Sparsity maximization under a quadratic constraint with applications in filter design. - Dennis Wei; Alan V. Oppenheim (ICASSP, 2010)

Video stabilization and rolling shutter distortion reduction. - Wei Hong; Dennis Wei; Aziz Umit Batur (ICIP, 2010)

Linear programming algorithms for sparse filter design. - Thomas A. Baran; Dennis Wei; Alan V. Oppenheim (IEEE Trans. Signal Process., 2010)

Saturation-robust SAR image formation. - Dennis Wei; Petros Boufounos (ICASSP, 2011)

Design of discrete-time filters for efficient implementation. - Dennis Wei (2011)

A Branch-and-Bound Algorithm for Quadratically-Constrained Sparse Filter Design. - Dennis Wei; Alan V. Oppenheim (IEEE Trans. Signal Process., 2013)

Sparse Filter Design Under a Quadratic Constraint: Low-Complexity Algorithms. - Dennis Wei; Charles K. Sestok; Alan V. Oppenheim (IEEE Trans. Signal Process., 2013)

Multiplicative regression via constrained least squares. - Dennis Wei; Karthikeyan Natesan Ramamurthy; Dmitriy A. Katz-Rogozhnikov; Aleksandra Mojsilovic (SSP, 2014)

Optigrow: People Analytics for Job Transfers. - Dennis Wei; Kush R. Varshney; Marcy Wagman (BigData Congress, 2015)

Adaptive sensing resource allocation over multiple hypothesis tests. - Dennis Wei (ICASSP, 2015)

Robust binary hypothesis testing under contaminated likelihoods. - Dennis Wei; Kush R. Varshney (ICASSP, 2015)

Toward Comprehensive Attribution of Healthcare Cost Changes. - Dmitriy A. Katz-Rogozhnikov; Dennis Wei; Gigi Y. Yuen-Reed; Karthikeyan Natesan Ramamurthy; Aleksandra Mojsilovic (ICDM Workshops, 2015)

Health Insurance Market Risk Assessment: Covariate Shift and k-Anonymity. - Dennis Wei; Karthikeyan Natesan Ramamurthy; Kush R. Varshney (SDM, 2015)

Interpretable Two-level Boolean Rule Learning for Classification. - Guolong Su; Dennis Wei; Kush R. Varshney; Dmitry M. Malioutov (CoRR, 2015)

Data Challenges in Disease Response: The 2014 Ebola Outbreak and Beyond. - Kush R. Varshney; Dennis Wei; Karthikeyan Natesan Ramamurthy; Aleksandra Mojsilovic (ACM J. Data Inf. Qual., 2015)

Empirically-estimable multi-class classification bounds. - Alan Wisler; Visar Berisha; Dennis Wei; Karthikeyan Ramamurthy; Andreas Spanias (ICASSP, 2016)

Learning sparse two-level boolean rules. - Guolong Su; Dennis Wei; Kush R. Varshney; Dmitry M. Malioutov (MLSP, 2016)

A Constant-Factor Bi-Criteria Approximation Guarantee for k-means++. - Dennis Wei (NIPS, 2016)

A configurable, big data system for on-demand healthcare cost prediction. - Karthikeyan Natesan Ramamurthy; Dennis Wei; Emily Ray; Moninder Singh; Vijay S. Iyengar; Dmitriy A. Katz-Rogozhnikov; Jingwei Yang; Kevin N. Tran; Gigi Y. Yuen-Reed (IEEE BigData, 2017)

Optimized Pre-Processing for Discrimination Prevention. - Flávio P. Calmon; Dennis Wei; Bhanukiran Vinzamuri; Karthikeyan Natesan Ramamurthy; Kush R. Varshney (NIPS, 2017)

Optimized Data Pre-Processing for Discrimination Prevention. - Flávio du Pin Calmon; Dennis Wei; Karthikeyan Natesan Ramamurthy; Kush R. Varshney (CoRR, 2017)

An End-To-End Machine Learning Pipeline That Ensures Fairness Policies. - Samiulla Shaikh; Harit Vishwakarma; Sameep Mehta; Kush R. Varshney; Karthikeyan Natesan Ramamurthy; Dennis Wei (CoRR, 2017)

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

On the Supermodularity of Active Graph-Based Semi-Supervised Learning with Stieltjes Matrix Regularization. - Pin-Yu Chen; Dennis Wei (ICASSP, 2018)

