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2020 – today
- 2024
[j12]Burak Varici, Dmitriy Katz, Dennis Wei, Prasanna Sattigeri, Ali Tajer:
Separability Analysis for Causal Discovery in Mixture of DAGs. Trans. Mach. Learn. Res. 2024 (2024)- [c52]Zirui Yan, Dennis Wei, Dmitriy A. Katz, Prasanna Sattigeri, Ali Tajer:
Causal Bandits with General Causal Models and Interventions. AISTATS 2024: 4609-4617 - [c51]Chongyu Fan, Jiancheng Liu, Yihua Zhang, Eric Wong
, Dennis Wei, Sijia Liu:
SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation. ICLR 2024 - [c50]Amit Dhurandhar, Swagatam Haldar, Dennis Wei, Karthikeyan Natesan Ramamurthy:
Trust Regions for Explanations via Black-Box Probabilistic Certification. ICML 2024 - [c49]Victor Akinwande, Megan MacGregor, Celia Cintas, Ehud Karavani, Dennis Wei, Kush R. Varshney, Pablo A. Nepomnaschy:
Using Causal Inference to Investigate Contraceptive Discontinuation in Sub-Saharan Africa. IJCAI 2024: 7161-7169 - [i46]Amit Dhurandhar, Swagatam Haldar, Dennis Wei, Karthikeyan Natesan Ramamurthy:
Trust Regions for Explanations via Black-Box Probabilistic Certification. CoRR abs/2402.11168 (2024) - [i45]Zirui Yan, Dennis Wei, Dmitriy A. Katz-Rogozhnikov, Prasanna Sattigeri, Ali Tajer:
Causal Bandits with General Causal Models and Interventions. CoRR abs/2403.00233 (2024) - [i44]Swapnaja Achintalwar, Adriana Alvarado Garcia, Ateret Anaby-Tavor, Ioana Baldini, Sara E. Berger, Bishwaranjan Bhattacharjee, Djallel Bouneffouf, Subhajit Chaudhury, Pin-Yu Chen, Lamogha Chiazor, Elizabeth M. Daly, Rogério Abreu de Paula, Pierre L. Dognin, Eitan Farchi, Soumya Ghosh, Michael Hind, Raya Horesh, George Kour, Ja Young Lee, Erik Miehling, Keerthiram Murugesan, Manish Nagireddy, Inkit Padhi, David Piorkowski, Ambrish Rawat, Orna Raz, Prasanna Sattigeri, Hendrik Strobelt, Sarathkrishna Swaminathan, Christoph Tillmann, Aashka Trivedi, Kush R. Varshney, Dennis Wei, Shalisha Witherspoon, Marcel Zalmanovici:
Detectors for Safe and Reliable LLMs: Implementations, Uses, and Limitations. CoRR abs/2403.06009 (2024) - [i43]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:
Multi-Level Explanations for Generative Language Models. CoRR abs/2403.14459 (2024) - [i42]Hussein Mozannar, Valerie Chen, Mohammed Alsobay, Subhro Das, Sebastian Zhao, Dennis Wei, Manish Nagireddy, Prasanna Sattigeri, Ameet Talwalkar, David A. Sontag:
The RealHumanEval: Evaluating Large Language Models' Abilities to Support Programmers. CoRR abs/2404.02806 (2024) - [i41]Lucas Monteiro Paes, Dennis Wei, Flávio P. Calmon:
Selective Explanations. CoRR abs/2405.19562 (2024) - [i40]Hyo Jin Do, Rachel Ostrand, Justin D. Weisz, Casey Dugan, Prasanna Sattigeri, Dennis Wei, Keerthiram Murugesan, Werner Geyer:
Facilitating Human-LLM Collaboration through Factuality Scores and Source Attributions. CoRR abs/2405.20434 (2024) - [i39]Burak Varici, Dmitriy A. Katz-Rogozhnikov, Dennis Wei, Prasanna Sattigeri, Ali Tajer:
Interventional Causal Discovery in a Mixture of DAGs. CoRR abs/2406.08666 (2024) - [i38]Swanand Ravindra Kadhe, Farhan Ahmed, Dennis Wei, Nathalie Baracaldo, Inkit Padhi:
Split, Unlearn, Merge: Leveraging Data Attributes for More Effective Unlearning in LLMs. CoRR abs/2406.11780 (2024) - [i37]Tian Gao, Amit Dhurandhar, Karthikeyan Natesan Ramamurthy, Dennis Wei:
Identifying Sub-networks in Neural Networks via Functionally Similar Representations. CoRR abs/2410.16484 (2024) - 2023
