Karthikeyan Natesan Ramamurthy

research

∙ 06/01/2022

Higher-Order Attention Networks

This paper introduces higher-order attention networks (HOANs), a novel c...

Mustafa Hajij, et al. ∙

research

∙ 02/02/2022

Analogies and Feature Attributions for Model Agnostic Explanation of Similarity Learners

Post-hoc explanations for black box models have been studied extensively...

Karthikeyan Natesan Ramamurthy, et al. ∙

research

∙ 12/07/2021

Ground-Truth, Whose Truth? – Examining the Challenges with Annotating Toxic Text Datasets

The use of machine learning (ML)-based language models (LMs) to monitor ...

Kofi Arhin, et al. ∙

research

∙ 11/17/2021

A label efficient two-sample test

Two-sample tests evaluate whether two samples are realizations of the sa...

Weizhi Li, et al. ∙

research

∙ 10/06/2021

Data-Centric AI Requires Rethinking Data Notion

The transition towards data-centric AI requires revisiting data notions ...

Mustafa Hajij, et al. ∙

research

∙ 08/03/2021

Your fairness may vary: Group fairness of pretrained language models in toxic text classification

We study the performance-fairness trade-off in more than a dozen fine-tu...

Ioana Baldini, et al. ∙

research

∙ 06/08/2021

Augmenting Molecular Deep Generative Models with Topological Data Analysis Representations

Deep generative models have emerged as a powerful tool for learning info...

Yair Schiff, et al. ∙

research

∙ 06/02/2021

Uncertainty Quantification 360: A Holistic Toolkit for Quantifying and Communicating the Uncertainty of AI

In this paper, we describe an open source Python toolkit named Uncertain...

Soumya Ghosh, et al. ∙

research

∙ 11/19/2020

Finding the Homology of Decision Boundaries with Active Learning

Accurately and efficiently characterizing the decision boundary of class...

Weizhi Li, et al. ∙

research

∙ 10/18/2020

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

Deep generative models are increasingly becoming integral parts of the i...

Yair Schiff, et al. ∙

research

∙ 04/30/2020

Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness

Mode connectivity provides novel geometric insights on analyzing loss la...

Pu Zhao, et al. ∙

research

∙ 03/12/2020

Model Agnostic Multilevel Explanations

In recent years, post-hoc local instance-level and global dataset-level ...

Karthikeyan Natesan Ramamurthy, et al. ∙

research

∙ 11/18/2019

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...

Shivashankar Subramanian, et al. ∙

research

∙ 11/04/2019

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

Over the years, several studies have demonstrated that there exist signi...

Moninder Singh, et al. ∙

research

∙ 06/05/2019

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

Using machine learning in high-stakes applications often requires predic...

Noel C. F. Codella, et al. ∙

research

∙ 06/05/2019

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

Topological features such as persistence diagrams and their functional a...

Anirudh Som, et al. ∙

research

∙ 05/31/2019

Optimized Score Transformation for Fair Classification

This paper considers fair probabilistic classification where the outputs...

Dennis Wei, et al. ∙

research

∙ 05/30/2019

Counting and Segmenting Sorghum Heads

Phenotyping is the process of measuring an organism's observable traits....

Min-hwan Oh, et al. ∙

research

∙ 03/15/2019

Crowd Counting with Decomposed Uncertainty

Research in neural networks in the field of computer vision has achieved...

Min-hwan Oh, et al. ∙

research

∙ 12/14/2018

Bias Mitigation Post-processing for Individual and Group Fairness

Whereas previous post-processing approaches for increasing the fairness ...

Pranay K. Lohia, et al. ∙

research

∙ 11/12/2018

TED: Teaching AI to Explain its Decisions

Artificial intelligence systems are being increasingly deployed due to t...

Noel C. F. Codella, et al. ∙

research

∙ 10/03/2018

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

Fairness is an increasingly important concern as machine learning models...

Rachel K. E. Bellamy, et al. ∙

research

∙ 08/22/2018

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

The accuracy and reliability of machine learning algorithms are an impor...

Michael Hind, et al. ∙

research

∙ 07/26/2018

Perturbation Robust Representations of Topological Persistence Diagrams

Topological methods for data analysis present opportunities for enforcin...

Anirudh Som, et al. ∙

research

∙ 05/29/2018

Teaching Meaningful Explanations

The adoption of machine learning in high-stakes applications such as hea...

Noel C. F. Codella, et al. ∙

research

∙ 05/25/2018

Topological Data Analysis of Decision Boundaries with Application to Model Selection

We propose the labeled Čech complex, the plain labeled Vietoris-Rips com...

Karthikeyan Natesan Ramamurthy, et al. ∙

research

∙ 04/29/2018

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

We consider multi-response and multitask regression models, where the pa...

Ming Yu, et al. ∙

research

∙ 12/19/2017

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

Two-dimensional embeddings remain the dominant approach to visualize hig...

Jayaraman J. Thiagarajan, et al. ∙

research

∙ 11/05/2017

Distribution-Preserving k-Anonymity

Preserving the privacy of individuals by protecting their sensitive attr...

Dennis Wei, et al. ∙

research

∙ 10/18/2017

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

In consequential real-world applications, machine learning (ML) based sy...

Samiulla Shaikh, et al. ∙

research

∙ 10/04/2017

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...

Ming Yu, et al. ∙

research

∙ 08/26/2017

Distributed Bundle Adjustment

Most methods for Bundle Adjustment (BA) in computer vision are either ce...

Karthikeyan Natesan Ramamurthy, et al. ∙

research

∙ 07/31/2017

Learning Robust Representations for Computer Vision

Unsupervised learning techniques in computer vision often require learni...

Aleksandr Y. Aravkin, et al. ∙

research

∙ 06/06/2017

Shape Parameter Estimation

Performance of machine learning approaches depends strongly on the choic...

Aleksandr Y. Aravkin, et al. ∙

research

∙ 04/11/2017

Optimized Data Pre-Processing for Discrimination Prevention

Non-discrimination is a recognized objective in algorithmic decision mak...

Flavio P. Calmon, et al. ∙

research

∙ 12/28/2016

A Deep Learning Approach To Multiple Kernel Fusion

Kernel fusion is a popular and effective approach for combining multiple...

Huan Song, et al. ∙

research

∙ 05/28/2016

A Riemannian Framework for Statistical Analysis of Topological Persistence Diagrams

Topological data analysis is becoming a popular way to study high dimens...

Rushil Anirudh, et al. ∙

research

∙ 03/16/2016

Persistent Homology of Attractors For Action Recognition

In this paper, we propose a novel framework for dynamical analysis of hu...

Vinay Venkataraman, et al. ∙

research

∙ 11/12/2015

Automatic Inference of the Quantile Parameter

Supervised learning is an active research area, with numerous applicatio...

Karthikeyan Natesan Ramamurthy, et al. ∙

research

∙ 03/26/2014

Beyond L2-Loss Functions for Learning Sparse Models

Incorporating sparsity priors in learning tasks can give rise to simple,...

Karthikeyan Natesan Ramamurthy, et al. ∙

research

∙ 03/12/2013

Recovering Non-negative and Combined Sparse Representations

The non-negative solution to an underdetermined linear system can be uni...

Karthikeyan Natesan Ramamurthy, et al. ∙

research

∙ 03/03/2013

Learning Stable Multilevel Dictionaries for Sparse Representations

Sparse representations using learned dictionaries are being increasingly...

Jayaraman J. Thiagarajan, et al. ∙

Karthikeyan Natesan Ramamurthy

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