Parallel Bayesian Network Structure Learning. - Tian Gao; Dennis Wei (ICML, 2018)

Boolean Decision Rules via Column Generation. - Sanjeeb Dash; Oktay Günlük; Dennis Wei (NeurIPS, 2018)

Teaching Meaningful Explanations. - Noel C. F. Codella; Michael Hind; Karthikeyan Natesan Ramamurthy; Murray Campbell; Amit Dhurandhar; Kush R. Varshney; Dennis Wei; Aleksandra Mojsilovic (CoRR, 2018)

Data Pre-Processing for Discrimination Prevention: Information-Theoretic Optimization and Analysis. - Flávio du Pin Calmon; Dennis Wei; Bhanukiran Vinzamuri; Karthikeyan Natesan Ramamurthy; Kush R. Varshney (IEEE J. Sel. Top. Signal Process., 2018)

Distribution-preserving k-anonymity. - Dennis Wei; Karthikeyan Natesan Ramamurthy; Kush R. Varshney (Stat. Anal. Data Min., 2018)

Fair Transfer Learning with Missing Protected Attributes. - Amanda Coston; Karthikeyan Natesan Ramamurthy; Dennis Wei; Kush R. Varshney; Skyler Speakman; Zairah Mustahsan; Supriyo Chakraborty (AIES, 2019)

TED: Teaching AI to Explain its Decisions. - Michael Hind; Dennis Wei; Murray Campbell; Noel C. F. Codella; Amit Dhurandhar; Aleksandra Mojsilovic; Karthikeyan Natesan Ramamurthy; Kush R. Varshney (AIES, 2019)

Generalized Linear Rule Models. - Dennis Wei; Sanjeeb Dash; Tian Gao; Oktay Günlük (ICML, 2019)

Teaching AI to Explain its Decisions Using Embeddings and Multi-Task Learning. - Noel C. F. Codella; Michael Hind; Karthikeyan Natesan Ramamurthy; Murray Campbell; Amit Dhurandhar; Kush R. Varshney; Dennis Wei; Aleksandra Mojsilovic (CoRR, 2019)

One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques. - Vijay Arya; Rachel K. E. Bellamy; Pin-Yu Chen; Amit Dhurandhar; Michael Hind; Samuel C. Hoffman; Stephanie Houde; Q. Vera Liao; Ronny Luss; Aleksandra Mojsilovic; Sami Mourad; Pablo Pedemonte; et al. (CoRR, 2019)

An Information-Theoretic Perspective on the Relationship Between Fairness and Accuracy. - Sanghamitra Dutta; Dennis Wei; Hazar Yueksel; Pin-Yu Chen; Sijia Liu 001; Kush R. Varshney (CoRR, 2019)

Characterization of Overlap in Observational Studies. - Michael Oberst; Fredrik D. Johansson; Dennis Wei; Tian Gao; Gabriel A. Brat; David A. Sontag; Kush R. Varshney (AISTATS, 2020)

Optimized Score Transformation for Fair Classification. - Dennis Wei; Karthikeyan Natesan Ramamurthy; Flávio P. Calmon (AISTATS, 2020)

Interpretable subgroup discovery in treatment effect estimation with application to opioid prescribing guidelines. - Chirag Nagpal; Dennis Wei; Bhanukiran Vinzamuri; Monica Shekhar; Sara E. Berger; Subhro Das; Kush R. Varshney (CHIL, 2020)

AI explainability 360: hands-on tutorial. - Vijay Arya; Rachel K. E. Bellamy; Pin-Yu Chen; Amit Dhurandhar; Michael Hind; Samuel C. Hoffman; Stephanie Houde; Q. Vera Liao; Ronny Luss; Aleksandra Mojsilovic; Sami Mourad; Pablo Pedemonte; et al. (FAT*, 2020)

Is There a Trade-Off Between Fairness and Accuracy? A Perspective Using Mismatched Hypothesis Testing. - Sanghamitra Dutta; Dennis Wei; Hazar Yueksel; Pin-Yu Chen; Sijia Liu 001; Kush R. Varshney (ICML, 2020)

Model Projection: Theory and Applications to Fair Machine Learning. - Wael Alghamdi; Shahab Asoodeh; Hao Wang 063; Flávio P. Calmon; Dennis Wei; Karthikeyan Natesan Ramamurthy (ISIT, 2020)

DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networks. - Dennis Wei; Tian Gao; Yue Yu (NeurIPS, 2020)

Consumer-Driven Explanations for Machine Learning Decisions: An Empirical Study of Robustness. - Michael Hind; Dennis Wei; Yunfeng Zhang (CoRR, 2020)

Optimal Policies for the Homogeneous Selective Labels Problem. - Dennis Wei (CoRR, 2020)

AI Explainability 360: An Extensible Toolkit for Understanding Data and Machine Learning Models. - Vijay Arya; Rachel K. E. Bellamy; Pin-Yu Chen; Amit Dhurandhar; Michael Hind; Samuel C. Hoffman; Stephanie Houde; Q. Vera Liao; Ronny Luss; Aleksandra Mojsilovic; Sami Mourad; Pablo Pedemonte; et al. (J. Mach. Learn. Res., 2020)

AI Explainability 360 Toolkit. - Vijay Arya; Rachel K. E. Bellamy; Pin-Yu Chen; Amit Dhurandhar; Michael Hind; Samuel C. Hoffman; Stephanie Houde; Q. Vera Liao; Ronny Luss; Aleksandra Mojsilovic; Sami Mourad; Pablo Pedemonte; et al. (COMAD/CODS, 2021)

Treatment Effect Estimation Using Invariant Risk Minimization. - Abhin Shah; Kartik Ahuja; Karthikeyan Shanmugam; Dennis Wei; Kush R. Varshney; Amit Dhurandhar (ICASSP, 2021)

Decision-Making Under Selective Labels: Optimal Finite-Domain Policies and Beyond. - Dennis Wei (ICML, 2021)

What Changed? Interpretable Model Comparison. - Rahul Nair; Massimiliano Mattetti; Elizabeth Daly; Dennis Wei; Oznur Alkan; Yunfeng Zhang (IJCAI, 2021)

AIMEE: Interactive model maintenance with rule-based surrogates. - Owen Cornec; Rahul Nair; Elizabeth Daly; Oznur Alkan; Dennis Wei (NeurIPS (Competition and Demos), 2021)

CoFrNets: Interpretable Neural Architecture Inspired by Continued Fractions. - Isha Puri; Amit Dhurandhar; Tejaswini Pedapati; Karthikeyan Shanmugam; Dennis Wei; Kush R. Varshney (NeurIPS, 2021)

Conditionally independent data generation. - Kartik Ahuja; Prasanna Sattigeri; Karthikeyan Shanmugam; Dennis Wei; Karthikeyan Natesan Ramamurthy; Murat Kocaoglu (UAI, 2021)

Your fairness may vary: Group fairness of pretrained language models in toxic text classification. - Ioana Baldini; Dennis Wei; Karthikeyan Natesan Ramamurthy; Mikhail Yurochkin; Moninder Singh (CoRR, 2021)

Ground-Truth, Whose Truth? - Examining the Challenges with Annotating Toxic Text Datasets. - Kofi Arhin; Ioana Baldini; Dennis Wei; Karthikeyan Natesan Ramamurthy; Moninder Singh (CoRR, 2021)

Optimized Score Transformation for Consistent Fair Classification. - Dennis Wei; Karthikeyan Natesan Ramamurthy; Flávio P. Calmon (J. Mach. Learn. Res., 2021)

AI Explainability 360: Impact and Design. - Vijay Arya; Rachel K. E. Bellamy; Pin-Yu Chen; Amit Dhurandhar; Michael Hind; Samuel C. Hoffman; Stephanie Houde; Q. Vera Liao; Ronny Luss; Aleksandra Mojsilovic; Sami Mourad; Pablo Pedemonte; et al. (AAAI, 2022)

Your fairness may vary: Pretrained language model fairness in toxic text classification. - Ioana Baldini; Dennis Wei; Karthikeyan Natesan Ramamurthy; Moninder Singh; Mikhail Yurochkin (ACL (Findings), 2022)

Evaluating Fairness of Synthetic Healthcare Data Models. - Karan Bhanot; Ioana Baldini; Dennis Wei; Jiaming Zeng; Kristin P. Bennett (AMIA, 2022)