- [j11]Connor Lawless, Sanjeeb Dash, Oktay Günlük, Dennis Wei:
Interpretable and Fair Boolean Rule Sets via Column Generation. J. Mach. Learn. Res. 24: 229:1-229:50 (2023) - [c48]Karan Bhanot, Ioana Baldini, Dennis Wei, Jiaming Zeng, Kristin P. Bennett:
Stress-Testing Bias Mitigation Algorithms to Understand Fairness Vulnerabilities. AIES 2023: 764-774 - [c47]Dmitry M. Malioutov, Sanjeeb Dash, Dennis Wei:
Heavy Sets with Applications to Interpretable Machine Learning Diagnostics. AISTATS 2023: 5918-5930 - [c46]Dennis Wei, Haoze Wu, Min Wu, Pin-Yu Chen, Clark W. Barrett, Eitan Farchi:
Convex Bounds on the Softmax Function with Applications to Robustness Verification. AISTATS 2023: 6853-6878 - [c45]Hussein Mozannar, Hunter Lang, Dennis Wei, Prasanna Sattigeri, Subhro Das, David A. Sontag:
Who Should Predict? Exact Algorithms For Learning to Defer to Humans. AISTATS 2023: 10520-10545 - [c44]Karan Bhanot, Dennis Wei, Ioana Baldini, Kristin P. Bennett:
Adversarial Auditing of Machine Learning Models under Compound Shift. ESANN 2023 - [c43]Brianna Richardson, Prasanna Sattigeri, Dennis Wei, Karthikeyan Natesan Ramamurthy, Kush R. Varshney, Amit Dhurandhar, Juan E. Gilbert:
Add-Remove-or-Relabel: Practitioner-Friendly Bias Mitigation via Influential Fairness. FAccT 2023: 736-752 - [c42]Dennis Wei, Dmitry M. Malioutov:
A Statistical Interpretation of the Maximum Subarray Problem. ICASSP 2023: 1-5 - [c41]Hussein Mozannar, Jimin J. Lee, Dennis Wei, Prasanna Sattigeri, Subhro Das, David A. Sontag:
Effective Human-AI Teams via Learned Natural Language Rules and Onboarding. NeurIPS 2023 - [c40]Swagatam Haldar, Diptikalyan Saha, Dennis Wei, Rahul Nair, Elizabeth M. Daly:
Interpretable differencing of machine learning models. UAI 2023: 788-797 - [i36]Hussein Mozannar, Hunter Lang, Dennis Wei, Prasanna Sattigeri, Subhro Das, David A. Sontag:
Who Should Predict? Exact Algorithms For Learning to Defer to Humans. CoRR abs/2301.06197 (2023) - [i35]Dennis Wei, Haoze Wu, Min Wu, Pin-Yu Chen, Clark W. Barrett, Eitan Farchi:
Convex Bounds on the Softmax Function with Applications to Robustness Verification. CoRR abs/2303.01713 (2023) - [i34]Swagatam Haldar, Diptikalyan Saha, Dennis Wei, Rahul Nair, Elizabeth M. Daly:
Interpretable Differencing of Machine Learning Models. CoRR abs/2306.06473 (2023) - [i33]Chongyu Fan, Jiancheng Liu, Yihua Zhang, Dennis Wei, Eric Wong, Sijia Liu:
SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation. CoRR abs/2310.12508 (2023) - [i32]Hussein Mozannar, Jimin J. Lee, Dennis Wei, Prasanna Sattigeri, Subhro Das, David A. Sontag:
Effective Human-AI Teams via Learned Natural Language Rules and Onboarding. CoRR abs/2311.01007 (2023) - 2022
- [j10]Charvi Rastogi, Yunfeng Zhang, Dennis Wei, Kush R. Varshney, Amit Dhurandhar, Richard Tomsett:
Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making. Proc. ACM Hum. Comput. Interact. 6(CSCW1): 83:1-83:22 (2022) - [c39]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, Ramya Raghavendra, John T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang:
AI Explainability 360: Impact and Design. AAAI 2022: 12651-12657 - [c38]Ioana Baldini, Dennis Wei, Karthikeyan Natesan Ramamurthy, Moninder Singh, Mikhail Yurochkin:
Your fairness may vary: Pretrained language model fairness in toxic text classification. ACL (Findings) 2022: 2245-2262 - [c37]Karan Bhanot, Ioana Baldini, Dennis Wei, Jiaming Zeng, Kristin P. Bennett:
Evaluating Fairness of Synthetic Healthcare Data Models. AMIA 2022 - [c36]Oznur Alkan, Dennis Wei, Massimiliano Mattetti, Rahul Nair, Elizabeth Daly, Diptikalyan Saha:
FROTE: Feedback Rule-Driven Oversampling for Editing Models. MLSys 2022 - [c35]Dennis Wei, Rahul Nair, Amit Dhurandhar, Kush R. Varshney, Elizabeth Daly, Moninder Singh:
On the Safety of Interpretable Machine Learning: A Maximum Deviation Approach. NeurIPS 2022 - [i31]Öznur Alkan, Dennis Wei, Massimiliano Mattetti, Rahul Nair, Elizabeth M. Daly, Diptikalyan Saha:
FROTE: Feedback Rule-Driven Oversampling for Editing Models. CoRR abs/2201.01070 (2022) - [i30]Karthikeyan Natesan Ramamurthy, Amit Dhurandhar, Dennis Wei, Zaid Bin Tariq:
Analogies and Feature Attributions for Model Agnostic Explanation of Similarity Learners. CoRR abs/2202.01153 (2022) - [i29]Karan Bhanot, Ioana Baldini, Dennis Wei, Jiaming Zeng, Kristin P. Bennett:
Downstream Fairness Caveats with Synthetic Healthcare Data. CoRR abs/2203.04462 (2022) - [i28]Dennis Wei, Rahul Nair, Amit Dhurandhar, Kush R. Varshney, Elizabeth M. Daly, Moninder Singh:
On the Safety of Interpretable Machine Learning: A Maximum Deviation Approach. CoRR abs/2211.01498 (2022) - 2021
- [j9]Dennis Wei, Karthikeyan Natesan Ramamurthy, Flávio P. Calmon:
Optimized Score Transformation for Consistent Fair Classification. J. Mach. Learn. Res. 22: 258:1-258:78 (2021) - [c34]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, Ramya Raghavendra, John T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang:
AI Explainability 360 Toolkit. COMAD/CODS 2021: 376-379 - [c33]Abhin Shah, Kartik Ahuja, Karthikeyan Shanmugam, Dennis Wei, Kush R. Varshney, Amit Dhurandhar:
Treatment Effect Estimation Using Invariant Risk Minimization. ICASSP 2021: 5005-5009 - [c32]Dennis Wei:
Decision-Making Under Selective Labels: Optimal Finite-Domain Policies and Beyond. ICML 2021: 11035-11046 - [c31]Rahul Nair, Massimiliano Mattetti, Elizabeth Daly, Dennis Wei, Oznur Alkan, Yunfeng Zhang:
What Changed? Interpretable Model Comparison. IJCAI 2021: 2855-2861 - [c30]Owen Cornec, Rahul Nair, Elizabeth Daly, Oznur Alkan, Dennis Wei:
AIMEE: Interactive model maintenance with rule-based surrogates. NeurIPS (Competition and Demos) 2021: 288-291 - [c29]Isha Puri, Amit Dhurandhar, Tejaswini Pedapati, Karthikeyan Shanmugam, Dennis Wei, Kush R. Varshney:
CoFrNets: Interpretable Neural Architecture Inspired by Continued Fractions. NeurIPS 2021: 21668-21680 - [c28]Kartik Ahuja, Prasanna Sattigeri, Karthikeyan Shanmugam, Dennis Wei, Karthikeyan Natesan Ramamurthy, Murat Kocaoglu:
Conditionally independent data generation. UAI 2021: 2050-2060 - [i27]Abhin Shah, Kartik Ahuja, Karthikeyan Shanmugam, Dennis Wei, Kush R. Varshney, Amit Dhurandhar:
Treatment Effect Estimation using Invariant Risk Minimization. CoRR abs/2103.07788 (2021) - [i26]Ioana Baldini, Dennis Wei, Karthikeyan Natesan Ramamurthy, Mikhail Yurochkin, Moninder Singh:
Your fairness may vary: Group fairness of pretrained language models in toxic text classification. CoRR abs/2108.01250 (2021) - [i25]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, Ramya Raghavendra, John T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang:
AI Explainability 360: Impact and Design. CoRR abs/2109.12151 (2021) - [i24]Connor Lawless, Sanjeeb Dash, Oktay Günlük, Dennis Wei:
Interpretable and Fair Boolean Rule Sets via Column Generation. CoRR abs/2111.08466 (2021) - [i23]Kofi Arhin, Ioana Baldini, Dennis Wei, Karthikeyan Natesan Ramamurthy, Moninder Singh:
