Search Results for author: Kush R. Varshney

Found 58 papers, 10 papers with code

Differentially Private SGDA for Minimax Problems

no code implementations22 Jan 2022 Zhenhuan Yang, Shu Hu, Yunwen Lei, Kush R. Varshney, Siwei Lyu, Yiming Ying

We further provide its utility analysis in the nonconvex-strongly-concave setting which is the first-ever-known result in terms of the primal population risk.

CoFrNets: Interpretable Neural Architecture Inspired by Continued Fractions

no code implementations NeurIPS 2021 Isha Puri, Amit Dhurandhar, Tejaswini Pedapati, Karthikeyan Shanmugam, Dennis Wei, Kush R. Varshney

We experiment on nonlinear synthetic functions and are able to accurately model as well as estimate feature attributions and even higher order terms in some cases, which is a testament to the representational power as well as interpretability of such architectures.

Human-Centered Explainable AI (XAI): From Algorithms to User Experiences

no code implementations20 Oct 2021 Q. Vera Liao, Kush R. Varshney

In this chapter, we begin with a high-level overview of the technical landscape of XAI algorithms, then selectively survey our own and other recent HCI works that take human-centered approaches to design, evaluate, and provide conceptual and methodological tools for XAI.

An Empirical Study of Accuracy, Fairness, Explainability, Distributional Robustness, and Adversarial Robustness

no code implementations29 Sep 2021 Moninder Singh, Gevorg Ghalachyan, Kush R. Varshney, Reginald E. Bryant

To ensure trust in AI models, it is becoming increasingly apparent that evaluation of models must be extended beyond traditional performance metrics, like accuracy, to other dimensions, such as fairness, explainability, adversarial robustness, and distribution shift.

Adversarial Robustness Fairness

Towards Interpreting Zoonotic Potential of Betacoronavirus Sequences With Attention

no code implementations18 Aug 2021 Kahini Wadhawan, Payel Das, Barbara A. Han, Ilya R. Fischhoff, Adrian C. Castellanos, Arvind Varsani, Kush R. Varshney

Current methods for viral discovery target evolutionarily conserved proteins that accurately identify virus families but remain unable to distinguish the zoonotic potential of newly discovered viruses.

Biomedical Interpretable Entity Representations

1 code implementation Findings (ACL) 2021 Diego Garcia-Olano, Yasumasa Onoe, Ioana Baldini, Joydeep Ghosh, Byron C. Wallace, Kush R. Varshney

Pre-trained language models induce dense entity representations that offer strong performance on entity-centric NLP tasks, but such representations are not immediately interpretable.

Entity Disambiguation Representation Learning

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

1 code implementation2 Jun 2021 Soumya Ghosh, Q. Vera Liao, Karthikeyan Natesan Ramamurthy, Jiri Navratil, Prasanna Sattigeri, Kush R. Varshney, Yunfeng Zhang

In this paper, we describe an open source Python toolkit named Uncertainty Quantification 360 (UQ360) for the uncertainty quantification of AI models.

Fairness

Disparate Impact Diminishes Consumer Trust Even for Advantaged Users

no code implementations29 Jan 2021 Tim Draws, Zoltán Szlávik, Benjamin Timmermans, Nava Tintarev, Kush R. Varshney, Michael Hind

Systems aiming to aid consumers in their decision-making (e. g., by implementing persuasive techniques) are more likely to be effective when consumers trust them.

Decision Making Fairness Human-Computer Interaction

Socially Responsible AI Algorithms: Issues, Purposes, and Challenges

no code implementations1 Jan 2021 Lu Cheng, Kush R. Varshney, Huan Liu

In this survey, we provide a systematic framework of Socially Responsible AI Algorithms that aims to examine the subjects of AI indifference and the need for socially responsible AI algorithms, define the objectives, and introduce the means by which we may achieve these objectives.

Fairness

Learning to Initialize Gradient Descent Using Gradient Descent

no code implementations22 Dec 2020 Kartik Ahuja, Amit Dhurandhar, Kush R. Varshney

Non-convex optimization problems are challenging to solve; the success and computational expense of a gradient descent algorithm or variant depend heavily on the initialization strategy.

Empirical or Invariant Risk Minimization? A Sample Complexity Perspective

3 code implementations ICLR 2021 Kartik Ahuja, Jun Wang, Amit Dhurandhar, Karthikeyan Shanmugam, Kush R. Varshney

Recently, invariant risk minimization (IRM) was proposed as a promising solution to address out-of-distribution (OOD) generalization.

Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making

no code implementations15 Oct 2020 Charvi Rastogi, Yunfeng Zhang, Dennis Wei, Kush R. Varshney, Amit Dhurandhar, Richard Tomsett

We, then, conduct a second user experiment which shows that our time allocation strategy with explanation can effectively de-anchor the human and improve collaborative performance when the AI model has low confidence and is incorrect.

Decision Making

Causal Feature Selection for Algorithmic Fairness

no code implementations10 Jun 2020 Sainyam Galhotra, Karthikeyan Shanmugam, Prasanna Sattigeri, Kush R. Varshney

In this work, we consider fairness in the integration component of data management, aiming to identify features that improve prediction without adding any bias to the dataset.

Fairness

Invariant Risk Minimization Games

3 code implementations ICML 2020 Kartik Ahuja, Karthikeyan Shanmugam, Kush R. Varshney, Amit Dhurandhar

The standard risk minimization paradigm of machine learning is brittle when operating in environments whose test distributions are different from the training distribution due to spurious correlations.

Image Classification

Joint Optimization of AI Fairness and Utility: A Human-Centered Approach

no code implementations5 Feb 2020 Yunfeng Zhang, Rachel K. E. Bellamy, Kush R. Varshney

Today, AI is increasingly being used in many high-stakes decision-making applications in which fairness is an important concern.

Decision Making Fairness

Preservation of Anomalous Subgroups On Machine Learning Transformed Data

no code implementations9 Nov 2019 Samuel C. Maina, Reginald E. Bryant, William O. Goal, Robert-Florian Samoilescu, Kush R. Varshney, Komminist Weldemariam

Our evaluation confirmed that the approach was able to produce synthetic datasets that preserved a high level of subgroup differentiation as identified initially in the original dataset.

DADI: Dynamic Discovery of Fair Information with Adversarial Reinforcement Learning

no code implementations30 Oct 2019 Michiel A. Bakker, Duy Patrick Tu, Humberto Riverón Valdés, Krishna P. Gummadi, Kush R. Varshney, Adrian Weller, Alex Pentland

We introduce a framework for dynamic adversarial discovery of information (DADI), motivated by a scenario where information (a feature set) is used by third parties with unknown objectives.

Fairness reinforcement-learning +1

Estimating Skin Tone and Effects on Classification Performance in Dermatology Datasets

no code implementations29 Oct 2019 Newton M. Kinyanjui, Timothy Odonga, Celia Cintas, Noel C. F. Codella, Rameswar Panda, Prasanna Sattigeri, Kush R. Varshney

We find that the majority of the data in the the two datasets have ITA values between 34. 5{\deg} and 48{\deg}, which are associated with lighter skin, and is consistent with under-representation of darker skinned populations in these datasets.

General Classification Skin Cancer Classification

Is There a Trade-Off Between Fairness and Accuracy? A Perspective Using Mismatched Hypothesis Testing

no code implementations ICML 2020 Sanghamitra Dutta, Dennis Wei, Hazar Yueksel, Pin-Yu Chen, Sijia Liu, Kush R. Varshney

Moreover, the same classifier yields the lack of a trade-off with respect to ideal distributions while yielding a trade-off when accuracy is measured with respect to the given (possibly biased) dataset.

Fairness Two-sample testing

A Kolmogorov Complexity Approach to Generalization in Deep Learning

no code implementations25 Sep 2019 Hazar Yueksel, Kush R. Varshney, Brian Kingsbury

Using this condition, we formulate an optimization problem to learn a more general classification function.

Classification General Classification +1

How Data Scientists Work Together With Domain Experts in Scientific Collaborations: To Find The Right Answer Or To Ask The Right Question?

no code implementations8 Sep 2019 Yaoli Mao, Dakuo Wang, Michael Muller, Kush R. Varshney, Ioana Baldini, Casey Dugan, AleksandraMojsilović

Our findings suggest that besides the glitches in the collaboration readiness, technology readiness, and coupling of work dimensions, the tensions that exist in the common ground building process influence the collaboration outcomes, and then persist in the actual collaboration process.

Characterization of Overlap in Observational Studies

1 code implementation9 Jul 2019 Michael Oberst, Fredrik D. Johansson, Dennis Wei, Tian Gao, Gabriel Brat, David Sontag, Kush R. Varshney

Overlap between treatment groups is required for non-parametric estimation of causal effects.

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

no code implementations5 Jun 2019 Noel C. F. Codella, Michael Hind, Karthikeyan Natesan Ramamurthy, Murray Campbell, Amit Dhurandhar, Kush R. Varshney, Dennis Wei, Aleksandra Mojsilović

Using machine learning in high-stakes applications often requires predictions to be accompanied by explanations comprehensible to the domain user, who has ultimate responsibility for decisions and outcomes.