FROTE: Feedback Rule-Driven Oversampling for Editing Models. - Oznur Alkan; Dennis Wei; Massimiliano Mattetti; Rahul Nair; Elizabeth Daly; Diptikalyan Saha (MLSys, 2022)

On the Safety of Interpretable Machine Learning: A Maximum Deviation Approach. - Dennis Wei; Rahul Nair; Amit Dhurandhar; Kush R. Varshney; Elizabeth Daly; Moninder Singh (NeurIPS, 2022)

Analogies and Feature Attributions for Model Agnostic Explanation of Similarity Learners. - Karthikeyan Natesan Ramamurthy; Amit Dhurandhar; Dennis Wei; Zaid Bin Tariq (CoRR, 2022)

Downstream Fairness Caveats with Synthetic Healthcare Data. - Karan Bhanot; Ioana Baldini; Dennis Wei; Jiaming Zeng; Kristin P. Bennett (CoRR, 2022)

Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making. - Charvi Rastogi; Yunfeng Zhang; Dennis Wei; Kush R. Varshney; Amit Dhurandhar; Richard Tomsett (Proc. ACM Hum. Comput. Interact., 2022)

Stress-Testing Bias Mitigation Algorithms to Understand Fairness Vulnerabilities. - Karan Bhanot; Ioana Baldini; Dennis Wei; Jiaming Zeng; Kristin P. Bennett (AIES, 2023)

Heavy Sets with Applications to Interpretable Machine Learning Diagnostics. - Dmitry M. Malioutov; Sanjeeb Dash; Dennis Wei (AISTATS, 2023)

Who Should Predict? Exact Algorithms For Learning to Defer to Humans. - Hussein Mozannar; Hunter Lang; Dennis Wei; Prasanna Sattigeri; Subhro Das; David A. Sontag (AISTATS, 2023)

Convex Bounds on the Softmax Function with Applications to Robustness Verification. - Dennis Wei; Haoze Wu 001; Min Wu; Pin-Yu Chen; Clark W. Barrett; Eitan Farchi (AISTATS, 2023)

Adversarial Auditing of Machine Learning Models under Compound Shift. - Karan Bhanot; Dennis Wei; Ioana Baldini; Kristin P. Bennett (ESANN, 2023)

Add-Remove-or-Relabel: Practitioner-Friendly Bias Mitigation via Influential Fairness. - Brianna Richardson; Prasanna Sattigeri; Dennis Wei; Karthikeyan Natesan Ramamurthy; Kush R. Varshney; Amit Dhurandhar; Juan E. Gilbert (FAccT, 2023)

A Statistical Interpretation of the Maximum Subarray Problem. - Dennis Wei; Dmitry M. Malioutov (ICASSP, 2023)

Effective Human-AI Teams via Learned Natural Language Rules and Onboarding. - Hussein Mozannar; Jimin J. Lee; Dennis Wei; Prasanna Sattigeri; Subhro Das; David A. Sontag (NeurIPS, 2023)

Interpretable differencing of machine learning models. - Swagatam Haldar; Diptikalyan Saha; Dennis Wei; Rahul Nair; Elizabeth M. Daly (UAI, 2023)

Interpretable and Fair Boolean Rule Sets via Column Generation. - Connor Lawless; Sanjeeb Dash; Oktay Günlük; Dennis Wei (J. Mach. Learn. Res., 2023)

Causal Bandits with General Causal Models and Interventions. - Zirui Yan; Dennis Wei; Dmitriy A. Katz; Prasanna Sattigeri; Ali Tajer (AISTATS, 2024)

SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation. - Chongyu Fan; Jiancheng Liu; Yihua Zhang; Eric Wong 001; Dennis Wei; Sijia Liu 001 (ICLR, 2024)

Trust Regions for Explanations via Black-Box Probabilistic Certification. - Amit Dhurandhar; Swagatam Haldar; Dennis Wei; Karthikeyan Natesan Ramamurthy (ICML, 2024)

Using Causal Inference to Investigate Contraceptive Discontinuation in Sub-Saharan Africa. - Victor Akinwande; Megan MacGregor; Celia Cintas; Ehud Karavani; Dennis Wei; Kush R. Varshney; Pablo A. Nepomnaschy (IJCAI, 2024)

Selective Explanations. - Lucas Monteiro Paes; Dennis Wei; Flávio P. Calmon (NeurIPS, 2024)

