Search Results for author: Dennis Wei

Found 39 papers, 4 papers with code

Downstream Fairness Caveats with Synthetic Healthcare Data

no code implementations9 Mar 2022 Karan Bhanot, Ioana Baldini, Dennis Wei, Jiaming Zeng, Kristin P. Bennett

In this paper, we evaluate the fairness of models generated on two healthcare datasets for gender and race biases.

Fairness

Analogies and Feature Attributions for Model Agnostic Explanation of Similarity Learners

no code implementations2 Feb 2022 Karthikeyan Natesan Ramamurthy, Amit Dhurandhar, Dennis Wei, Zaid Bin Tariq

We first propose a method that provides feature attributions to explain the similarity between a pair of inputs as determined by a black box similarity learner.

FROTE: Feedback Rule-Driven Oversampling for Editing Models

no code implementations4 Jan 2022 Öznur Alkan, Dennis Wei, Massimiliano Mattetti, Rahul Nair, Elizabeth M. Daly, Diptikalyan Saha

However, in such scenarios, it may take time for sufficient training data to accumulate in order to retrain the model to reflect the new decision boundaries.

Data Augmentation

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.

Interpretable and Fair Boolean Rule Sets via Column Generation

no code implementations16 Nov 2021 Connor Lawless, Sanjeeb Dash, Oktay Gunluk, Dennis Wei

This paper considers the learning of Boolean rules in either disjunctive normal form (DNF, OR-of-ANDs, equivalent to decision rule sets) or conjunctive normal form (CNF, AND-of-ORs) as an interpretable model for classification.

Fairness

Your fairness may vary: Pretrained language model fairness in toxic text classification

no code implementations Findings (ACL) 2022 Ioana Baldini, Dennis Wei, Karthikeyan Natesan Ramamurthy, Mikhail Yurochkin, Moninder Singh

Through the analysis of more than a dozen pretrained language models of varying sizes on two toxic text classification tasks (English), we demonstrate that focusing on accuracy measures alone can lead to models with wide variation in fairness characteristics.

Fairness Language Modelling +3

Treatment Effect Estimation using Invariant Risk Minimization

2 code implementations13 Mar 2021 Abhin Shah, Kartik Ahuja, Karthikeyan Shanmugam, Dennis Wei, Kush Varshney, Amit Dhurandhar

Inferring causal individual treatment effect (ITE) from observational data is a challenging problem whose difficulty is exacerbated by the presence of treatment assignment bias.

Domain Generalization

Optimal Policies for the Homogeneous Selective Labels Problem

no code implementations2 Nov 2020 Dennis Wei

Selective labels are a common feature of consequential decision-making applications, referring to the lack of observed outcomes under one of the possible decisions.

Decision Making

DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networks

1 code implementation NeurIPS 2020 Dennis Wei, Tian Gao, Yue Yu

This paper re-examines a continuous optimization framework dubbed NOTEARS for learning Bayesian networks.

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

Consumer-Driven Explanations for Machine Learning Decisions: An Empirical Study of Robustness

no code implementations13 Jan 2020 Michael Hind, Dennis Wei, Yunfeng Zhang

Many proposed methods for explaining machine learning predictions are in fact challenging to understand for nontechnical consumers.

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

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

Generalized Linear Rule Models

no code implementations5 Jun 2019 Dennis Wei, Sanjeeb Dash, Tian Gao, Oktay Günlük

Column generation is used to optimize over an exponentially large space of rules without pre-generating a large subset of candidates or greedily boosting rules one by one.

General Classification

Optimized Score Transformation for Consistent Fair Classification

no code implementations31 May 2019 Dennis Wei, Karthikeyan Natesan Ramamurthy, Flavio du Pin Calmon

We derive a closed-form expression for the optimal transformed scores and a convex optimization problem for the transformation parameters.

Classification Fairness +1

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

Parallel Bayesian Network Structure Learning

no code implementations ICML 2018 Tian Gao, Dennis Wei

Recent advances in Bayesian Network (BN) structure learning have focused on local-to-global learning, where the graph structure is learned via one local subgraph at a time.

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.

Boolean Decision Rules via Column Generation

no code implementations NeurIPS 2018 Sanjeeb Dash, Oktay Günlük, Dennis Wei

This paper considers the learning of Boolean rules in either disjunctive normal form (DNF, OR-of-ANDs, equivalent to decision rule sets) or conjunctive normal form (CNF, AND-of-ORs) as an interpretable model for classification.

General Classification

On the Supermodularity of Active Graph-based Semi-supervised Learning with Stieltjes Matrix Regularization

no code implementations9 Apr 2018 Pin-Yu Chen, Dennis Wei

Active graph-based semi-supervised learning (AG-SSL) aims to select a small set of labeled examples and utilize their graph-based relation to other unlabeled examples to aid in machine learning tasks.

Community Detection General Classification

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

A Constant-Factor Bi-Criteria Approximation Guarantee for k-means++

no code implementations NeurIPS 2016 Dennis Wei

This paper studies the $k$-means++ algorithm for clustering as well as the class of $D^\ell$ sampling algorithms to which $k$-means++ belongs.

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

A Constant-Factor Bi-Criteria Approximation Guarantee for $k$-means++

no code implementations16 May 2016 Dennis Wei

This paper studies the $k$-means++ algorithm for clustering as well as the class of $D^\ell$ sampling algorithms to which $k$-means++ belongs.

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

Statistical Estimation and Clustering of Group-invariant Orientation Parameters

no code implementations15 Mar 2015 Yu-Hui Chen, Dennis Wei, Gregory Newstadt, Marc DeGraef, Jeffrey Simmons, Alfred Hero

We treat the problem of estimation of orientation parameters whose values are invariant to transformations from a spherical symmetry group.

Coercive Region-level Registration for Multi-modal Images

no code implementations26 Feb 2015 Yu-Hui Chen, Dennis Wei, Gregory Newstadt, Jeffrey Simmons, Alfred Hero

We propose a coercive approach to simultaneously register and segment multi-modal images which share similar spatial structure.

A Dictionary Approach to EBSD Indexing

no code implementations26 Feb 2015 Yu-Hui Chen, Se Un Park, Dennis Wei, Gregory Newstadt, Michael Jackson, Jeff P. Simmons, Marc De Graef, Alfred O. Hero

We discretize the domain of the forward model onto a dense grid of Euler angles and for each measured pattern we identify the most similar patterns in the dictionary.

Anomaly Detection

Parameter estimation in spherical symmetry groups

no code implementations10 Nov 2014 Yu-Hui Chen, Dennis Wei, Gregory Newstadt, Marc DeGraef, Jeffrey Simmons, Alfred Hero

This paper considers statistical estimation problems where the probability distribution of the observed random variable is invariant with respect to actions of a finite topological group.

Spectral Correlation Hub Screening of Multivariate Time Series

no code implementations13 Mar 2014 Hamed Firouzi, Dennis Wei, Alfred O. Hero III

This property permits independent correlation analysis at each frequency, alleviating the computational and statistical challenges of high-dimensional time series.

Time Series

Marginal Likelihoods for Distributed Parameter Estimation of Gaussian Graphical Models

no code implementations19 Mar 2013 Zhaoshi Meng, Dennis Wei, Ami Wiesel, Alfred O. Hero III

In this paper, we propose a general framework for distributed estimation based on a maximum marginal likelihood (MML) approach.

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