Dennis Wei

Orcid: 0000-0002-6510-1537

According to our database1, Dennis Wei authored at least 77 papers between 2010 and 2024.

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Bibliography

2024
Multi-Level Explanations for Generative Language Models.
CoRR, 2024

Detectors for Safe and Reliable LLMs: Implementations, Uses, and Limitations.
CoRR, 2024

Causal Bandits with General Causal Models and Interventions.
CoRR, 2024

Trust Regions for Explanations via Black-Box Probabilistic Certification.
CoRR, 2024

2023
Interpretable and Fair Boolean Rule Sets via Column Generation.
J. Mach. Learn. Res., 2023

SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation.
CoRR, 2023

Interpretable differencing of machine learning models.
Proceedings of the Uncertainty in Artificial Intelligence, 2023

Effective Human-AI Teams via Learned Natural Language Rules and Onboarding.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

A Statistical Interpretation of the Maximum Subarray Problem.
Proceedings of the IEEE International Conference on Acoustics, 2023

Add-Remove-or-Relabel: Practitioner-Friendly Bias Mitigation via Influential Fairness.
Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, 2023

Convex Bounds on the Softmax Function with Applications to Robustness Verification.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

Who Should Predict? Exact Algorithms For Learning to Defer to Humans.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

Heavy Sets with Applications to Interpretable Machine Learning Diagnostics.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

Stress-Testing Bias Mitigation Algorithms to Understand Fairness Vulnerabilities.
Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society, 2023

2022
Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making.
Proc. ACM Hum. Comput. Interact., 2022

Downstream Fairness Caveats with Synthetic Healthcare Data.
CoRR, 2022

Analogies and Feature Attributions for Model Agnostic Explanation of Similarity Learners.
CoRR, 2022

On the Safety of Interpretable Machine Learning: A Maximum Deviation Approach.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

FROTE: Feedback Rule-Driven Oversampling for Editing Models.
Proceedings of Machine Learning and Systems 2022, 2022

Evaluating Fairness of Synthetic Healthcare Data Models.
Proceedings of the AMIA 2022, 2022

Your fairness may vary: Pretrained language model fairness in toxic text classification.
Proceedings of the Findings of the Association for Computational Linguistics: ACL 2022, 2022


2021
Optimized Score Transformation for Consistent Fair Classification.
J. Mach. Learn. Res., 2021

Ground-Truth, Whose Truth? - Examining the Challenges with Annotating Toxic Text Datasets.
CoRR, 2021

Your fairness may vary: Group fairness of pretrained language models in toxic text classification.
CoRR, 2021

Conditionally independent data generation.
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

CoFrNets: Interpretable Neural Architecture Inspired by Continued Fractions.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

AIMEE: Interactive model maintenance with rule-based surrogates.
Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track, 2021

What Changed? Interpretable Model Comparison.
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

Decision-Making Under Selective Labels: Optimal Finite-Domain Policies and Beyond.
Proceedings of the 38th International Conference on Machine Learning, 2021

Treatment Effect Estimation Using Invariant Risk Minimization.
Proceedings of the IEEE International Conference on Acoustics, 2021


2020
AI Explainability 360: An Extensible Toolkit for Understanding Data and Machine Learning Models.
J. Mach. Learn. Res., 2020

Optimal Policies for the Homogeneous Selective Labels Problem.
CoRR, 2020

Consumer-Driven Explanations for Machine Learning Decisions: An Empirical Study of Robustness.
CoRR, 2020

DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networks.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Model Projection: Theory and Applications to Fair Machine Learning.
Proceedings of the IEEE International Symposium on Information Theory, 2020

Is There a Trade-Off Between Fairness and Accuracy? A Perspective Using Mismatched Hypothesis Testing.
Proceedings of the 37th International Conference on Machine Learning, 2020


Interpretable subgroup discovery in treatment effect estimation with application to opioid prescribing guidelines.
Proceedings of the ACM CHIL '20: ACM Conference on Health, 2020

Optimized Score Transformation for Fair Classification.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