Multi-Task Learning

Bias Mitigation Post-processing for Individual and Group Fairness

no code implementations14 Dec 2018 Pranay K. Lohia, Karthikeyan Natesan Ramamurthy, Manish Bhide, Diptikalyan Saha, Kush R. Varshney, Ruchir Puri

Whereas previous post-processing approaches for increasing the fairness of predictions of biased classifiers address only group fairness, we propose a method for increasing both individual and group fairness.

Fairness General Classification

Understanding Unequal Gender Classification Accuracy from Face Images

no code implementations30 Nov 2018 Vidya Muthukumar, Tejaswini Pedapati, Nalini Ratha, Prasanna Sattigeri, Chai-Wah Wu, Brian Kingsbury, Abhishek Kumar, Samuel Thomas, Aleksandra Mojsilovic, Kush R. Varshney

Recent work shows unequal performance of commercial face classification services in the gender classification task across intersectional groups defined by skin type and gender.

Classification General Classification

TED: Teaching AI to Explain its Decisions

no code implementations12 Nov 2018 Michael Hind, Dennis Wei, Murray Campbell, Noel C. F. Codella, Amit Dhurandhar, Aleksandra Mojsilović, Karthikeyan Natesan Ramamurthy, Kush R. Varshney

Artificial intelligence systems are being increasingly deployed due to their potential to increase the efficiency, scale, consistency, fairness, and accuracy of decisions.

Fairness

SimplerVoice: A Key Message & Visual Description Generator System for Illiteracy

no code implementations3 Nov 2018 Minh N. B. Nguyen, Samuel Thomas, Anne E. Gattiker, Sujatha Kashyap, Kush R. Varshney

We introduce SimplerVoice: a key message and visual description generator system to help low-literate adults navigate the information-dense world with confidence, on their own.

Trusted Multi-Party Computation and Verifiable Simulations: A Scalable Blockchain Approach

no code implementations22 Sep 2018 Ravi Kiran Raman, Roman Vaculin, Michael Hind, Sekou L. Remy, Eleftheria K. Pissadaki, Nelson Kibichii Bore, Roozbeh Daneshvar, Biplav Srivastava, Kush R. Varshney

Large-scale computational experiments, often running over weeks and over large datasets, are used extensively in fields such as epidemiology, meteorology, computational biology, and healthcare to understand phenomena, and design high-stakes policies affecting everyday health and economy.

Epidemiology

Proceedings of the 2018 ICML Workshop on Human Interpretability in Machine Learning (WHI 2018)

no code implementations3 Jul 2018 Been Kim, Kush R. Varshney, Adrian Weller

This is the Proceedings of the 2018 ICML Workshop on Human Interpretability in Machine Learning (WHI 2018), which was held in Stockholm, Sweden, July 14, 2018.

14

Why Interpretability in Machine Learning? An Answer Using Distributed Detection and Data Fusion Theory

no code implementations25 Jun 2018 Kush R. Varshney, Prashant Khanduri, Pranay Sharma, Shan Zhang, Pramod K. Varshney

Such arguments, however, fail to acknowledge that the overall decision-making system is composed of two entities: the learned model and a human who fuses together model outputs with his or her own information.

Decision Making

Teaching Meaningful Explanations

no code implementations29 May 2018 Noel C. F. Codella, Michael Hind, Karthikeyan Natesan Ramamurthy, Murray Campbell, Amit Dhurandhar, Kush R. Varshney, Dennis Wei, Aleksandra Mojsilovic

The adoption of machine learning in high-stakes applications such as healthcare and law has lagged in part because predictions are not accompanied by explanations comprehensible to the domain user, who often holds the ultimate responsibility for decisions and outcomes.

Topological Data Analysis of Decision Boundaries with Application to Model Selection

1 code implementation25 May 2018 Karthikeyan Natesan Ramamurthy, Kush R. Varshney, Krishnan Mody

We propose the labeled \v{C}ech complex, the plain labeled Vietoris-Rips complex, and the locally scaled labeled Vietoris-Rips complex to perform persistent homology inference of decision boundaries in classification tasks.

General Classification Model Selection +1

Fairness GAN

no code implementations24 May 2018 Prasanna Sattigeri, Samuel C. Hoffman, Vijil Chenthamarakshan, Kush R. Varshney

In this paper, we introduce the Fairness GAN, an approach for generating a dataset that is plausibly similar to a given multimedia dataset, but is more fair with respect to protected attributes in allocative decision making.

Decision Making Fairness

Structure Learning from Time Series with False Discovery Control

no code implementations24 May 2018 Bernat Guillen Pegueroles, Bhanukiran Vinzamuri, Karthikeyan Shanmugam, Steve Hedden, Jonathan D. Moyer, Kush R. Varshney

Almost all existing Granger causal algorithms condition on a large number of variables (all but two variables) to test for effects between a pair of variables.

