sundararajan Sellamanickam
Training multi-layer Graph Convolution Networks (GCN) using standard SGD techniques scales poorly as each descent step ends up updating node embeddings for a large portion of the graph.
1 code implementation • 1 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.
1 code implementation • 31 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.
no code implementations • 25 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.
1 code implementation • 28 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.
1 code implementation • 22 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.
1 code implementation • 28 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.
no code implementations • 1 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.
no code implementations • 17 Feb 2018 • Dheeraj Mekala, Vivek Gupta, Purushottam Kar, Harish Karnick
We extend the consistency of hierarchical classification algorithm over asymmetric tree distance loss.
no code implementations • 31 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.
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.
no code implementations • 21 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.
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.
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.
no code implementations • 1 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.
no code implementations • 13 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.
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.
no code implementations • 12 Oct 2015 • Shuai Li, Purushottam Kar
We propose an efficient Context-Aware clustering of Bandits (CAB) algorithm, which can capture collaborative effects.
no code implementations • 9 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
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*.
no code implementations • 26 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.
no code implementations • 26 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.
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.
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.
no code implementations • 18 Jul 2013 • Hsiang-Fu Yu, Prateek Jain, Purushottam Kar, Inderjit S. Dhillon
The multi-label classification problem has generated significant interest in recent years.
no code implementations • 11 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.
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.
no code implementations • NeurIPS 2011 • Purushottam Kar, Prateek Jain
We propose a landmarking-based approach to obtaining a classifier from such learned goodness criteria.
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.