Characterization of Overlap in Observational Studies.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
An Information-Theoretic Perspective on the Relationship Between Fairness and Accuracy.
CoRR, 2019

One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques.
CoRR, 2019

Teaching AI to Explain its Decisions Using Embeddings and Multi-Task Learning.
CoRR, 2019

Generalized Linear Rule Models.
Proceedings of the 36th International Conference on Machine Learning, 2019

TED: Teaching AI to Explain its Decisions.
Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 2019

Fair Transfer Learning with Missing Protected Attributes.
Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 2019

2018
Distribution-preserving k-anonymity.
Stat. Anal. Data Min., 2018

Data Pre-Processing for Discrimination Prevention: Information-Theoretic Optimization and Analysis.
IEEE J. Sel. Top. Signal Process., 2018

Teaching Meaningful Explanations.
CoRR, 2018

Boolean Decision Rules via Column Generation.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Parallel Bayesian Network Structure Learning.
Proceedings of the 35th International Conference on Machine Learning, 2018

On the Supermodularity of Active Graph-Based Semi-Supervised Learning with Stieltjes Matrix Regularization.
Proceedings of the 2018 IEEE International Conference on Acoustics, 2018

2017
k-quantiles: L1 distance clustering under a sum constraint.
Pattern Recognit. Lett., 2017

An End-To-End Machine Learning Pipeline That Ensures Fairness Policies.
CoRR, 2017

Optimized Data Pre-Processing for Discrimination Prevention.
CoRR, 2017

Optimized Pre-Processing for Discrimination Prevention.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

A configurable, big data system for on-demand healthcare cost prediction.
Proceedings of the 2017 IEEE International Conference on Big Data (IEEE BigData 2017), 2017

2016
A Constant-Factor Bi-Criteria Approximation Guarantee for k-means++.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Learning sparse two-level boolean rules.
Proceedings of the 26th IEEE International Workshop on Machine Learning for Signal Processing, 2016

Empirically-estimable multi-class classification bounds.
Proceedings of the 2016 IEEE International Conference on Acoustics, 2016

2015
Data Challenges in Disease Response: The 2014 Ebola Outbreak and Beyond.
ACM J. Data Inf. Qual., 2015

Interpretable Two-level Boolean Rule Learning for Classification.
CoRR, 2015

Health Insurance Market Risk Assessment: Covariate Shift and k-Anonymity.
Proceedings of the 2015 SIAM International Conference on Data Mining, Vancouver, BC, Canada, April 30, 2015

Toward Comprehensive Attribution of Healthcare Cost Changes.
Proceedings of the IEEE International Conference on Data Mining Workshop, 2015

Robust binary hypothesis testing under contaminated likelihoods.
Proceedings of the 2015 IEEE International Conference on Acoustics, 2015

Adaptive sensing resource allocation over multiple hypothesis tests.
Proceedings of the 2015 IEEE International Conference on Acoustics, 2015

Optigrow: People Analytics for Job Transfers.
Proceedings of the 2015 IEEE International Congress on Big Data, New York City, NY, USA, June 27, 2015

2014
Multiplicative regression via constrained least squares.
Proceedings of the IEEE Workshop on Statistical Signal Processing, 2014

2013
Sparse Filter Design Under a Quadratic Constraint: Low-Complexity Algorithms.
IEEE Trans. Signal Process., 2013

A Branch-and-Bound Algorithm for Quadratically-Constrained Sparse Filter Design.
IEEE Trans. Signal Process., 2013

2011
Design of discrete-time filters for efficient implementation.
PhD thesis, 2011

Saturation-robust SAR image formation.
Proceedings of the IEEE International Conference on Acoustics, 2011

2010
Linear programming algorithms for sparse filter design.
IEEE Trans. Signal Process., 2010

Video stabilization and rolling shutter distortion reduction.
Proceedings of the International Conference on Image Processing, 2010

Sparsity maximization under a quadratic constraint with applications in filter design.
Proceedings of the IEEE International Conference on Acoustics, 2010


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