Ground-Truth, Whose Truth? - Examining the Challenges with Annotating Toxic Text Datasets. CoRR abs/2112.03529 (2021) - 2020
- [j8]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, Ramya Raghavendra, John T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang:
AI Explainability 360: An Extensible Toolkit for Understanding Data and Machine Learning Models. J. Mach. Learn. Res. 21: 130:1-130:6 (2020) - [c27]Michael Oberst, Fredrik D. Johansson, Dennis Wei, Tian Gao, Gabriel A. Brat, David A. Sontag, Kush R. Varshney:
Characterization of Overlap in Observational Studies. AISTATS 2020: 788-798 - [c26]Dennis Wei, Karthikeyan Natesan Ramamurthy, Flávio P. Calmon:
Optimized Score Transformation for Fair Classification. AISTATS 2020: 1673-1683 - [c25]Chirag Nagpal, Dennis Wei, Bhanukiran Vinzamuri, Monica Shekhar, Sara E. Berger, Subhro Das, Kush R. Varshney:
Interpretable subgroup discovery in treatment effect estimation with application to opioid prescribing guidelines. CHIL 2020: 19-29 - [c24]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, Ramya Raghavendra, John T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang:
AI explainability 360: hands-on tutorial. FAT* 2020: 696 - [c23]Sanghamitra Dutta, Dennis Wei, Hazar Yueksel, Pin-Yu Chen, Sijia Liu, Kush R. Varshney:
Is There a Trade-Off Between Fairness and Accuracy? A Perspective Using Mismatched Hypothesis Testing. ICML 2020: 2803-2813 - [c22]Wael Alghamdi, Shahab Asoodeh, Hao Wang, Flávio P. Calmon, Dennis Wei, Karthikeyan Natesan Ramamurthy:
Model Projection: Theory and Applications to Fair Machine Learning. ISIT 2020: 2711-2716 - [c21]Dennis Wei, Tian Gao, Yue Yu:
DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networks. NeurIPS 2020 - [i22]Michael Hind, Dennis Wei, Yunfeng Zhang:
Consumer-Driven Explanations for Machine Learning Decisions: An Empirical Study of Robustness. CoRR abs/2001.05573 (2020) - [i21]Charvi Rastogi, Yunfeng Zhang, Dennis Wei, Kush R. Varshney, Amit Dhurandhar, Richard Tomsett:
Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making. CoRR abs/2010.07938 (2020) - [i20]Dennis Wei, Tian Gao, Yue Yu:
DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networks. CoRR abs/2010.09133 (2020) - [i19]Dennis Wei:
Optimal Policies for the Homogeneous Selective Labels Problem. CoRR abs/2011.01381 (2020)
2010 – 2019
- 2019
- [c20]Amanda Coston, Karthikeyan Natesan Ramamurthy, Dennis Wei, Kush R. Varshney, Skyler Speakman, Zairah Mustahsan, Supriyo Chakraborty:
Fair Transfer Learning with Missing Protected Attributes. AIES 2019: 91-98 - [c19]Michael Hind, Dennis Wei, Murray Campbell, Noel C. F. Codella, Amit Dhurandhar, Aleksandra Mojsilovic, Karthikeyan Natesan Ramamurthy, Kush R. Varshney:
TED: Teaching AI to Explain its Decisions. AIES 2019: 123-129 - [c18]Dennis Wei, Sanjeeb Dash, Tian Gao, Oktay Günlük:
Generalized Linear Rule Models. ICML 2019: 6687-6696 - [i18]Chirag Nagpal, Dennis Wei, Bhanukiran Vinzamuri, Monica Shekhar, Sara E. Berger, Subhro Das, Kush R. Varshney:
Interpretable Subgroup Discovery in Treatment Effect Estimation with Application to Opioid Prescribing Guidelines. CoRR abs/1905.03297 (2019) - [i17]Dennis Wei, Karthikeyan Natesan Ramamurthy, Flávio du Pin Calmon:
Optimized Score Transformation for Fair Classification. CoRR abs/1906.00066 (2019) - [i16]Dennis Wei, Sanjeeb Dash, Tian Gao, Oktay Günlük:
Generalized Linear Rule Models. CoRR abs/1906.01761 (2019) - [i15]Noel C. F. Codella, Michael Hind, Karthikeyan Natesan Ramamurthy, Murray Campbell, Amit Dhurandhar, Kush R. Varshney, Dennis Wei, Aleksandra Mojsilovic:
Teaching AI to Explain its Decisions Using Embeddings and Multi-Task Learning. CoRR abs/1906.02299 (2019) - [i14]Fredrik D. Johansson, Dennis Wei, Michael Oberst, Tian Gao, Gabriel A. Brat, David A. Sontag, Kush R. Varshney:
Characterization of Overlap in Observational Studies. CoRR abs/1907.04138 (2019) - [i13]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, Ramya Raghavendra, John T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang:
One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques. CoRR abs/1909.03012 (2019) - [i12]Sanghamitra Dutta, Dennis Wei, Hazar Yueksel, Pin-Yu Chen, Sijia Liu, Kush R. Varshney:
An Information-Theoretic Perspective on the Relationship Between Fairness and Accuracy. CoRR abs/1910.07870 (2019) - 2018
- [j7]Flávio du Pin Calmon, Dennis Wei, Bhanukiran Vinzamuri, Karthikeyan Natesan Ramamurthy, Kush R. Varshney:
Data Pre-Processing for Discrimination Prevention: Information-Theoretic Optimization and Analysis. IEEE J. Sel. Top. Signal Process. 12(5): 1106-1119 (2018) - [j6]Dennis Wei, Karthikeyan Natesan Ramamurthy, Kush R. Varshney:
Distribution-preserving k-anonymity. Stat. Anal. Data Min. 11(6): 253-270 (2018) - [c17]Pin-Yu Chen, Dennis Wei:
On the Supermodularity of Active Graph-Based Semi-Supervised Learning with Stieltjes Matrix Regularization. ICASSP 2018: 2801-2805 - [c16]Tian Gao, Dennis Wei:
Parallel Bayesian Network Structure Learning. ICML 2018: 1671-1680 - [c15]Sanjeeb Dash, Oktay Günlük, Dennis Wei:
Boolean Decision Rules via Column Generation. NeurIPS 2018: 4660-4670 - [i11]Pin-Yu Chen, Dennis Wei:
On the Supermodularity of Active Graph-based Semi-supervised Learning with Stieltjes Matrix Regularization. CoRR abs/1804.03273 (2018) - [i10]Sanjeeb Dash, Oktay Günlük, Dennis Wei:
Boolean Decision Rules via Column Generation. CoRR abs/1805.09901 (2018) - [i9]Noel C. F. Codella, Michael Hind, Karthikeyan Natesan Ramamurthy, Murray Campbell, Amit Dhurandhar, Kush R. Varshney, Dennis Wei, Aleksandra Mojsilovic:
Teaching Meaningful Explanations. CoRR abs/1805.11648 (2018) - [i8]Noel C. F. Codella, Michael Hind, Karthikeyan Natesan Ramamurthy, Murray Campbell, Amit Dhurandhar, Kush R. Varshney, Dennis Wei, Aleksandra Mojsilovic:
TED: Teaching AI to Explain its Decisions. CoRR abs/1811.04896 (2018) - 2017
- [j5]Dennis Wei:
k-quantiles: L1 distance clustering under a sum constraint. Pattern Recognit. Lett. 92: 49-55 (2017) - [c14]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:
A configurable, big data system for on-demand healthcare cost prediction. IEEE BigData 2017: 1524-1533 - [c13]Flávio P. Calmon, Dennis Wei, Bhanukiran Vinzamuri, Karthikeyan Natesan Ramamurthy, Kush R. Varshney:
Optimized Pre-Processing for Discrimination Prevention. NIPS 2017: 3992-4001 - [i7]Flávio du Pin Calmon, Dennis Wei, Karthikeyan Natesan Ramamurthy, Kush R. Varshney:
Optimized Data Pre-Processing for Discrimination Prevention. CoRR abs/1704.03354 (2017) - [i6]Samiulla Shaikh, Harit Vishwakarma, Sameep Mehta, Kush R. Varshney, Karthikeyan Natesan Ramamurthy, Dennis Wei:
An End-To-End Machine Learning Pipeline That Ensures Fairness Policies. CoRR abs/1710.06876 (2017) - [i5]Dennis Wei, Karthikeyan Natesan Ramamurthy, Kush R. Varshney:
Distribution-Preserving k-Anonymity. CoRR abs/1711.01514 (2017) - 2016
- [c12]Alan Wisler, Visar Berisha, Dennis Wei, Karthikeyan Ramamurthy, Andreas Spanias:
Empirically-estimable multi-class classification bounds. ICASSP 2016: 2594-2598 - [c11]Guolong Su, Dennis Wei, Kush R. Varshney, Dmitry M. Malioutov:
Learning sparse two-level boolean rules. MLSP 2016: 1-6 - [c10]Dennis Wei:
A Constant-Factor Bi-Criteria Approximation Guarantee for k-means++. NIPS 2016: 604-612 - [i4]Dennis Wei:
A Constant-Factor Bi-Criteria Approximation Guarantee for $k$-means++. CoRR abs/1605.04986 (2016) - [i3]Guolong Su, Dennis Wei, Kush R. Varshney, Dmitry M. Malioutov:
Interpretable Two-level Boolean Rule Learning for Classification. CoRR abs/1606.05798 (2016) - 2015
- [j4]Kush R. Varshney, Dennis Wei, Karthikeyan Natesan Ramamurthy, Aleksandra Mojsilovic:
Data Challenges in Disease Response: The 2014 Ebola Outbreak and Beyond. ACM J. Data Inf. Qual. 6(2-3): 5:1-5:3 (2015) - [c9]Dennis Wei, Kush R. Varshney, Marcy Wagman:
Optigrow: People Analytics for Job Transfers. BigData Congress 2015: 535-542 - [c8]Dennis Wei, Kush R. Varshney:
Robust binary hypothesis testing under contaminated likelihoods. ICASSP 2015: 3407-3411 - [c7]Dennis Wei:
Adaptive sensing resource allocation over multiple hypothesis tests. ICASSP 2015: 3526-3530 - [c6]Dmitriy A. Katz-Rogozhnikov, Dennis Wei, Gigi Y. Yuen-Reed, Karthikeyan Natesan Ramamurthy, Aleksandra Mojsilovic:
Toward Comprehensive Attribution of Healthcare Cost Changes. ICDM Workshops 2015: 146-155 - [c5]Dennis Wei, Karthikeyan Natesan Ramamurthy, Kush R. Varshney:
Health Insurance Market Risk Assessment: Covariate Shift and k-Anonymity. SDM 2015: 226-234 - [i2]Guolong Su, Dennis Wei, Kush R. Varshney, Dmitry M. Malioutov:
Interpretable Two-level Boolean Rule Learning for Classification. CoRR abs/1511.07361 (2015) - 2014
- [c4]Dennis Wei, Karthikeyan Natesan Ramamurthy, Dmitriy A. Katz-Rogozhnikov, Aleksandra Mojsilovic:
Multiplicative regression via constrained least squares. SSP 2014: 304-307 - [i1]Dennis Wei, Kush R. Varshney:
Robust Binary Hypothesis Testing Under Contaminated Likelihoods. CoRR abs/1410.0952 (2014) - 2013
- [j3]Dennis Wei, Charles K. Sestok, Alan V. Oppenheim:
Sparse Filter Design Under a Quadratic Constraint: Low-Complexity Algorithms. IEEE Trans. Signal Process. 61(4): 857-870 (2013) - [j2]Dennis Wei, Alan V. Oppenheim:
A Branch-and-Bound Algorithm for Quadratically-Constrained Sparse Filter Design. IEEE Trans. Signal Process. 61(4): 1006-1018 (2013) - 2011
- [b1]Dennis Wei:
Design of discrete-time filters for efficient implementation. Massachusetts Institute of Technology, Cambridge, MA, USA, 2011 - [c3]Dennis Wei, Petros Boufounos:
Saturation-robust SAR image formation. ICASSP 2011: 1385-1388 - 2010
- [j1]Thomas A. Baran, Dennis Wei, Alan V. Oppenheim:
Linear programming algorithms for sparse filter design. IEEE Trans. Signal Process. 58(3): 1605-1617 (2010) - [c2]Dennis Wei, Alan V. Oppenheim:
Sparsity maximization under a quadratic constraint with applications in filter design. ICASSP 2010: 3686-3689 - [c1]Wei Hong, Dennis Wei, Aziz Umit Batur:
Video stabilization and rolling shutter distortion reduction. ICIP 2010: 3501-3504
Coauthor Index
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last updated on 2024-11-27 21:23 CET by the dblp team
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