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05/31/2023
The Impact of Positional Encoding on Length Generalization in Transformers
Length generalization, the ability to generalize from small training con...
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02/17/2023
Function Composition in Trustworthy Machine Learning: Implementation Choices, Insights, and Questions
Ensuring trustworthiness in machine learning (ML) models is a multi-dime...
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01/30/2023
Active Sequential Two-Sample Testing
Two-sample testing tests whether the distributions generating two sample...
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10/13/2022
Equi-Tuning: Group Equivariant Fine-Tuning of Pretrained Models
We introduce equi-tuning, a novel fine-tuning method that transforms (po...
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06/01/2022
Higher-Order Attention Networks
This paper introduces higher-order attention networks (HOANs), a novel c...
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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...
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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 ...
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11/17/2021
A label efficient two-sample test
Two-sample tests evaluate whether two samples are realizations of the sa...
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10/06/2021
Data-Centric AI Requires Rethinking Data Notion
The transition towards data-centric AI requires revisiting data notions ...
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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...
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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...
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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...
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11/19/2020
Finding the Homology of Decision Boundaries with Active Learning
Accurately and efficiently characterizing the decision boundary of class...
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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...
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04/30/2020
Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness
Mode connectivity provides novel geometric insights on analyzing loss la...
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03/12/2020
Model Agnostic Multilevel Explanations
In recent years, post-hoc local instance-level and global dataset-level ...
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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...
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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...
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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...
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06/05/2019
PI-Net: A Deep Learning Approach to Extract Topological Persistence Images
Topological features such as persistence diagrams and their functional a...
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05/31/2019
Optimized Score Transformation for Fair Classification
This paper considers fair probabilistic classification where the outputs...
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05/30/2019
Counting and Segmenting Sorghum Heads
Phenotyping is the process of measuring an organism's observable traits....
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03/15/2019
Crowd Counting with Decomposed Uncertainty
Research in neural networks in the field of computer vision has achieved...
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12/14/2018
Bias Mitigation Post-processing for Individual and Group Fairness
Whereas previous post-processing approaches for increasing the fairness ...
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11/12/2018
TED: Teaching AI to Explain its Decisions
Artificial intelligence systems are being increasingly deployed due to t...
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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...
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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...
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07/26/2018
Perturbation Robust Representations of Topological Persistence Diagrams
Topological methods for data analysis present opportunities for enforcin...
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05/29/2018
Teaching Meaningful Explanations
The adoption of machine learning in high-stakes applications such as hea...
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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...
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04/29/2018
Simultaneous Parameter Learning and Bi-Clustering for Multi-Response Models
We consider multi-response and multitask regression models, where the pa...
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12/19/2017
Exploring High-Dimensional Structure via Axis-Aligned Decomposition of Linear Projections
Two-dimensional embeddings remain the dominant approach to visualize hig...
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11/05/2017
Distribution-Preserving k-Anonymity
Preserving the privacy of individuals by protecting their sensitive attr...
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10/18/2017
An End-To-End Machine Learning Pipeline That Ensures Fairness Policies
In consequential real-world applications, machine learning (ML) based sy...
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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...
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08/26/2017
Distributed Bundle Adjustment
Most methods for Bundle Adjustment (BA) in computer vision are either ce...
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07/31/2017
Learning Robust Representations for Computer Vision
Unsupervised learning techniques in computer vision often require learni...
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06/06/2017
Shape Parameter Estimation
Performance of machine learning approaches depends strongly on the choic...
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04/11/2017
Optimized Data Pre-Processing for Discrimination Prevention
Non-discrimination is a recognized objective in algorithmic decision mak...
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12/28/2016
A Deep Learning Approach To Multiple Kernel Fusion
Kernel fusion is a popular and effective approach for combining multiple...
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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...
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03/16/2016
Persistent Homology of Attractors For Action Recognition
In this paper, we propose a novel framework for dynamical analysis of hu...
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11/12/2015
Automatic Inference of the Quantile Parameter
Supervised learning is an active research area, with numerous applicatio...
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03/26/2014
Beyond L2-Loss Functions for Learning Sparse Models
Incorporating sparsity priors in learning tasks can give rise to simple,...
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03/12/2013
Recovering Non-negative and Combined Sparse Representations
The non-negative solution to an underdetermined linear system can be uni...
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03/03/2013
