Karthikeyan Natesan Ramamurthy

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Research Staff Member at IBM Thomas J Watson Research Center

Finding the Homology of Decision Boundaries with Active Learning

Accurately and efficiently characterizing the decision boundary of class...
11/19/2020 ∙ by Weizhi Li, et al. ∙ 7 ∙ share

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Characterizing the Latent Space of Molecular Deep Generative Models with Persistent Homology Metrics

Deep generative models are increasingly becoming integral parts of the i...
10/18/2020 ∙ by Yair Schiff, et al. ∙ 1 ∙ share

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Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness

Mode connectivity provides novel geometric insights on analyzing loss la...
04/30/2020 ∙ by Pu Zhao, et al. ∙ 7 ∙ share

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Model Agnostic Multilevel Explanations

In recent years, post-hoc local instance-level and global dataset-level ...
03/12/2020 ∙ by Karthikeyan Natesan Ramamurthy, et al. ∙ 0 ∙ share

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Drug Repurposing for Cancer: An NLP Approach to Identify Low-Cost Therapies

More than 200 generic drugs approved by the U.S. Food and Drug Administr...
11/18/2019 ∙ by Shivashankar Subramanian, et al. ∙ 30 ∙ share

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Understanding racial bias in health using the Medical Expenditure Panel Survey data

Over the years, several studies have demonstrated that there exist signi...
11/04/2019 ∙ by Moninder Singh, et al. ∙ 0 ∙ share

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Teaching AI to Explain its Decisions Using Embeddings and Multi-Task Learning

Using machine learning in high-stakes applications often requires predic...
06/05/2019 ∙ by Noel C. F. Codella, et al. ∙ 4 ∙ share

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PI-Net: A Deep Learning Approach to Extract Topological Persistence Images

Topological features such as persistence diagrams and their functional a...
06/05/2019 ∙ by Anirudh Som, et al. ∙ 0 ∙ share

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Optimized Score Transformation for Fair Classification

This paper considers fair probabilistic classification where the outputs...
05/31/2019 ∙ by Dennis Wei, et al. ∙ 0 ∙ share

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Counting and Segmenting Sorghum Heads

Phenotyping is the process of measuring an organism's observable traits....
05/30/2019 ∙ by Min-hwan Oh, et al. ∙ 0 ∙ share

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Crowd Counting with Decomposed Uncertainty

Research in neural networks in the field of computer vision has achieved...
03/15/2019 ∙ by Min-hwan Oh, et al. ∙ 0 ∙ share

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Bias Mitigation Post-processing for Individual and Group Fairness

Whereas previous post-processing approaches for increasing the fairness ...
12/14/2018 ∙ by Pranay K. Lohia, et al. ∙ 0 ∙ share

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TED: Teaching AI to Explain its Decisions

Artificial intelligence systems are being increasingly deployed due to t...
11/12/2018 ∙ by Noel C. F. Codella, et al. ∙ 12 ∙ share

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AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias

Fairness is an increasingly important concern as machine learning models...
10/03/2018 ∙ by Rachel K. E. Bellamy, et al. ∙ 12 ∙ share

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Increasing Trust in AI Services through Supplier's Declarations of Conformity

The accuracy and reliability of machine learning algorithms are an impor...
08/22/2018 ∙ by Michael Hind, et al. ∙ 0 ∙ share

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Perturbation Robust Representations of Topological Persistence Diagrams

Topological methods for data analysis present opportunities for enforcin...
07/26/2018 ∙ by Anirudh Som, et al. ∙ 0 ∙ share

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Teaching Meaningful Explanations

The adoption of machine learning in high-stakes applications such as hea...
05/29/2018 ∙ by Noel C. F. Codella, et al. ∙ 0 ∙ share

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Topological Data Analysis of Decision Boundaries with Application to Model Selection

We propose the labeled Čech complex, the plain labeled Vietoris-Rips com...
05/25/2018 ∙ by Karthikeyan Natesan Ramamurthy, et al. ∙ 0 ∙ share

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Simultaneous Parameter Learning and Bi-Clustering for Multi-Response Models

We consider multi-response and multitask regression models, where the pa...
04/29/2018 ∙ by Ming Yu, et al. ∙ 0 ∙ share

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Exploring High-Dimensional Structure via Axis-Aligned Decomposition of Linear Projections

Two-dimensional embeddings remain the dominant approach to visualize hig...
12/19/2017 ∙ by Jayaraman J. Thiagarajan, et al. ∙ 0 ∙ share

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Distribution-Preserving k-Anonymity

Preserving the privacy of individuals by protecting their sensitive attr...
11/05/2017 ∙ by Dennis Wei, et al. ∙ 0 ∙ share

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An End-To-End Machine Learning Pipeline That Ensures Fairness Policies

In consequential real-world applications, machine learning (ML) based sy...
10/18/2017 ∙ by Samiulla Shaikh, et al. ∙ 0 ∙ share

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Multitask Learning using Task Clustering with Applications to Predictive Modeling and GWAS of Plant Varieties

Inferring predictive maps between multiple input and multiple output var...
10/04/2017 ∙ by Ming Yu, et al. ∙ 0 ∙ share

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Distributed Bundle Adjustment

Most methods for Bundle Adjustment (BA) in computer vision are either ce...
08/26/2017 ∙ by Karthikeyan Natesan Ramamurthy, et al. ∙ 0 ∙ share

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Learning Robust Representations for Computer Vision

Unsupervised learning techniques in computer vision often require learni...
07/31/2017 ∙ by Peng Zheng, et al. ∙ 0 ∙ share

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Shape Parameter Estimation

Performance of machine learning approaches depends strongly on the choic...
06/06/2017 ∙ by Peng Zheng, et al. ∙ 0 ∙ share

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Optimized Data Pre-Processing for Discrimination Prevention

Non-discrimination is a recognized objective in algorithmic decision mak...
04/11/2017 ∙ by Flavio P. Calmon, et al. ∙ 0 ∙ share

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A Deep Learning Approach To Multiple Kernel Fusion

Kernel fusion is a popular and effective approach for combining multiple...
12/28/2016 ∙ by Huan Song, et al. ∙ 0 ∙ share

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A Riemannian Framework for Statistical Analysis of Topological Persistence Diagrams

Topological data analysis is becoming a popular way to study high dimens...
05/28/2016 ∙ by Rushil Anirudh, et al. ∙ 0 ∙ share

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Persistent Homology of Attractors For Action Recognition

In this paper, we propose a novel framework for dynamical analysis of hu...
03/16/2016 ∙ by Vinay Venkataraman, et al. ∙ 0 ∙ share

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Automatic Inference of the Quantile Parameter

Supervised learning is an active research area, with numerous applicatio...
11/12/2015 ∙ by Karthikeyan Natesan Ramamurthy, et al. ∙ 0 ∙ share

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Beyond L2-Loss Functions for Learning Sparse Models

Incorporating sparsity priors in learning tasks can give rise to simple,...
03/26/2014 ∙ by Karthikeyan Natesan Ramamurthy, et al. ∙ 0 ∙ share

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Recovering Non-negative and Combined Sparse Representations

The non-negative solution to an underdetermined linear system can be uni...
03/12/2013 ∙ by Karthikeyan Natesan Ramamurthy, et al. ∙ 0 ∙ share

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Learning Stable Multilevel Dictionaries for Sparse Representations

Sparse representations using learned dictionaries are being increasingly...
03/03/2013 ∙ by Jayaraman J. Thiagarajan, et al. ∙ 0 ∙ share

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Karthikeyan Natesan Ramamurthy

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