Interventional Causal Discovery in a Mixture of DAGs. - Burak Varici; Dmitriy Katz; Dennis Wei; Prasanna Sattigeri; Ali Tajer (NeurIPS, 2024)

Detectors for Safe and Reliable LLMs: Implementations, Uses, and Limitations. - Swapnaja Achintalwar; Adriana Alvarado Garcia; Ateret Anaby-Tavor; Ioana Baldini; Sara E. Berger; Bishwaranjan Bhattacharjee; Djallel Bouneffouf 001; Subhajit Chaudhury; Pin-Yu Chen; Lamogha Chiazor; Elizabeth M. Daly; Rogério Abreu de Paula; et al. (CoRR, 2024)

Facilitating Human-LLM Collaboration through Factuality Scores and Source Attributions. - Hyo Jin Do; Rachel Ostrand; Justin D. Weisz; Casey Dugan; Prasanna Sattigeri; Dennis Wei; Keerthiram Murugesan; Werner Geyer (CoRR, 2024)

Split, Unlearn, Merge: Leveraging Data Attributes for More Effective Unlearning in LLMs. - Swanand Ravindra Kadhe; Farhan Ahmed; Dennis Wei; Nathalie Baracaldo; Inkit Padhi (CoRR, 2024)

Identifying Sub-networks in Neural Networks via Functionally Similar Representations. - Tian Gao 007; Amit Dhurandhar; Karthikeyan Natesan Ramamurthy; Dennis Wei (CoRR, 2024)

Final-Model-Only Data Attribution with a Unifying View of Gradient-Based Methods. - Dennis Wei; Inkit Padhi; Soumya Ghosh; Amit Dhurandhar; Karthikeyan Natesan Ramamurthy; Maria Chang 001 (CoRR, 2024)

Separability Analysis for Causal Discovery in Mixture of DAGs. - Burak Varici; Dmitriy Katz; Dennis Wei; Prasanna Sattigeri; Ali Tajer (Trans. Mach. Learn. Res., 2024)

Multi-Level Explanations for Generative Language Models. - Lucas Monteiro Paes; Dennis Wei; Hyo Jin Do; Hendrik Strobelt; Ronny Luss; Amit Dhurandhar; Manish Nagireddy; Karthikeyan Natesan Ramamurthy; Prasanna Sattigeri; Werner Geyer; Soumya Ghosh (ACL (1), 2025)

The RealHumanEval: Evaluating Large Language Models' Abilities to Support Programmers. - Hussein Mozannar; Valerie Chen; Mohammed Alsobay; Subhro Das; Sebastian Zhao; Dennis Wei; Manish Nagireddy; Prasanna Sattigeri; Ameet Talwalkar; David A. Sontag (Trans. Mach. Learn. Res., 2025)

Agentic AI Needs a Systems Theory. - Erik Miehling; Karthikeyan Natesan Ramamurthy; Kush R. Varshney; Matthew Riemer; Djallel Bouneffouf 001; John T. Richards; Amit Dhurandhar; Elizabeth M. Daly; Michael Hind; Prasanna Sattigeri; Dennis Wei; Ambrish Rawat; et al. (CoRR, 2025)

Invariance Makes LLM Unlearning Resilient Even to Unanticipated Downstream Fine-Tuning. - Changsheng Wang; Yihua Zhang; Jinghan Jia; Parikshit Ram; Dennis Wei; Yuguang Yao; Soumyadeep Pal; Nathalie Baracaldo; Sijia Liu 001 (CoRR, 2025)

CoFrNets: Interpretable Neural Architecture Inspired by Continued Fractions. - Isha Puri; Amit Dhurandhar; Tejaswini Pedapati; Kartikeyan Shanmugam; Dennis Wei; Kush R. Varshney (CoRR, 2025)

Reasoning Model Unlearning: Forgetting Traces, Not Just Answers, While Preserving Reasoning Skills. - Changsheng Wang; Chongyu Fan; Yihua Zhang; Jinghan Jia; Dennis Wei; Parikshit Ram; Nathalie Baracaldo; Sijia Liu 001 (CoRR, 2025)

Highlight All the Phrases: Enhancing LLM Transparency through Visual Factuality Indicators. - Hyo Jin Do; Rachel Ostrand; Werner Geyer; Keerthiram Murugesan; Dennis Wei; Justin D. Weisz (CoRR, 2025)