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Muhammad Bilal Zafar
Person information
- affiliation: Ruhr University Bochum, Germany
- affiliation (former): Bosch Center for Artificial Intelligence, Renningen, Germany
- affiliation (former): Max Planck Institute for Software Systems (MPI-SWS), Saarbrücken, Germany
- affiliation (PhD 2019): Saarland University, Saarbrücken, Germany
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2020 – today
- 2025
- [c27]Elisabeth Kirsten, Ivan Habernal, Vedant Nanda, Muhammad Bilal Zafar:
The Impact of Inference Acceleration on Bias of LLMs. NAACL (Long Papers) 2025: 1834-1853 - [i25]Zahra Dehghanighobadi, Asja Fischer, Muhammad Bilal Zafar:
Can LLMs Explain Themselves Counterfactually? CoRR abs/2502.18156 (2025) - [i24]Umang Bhatt, Sanyam Kapoor, Mihir Upadhyay, Ilia Sucholutsky, Francesco Quinzan, Katherine M. Collins, Adrian Weller, Andrew Gordon Wilson, Muhammad Bilal Zafar:
When Should We Orchestrate Multiple Agents? CoRR abs/2503.13577 (2025) - 2024
- [c26]Martin Schäf
, Berk Çirisci
Understanding Developer-Analyzer Interactions in Code Reviews. ASE 2024: 1945-1955 - [c25]Ben Snyder
On Early Detection of Hallucinations in Factual Question Answering. KDD 2024: 2721-2732 - [i23]Pola Schwöbel, Luca Franceschi, Muhammad Bilal Zafar, Keerthan Vasist, Aman Malhotra, Tomer Shenhar, Pinal Tailor, Pinar Yilmaz, Michael Diamond, Michele Donini:
Evaluating Large Language Models with fmeval. CoRR abs/2407.12872 (2024) - [i22]Elisabeth Kirsten, Ivan Habernal, Vedant Nanda, Muhammad Bilal Zafar:
The Impact of Inference Acceleration Strategies on Bias of LLMs. CoRR abs/2410.22118 (2024) - 2023
- [c24]Matthäus Kleindessner, Michele Donini, Chris Russell, Muhammad Bilal Zafar:
Efficient fair PCA for fair representation learning. AISTATS 2023: 5250-5270 - [c23]Mia C. Mayer
Hands-on Tutorial: "Explanations in AI: Methods, Stakeholders and Pitfalls". KDD 2023: 5783-5785 - [c22]Luca Franceschi, Cemre Zor
Explaining Multiclass Classifiers with Categorical Values: A Case Study in Radiography. TML4H 2023: 11-24 - [i21]Matthäus Kleindessner, Michele Donini, Chris Russell
Efficient fair PCA for fair representation learning. CoRR abs/2302.13319 (2023) - [i20]Ben Snyder, Marius Moisescu, Muhammad Bilal Zafar:
On Early Detection of Hallucinations in Factual Question Answering. CoRR abs/2312.14183 (2023) - 2022
- [c21]Matthäus Kleindessner, Samira Samadi, Muhammad Bilal Zafar, Krishnaram Kenthapadi, Chris Russell:
Pairwise Fairness for Ordinal Regression. AISTATS 2022: 3381-3417 - [c20]Yigitcan Kaya, Muhammad Bilal Zafar, Sergül Aydöre, Nathalie Rauschmayr, Krishnaram Kenthapadi:
Generating Distributional Adversarial Examples to Evade Statistical Detectors. ICML 2022: 10895-10911 - [c19]David Nigenda, Zohar Karnin, Muhammad Bilal Zafar, Raghu Ramesha, Alan Tan, Michele Donini, Krishnaram Kenthapadi:
Amazon SageMaker Model Monitor: A System for Real-Time Insights into Deployed Machine Learning Models. KDD 2022: 3671-3681 - [i19]Déborah Sulem, Michele Donini, Muhammad Bilal Zafar, Francois-Xavier Aubet, Jan Gasthaus, Tim Januschowski, Sanjiv Das, Krishnaram Kenthapadi, Cédric Archambeau:
Diverse Counterfactual Explanations for Anomaly Detection in Time Series. CoRR abs/2203.11103 (2022) - [i18]Parantapa Bhattacharya, Saptarshi Ghosh, Muhammad Bilal Zafar, Soumya K. Ghosh, Niloy Ganguly:
What You Like: Generating Explainable Topical Recommendations for Twitter Using Social Annotations. CoRR abs/2212.13897 (2022) - 2021
- [c18]Muhammad Bilal Zafar, Michele Donini, Dylan Slack, Cédric Archambeau, Sanjiv Das, Krishnaram Kenthapadi:
On the Lack of Robust Interpretability of Neural Text Classifiers. ACL/IJCNLP (Findings) 2021: 3730-3740 - [c17]Valerio Perrone, Michele Donini, Muhammad Bilal Zafar, Robin Schmucker, Krishnaram Kenthapadi, Cédric Archambeau:
Fair Bayesian Optimization. AIES 2021: 854-863 - [c16]Michaela Hardt, Xiaoguang Chen, Xiaoyi Cheng, Michele Donini, Jason Gelman, Satish Gollaprolu, John He, Pedro Larroy, Xinyu Liu, Nick McCarthy, Ashish Rathi, Scott Rees, Amaresh Ankit Siva, ErhYuan Tsai, Keerthan Vasist, Pinar Yilmaz, Muhammad Bilal Zafar, Sanjiv Das, Kevin Haas, Tyler Hill, Krishnaram Kenthapadi:
Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability in the Cloud. KDD 2021: 2974-2983 - [i17]Matthäus Kleindessner, Samira Samadi, Muhammad Bilal Zafar, Krishnaram Kenthapadi, Chris Russell:
Pairwise Fairness for Ordinal Regression. CoRR abs/2105.03153 (2021) - [i16]Junaid Ali, Muhammad Bilal Zafar, Adish Singla, Krishna P. Gummadi:
Loss-Aversively Fair Classification. CoRR abs/2105.04273 (2021) - [i15]Muhammad Bilal Zafar, Michele Donini, Dylan Slack, Cédric Archambeau, Sanjiv Das, Krishnaram Kenthapadi:
On the Lack of Robust Interpretability of Neural Text Classifiers. CoRR abs/2106.04631 (2021) - [i14]Robin Schmucker, Michele Donini, Muhammad Bilal Zafar, David Salinas, Cédric Archambeau:
Multi-objective Asynchronous Successive Halving. CoRR abs/2106.12639 (2021) - [i13]Umang Bhatt, Isabel Chien, Muhammad Bilal Zafar, Adrian Weller:
DIVINE: Diverse Influential Training Points for Data Visualization and Model Refinement. CoRR abs/2107.05978 (2021) - [i12]Michaela Hardt, Xiaoguang Chen, Xiaoyi Cheng, Michele Donini, Jason Gelman, Satish Gollaprolu, John He, Pedro Larroy, Xinyu Liu, Nick McCarthy, Ashish Rathi, Scott Rees, Amaresh Ankit Siva, ErhYuan Tsai, Keerthan Vasist, Pinar Yilmaz, Muhammad Bilal Zafar, Sanjiv Das, Kevin Haas, Tyler Hill, Krishnaram Kenthapadi:
Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability in the Cloud. CoRR abs/2109.03285 (2021) - [i11]David Nigenda, Zohar Karnin, Muhammad Bilal Zafar, Raghu Ramesha, Alan Tan, Michele Donini, Krishnaram Kenthapadi:
Amazon SageMaker Model Monitor: A System for Real-Time Insights into Deployed Machine Learning Models. CoRR abs/2111.13657 (2021) - [i10]Muhammad Bilal Zafar, Philipp Schmidt, Michele Donini, Cédric Archambeau, Felix Biessmann, Sanjiv Ranjan Das, Krishnaram Kenthapadi:
More Than Words: Towards Better Quality Interpretations of Text Classifiers. CoRR abs/2112.12444 (2021) - 2020
- [i9]Vedant Nanda, Till Speicher, John P. Dickerson, Krishna P. Gummadi, Muhammad Bilal Zafar:
Unifying Model Explainability and Robustness via Machine-Checkable Concepts. CoRR abs/2007.00251 (2020)
2010 – 2019
- 2019
- [b1]Muhammad Bilal Zafar:
Discrimination in Algorithmic Decision Making: From Principles to Measures and Mechanisms. Saarland University, Saarbrücken, Germany, 2019 - [j4]Juhi Kulshrestha
Search bias quantification: investigating political bias in social media and web search. Inf. Retr. J. 22(1-2): 188-227 (2019) - [j3]Muhammad Bilal Zafar, Isabel Valera
Fairness Constraints: A Flexible Approach for Fair Classification. J. Mach. Learn. Res. 20: 75:1-75:42 (2019) - [c15]Junaid Ali, Muhammad Bilal Zafar, Adish Singla
Loss-Aversively Fair Classification. AIES 2019: 211-218 - 2018
- [c14]Nina Grgic-Hlaca, Muhammad Bilal Zafar, Krishna P. Gummadi
Beyond Distributive Fairness in Algorithmic Decision Making: Feature Selection for Procedurally Fair Learning. AAAI 2018: 51-60 - [c13]Till Speicher, Hoda Heidari, Nina Grgic-Hlaca
A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual &Group Unfairness via Inequality Indices. KDD 2018: 2239-2248 - [i8]Till Speicher, Hoda Heidari, Nina Grgic-Hlaca, Krishna P. Gummadi, Adish Singla, Adrian Weller, Muhammad Bilal Zafar:
A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual & Group Unfairness via Inequality Indices. CoRR abs/1807.00787 (2018) - 2017
- [c12]Muhammad Bilal Zafar, Isabel Valera
Fairness Constraints: Mechanisms for Fair Classification. AISTATS 2017: 962-970 - [c11]Juhi Kulshrestha
Quantifying Search Bias: Investigating Sources of Bias for Political Searches in Social Media. CSCW 2017: 417-432 - [c10]Muhammad Bilal Zafar, Isabel Valera
From Parity to Preference-based Notions of Fairness in Classification. NIPS 2017: 229-239 - [c9]Muhammad Bilal Zafar, Isabel Valera
Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment. WWW 2017: 1171-1180 - [i7]Juhi Kulshrestha, Motahhare Eslami, Johnnatan Messias, Muhammad Bilal Zafar, Saptarshi Ghosh, Krishna P. Gummadi, Karrie Karahalios:
Quantifying Search Bias: Investigating Sources of Bias for Political Searches in Social Media. CoRR abs/1704.01347 (2017) - [i6]Juhi Kulshrestha, Muhammad Bilal Zafar, Lisette Espin Noboa, Krishna P. Gummadi, Saptarshi Ghosh:
Characterizing Information Diets of Social Media Users. CoRR abs/1704.01442 (2017) - [i5]Nina Grgic-Hlaca
On Fairness, Diversity and Randomness in Algorithmic Decision Making. CoRR abs/1706.10208 (2017) - [i4]Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez-Rodriguez, Krishna P. Gummadi, Adrian Weller:
From Parity to Preference-based Notions of Fairness in Classification. CoRR abs/1707.00010 (2017) - 2016
- [j2]Pedro Moreno-Sanchez
Listening to Whispers of Ripple: Linking Wallets and Deanonymizing Transactions in the Ripple Network. Proc. Priv. Enhancing Technol. 2016(4): 436-453 (2016) - [c8]Muhammad Bilal Zafar, Parantapa Bhattacharya, Niloy Ganguly
On the Wisdom of Experts vs. Crowds: Discovering Trustworthy Topical News in Microblogs. CSCW 2016: 437-450 - [c7]Muhammad Bilal Zafar, Krishna P. Gummadi
Message Impartiality in Social Media Discussions. ICWSM 2016: 466-475 - [i3]Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez-Rodriguez, Krishna P. Gummadi:
Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment. CoRR abs/1610.08452 (2016) - [i2]Miguel Ferreira, Muhammad Bilal Zafar, Krishna P. Gummadi:
The Case for Temporal Transparency: Detecting Policy Change Events in Black-Box Decision Making Systems. CoRR abs/1610.10064 (2016) - 2015
- [j1]Muhammad Bilal Zafar, Parantapa Bhattacharya, Niloy Ganguly
Sampling Content from Online Social Networks: Comparing Random vs. Expert Sampling of the Twitter Stream. ACM Trans. Web 9(3): 12:1-12:33 (2015) - [c6]Bimal Viswanath, Muhammad Ahmad Bashir, Muhammad Bilal Zafar, Simon Bouget
Strength in Numbers: Robust Tamper Detection in Crowd Computations. COSN 2015: 113-124 - [c5]Juhi Kulshrestha, Muhammad Bilal Zafar, Lisette Espin Noboa, Krishna P. Gummadi
Characterizing Information Diets of Social Media Users. ICWSM 2015: 218-227 - [i1]Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez-Rodriguez, Krishna P. Gummadi:
Fairness Constraints: A Mechanism for Fair Classification. CoRR abs/1507.05259 (2015) - 2014
- [c4]Parantapa Bhattacharya, Saptarshi Ghosh, Juhi Kulshrestha
Deep Twitter diving: exploring topical groups in microblogs at scale. CSCW 2014: 197-210 - [c3]Parantapa Bhattacharya, Muhammad Bilal Zafar, Niloy Ganguly
Inferring user interests in the Twitter social network. RecSys 2014: 357-360 - 2013
- [c2]Saptarshi Ghosh, Muhammad Bilal Zafar, Parantapa Bhattacharya, Naveen Kumar Sharma, Niloy Ganguly
On sampling the wisdom of crowds: random vs. expert sampling of the twitter stream. CIKM 2013: 1739-1744 - [c1]Shahida Jabeen
SplitBuff: Improving the interaction of heterogeneous RTT flows on the Internet. ICC 2013: 2315-2319
Coauthor Index
aka: P. Krishna Gummadi
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