Time Series

The Effect of Extremist Violence on Hateful Speech Online

1 code implementation16 Apr 2018 Alexandra Olteanu, Carlos Castillo, Jeremy Boy, Kush R. Varshney

In this paper, we focus on quantifying the impact of violent events on various types of hate speech, from offensive and derogatory to intimidation and explicit calls for violence.

Social and Information Networks Computers and Society

How an Electrical Engineer Became an Artificial Intelligence Researcher, a Multiphase Active Contours Analysis

no code implementations29 Mar 2018 Kush R. Varshney

This essay examines how what is considered to be artificial intelligence (AI) has changed over time and come to intersect with the expertise of the author.

Interpretable Machine Learning reinforcement-learning

Neurology-as-a-Service for the Developing World

no code implementations16 Nov 2017 Tejas Dharamsi, Payel Das, Tejaswini Pedapati, Gregory Bramble, Vinod Muthusamy, Horst Samulowitz, Kush R. Varshney, Yuvaraj Rajamanickam, John Thomas, Justin Dauwels

In this work, we present a cloud-based deep neural network approach to provide decision support for non-specialist physicians in EEG analysis and interpretation.

EEG Feature Engineering

Distribution-Preserving k-Anonymity

no code implementations5 Nov 2017 Dennis Wei, Karthikeyan Natesan Ramamurthy, Kush R. Varshney

Preserving the privacy of individuals by protecting their sensitive attributes is an important consideration during microdata release.

Quantization Transfer Learning

Proceedings of the 2017 ICML Workshop on Human Interpretability in Machine Learning (WHI 2017)

no code implementations8 Aug 2017 Been Kim, Dmitry M. Malioutov, Kush R. Varshney, Adrian Weller

This is the Proceedings of the 2017 ICML Workshop on Human Interpretability in Machine Learning (WHI 2017), which was held in Sydney, Australia, August 10, 2017.

On the Safety of Machine Learning: Cyber-Physical Systems, Decision Sciences, and Data Products

no code implementations5 Oct 2016 Kush R. Varshney, Homa Alemzadeh

Machine learning algorithms increasingly influence our decisions and interact with us in all parts of our daily lives.

Proceedings of the 2016 ICML Workshop on Human Interpretability in Machine Learning (WHI 2016)

no code implementations8 Jul 2016 Been Kim, Dmitry M. Malioutov, Kush R. Varshney

This is the Proceedings of the 2016 ICML Workshop on Human Interpretability in Machine Learning (WHI 2016), which was held in New York, NY, June 23, 2016.

Proceedings of the 2016 ICML Workshop on #Data4Good: Machine Learning in Social Good Applications

no code implementations8 Jul 2016 Kush R. Varshney

This is the Proceedings of the ICML Workshop on #Data4Good: Machine Learning in Social Good Applications, which was held on June 24, 2016 in New York.

Interpretable Two-level Boolean Rule Learning for Classification

no code implementations18 Jun 2016 Guolong Su, Dennis Wei, Kush R. Varshney, Dmitry M. Malioutov

As a contribution to interpretable machine learning research, we develop a novel optimization framework for learning accurate and sparse two-level Boolean rules.

Classification General Classification +1

Dynamic matrix factorization with social influence

no code implementations21 Apr 2016 Aleksandr Y. Aravkin, Kush R. Varshney, Liu Yang

Matrix factorization is a key component of collaborative filtering-based recommendation systems because it allows us to complete sparse user-by-item ratings matrices under a low-rank assumption that encodes the belief that similar users give similar ratings and that similar items garner similar ratings.

Collaborative Filtering Recommendation Systems

Engineering Safety in Machine Learning

no code implementations16 Jan 2016 Kush R. Varshney

Machine learning algorithms are increasingly influencing our decisions and interacting with us in all parts of our daily lives.

Interpretable Two-level Boolean Rule Learning for Classification

no code implementations23 Nov 2015 Guolong Su, Dennis Wei, Kush R. Varshney, Dmitry M. Malioutov

Experiments show that the two-level rules can yield noticeably better performance than one-level rules due to their dramatically larger modeling capacity, and the two algorithms based on the Hamming distance formulation are generally superior to the other two-level rule learning methods in our comparison.

Classification General Classification

A Big Data Approach to Computational Creativity

1 code implementation5 Nov 2013 Lav R. Varshney, Florian Pinel, Kush R. Varshney, Debarun Bhattacharjya, Angela Schoergendorfer, Yi-Min Chee

Computational creativity is an emerging branch of artificial intelligence that places computers in the center of the creative process.

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