Muhammad Bilal Zafar

According to our database, Muhammad Bilal Zafar authored at least 41 papers between 2013 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

The Impact of Inference Acceleration Strategies on Bias of LLMs.

Elisabeth Kirsten, Ivan Habernal, Vedant Nanda, Muhammad Bilal Zafar

CoRR, 2024

Evaluating Large Language Models with fmeval.

Pola Schwöbel, Luca Franceschi, Muhammad Bilal Zafar, Keerthan Vasist, Aman Malhotra, Tomer Shenhar, Pinal Tailor, Pinar Yilmaz, Michael Diamond, Michele Donini

CoRR, 2024

On Early Detection of Hallucinations in Factual Question Answering.

Ben Snyder, Marius Moisescu, Muhammad Bilal Zafar

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

2023

Explaining Multiclass Classifiers with Categorical Values: A Case Study in Radiography.

Luca Franceschi, Cemre Zor, Muhammad Bilal Zafar, Gianluca Detommaso, Cédric Archambeau, Tamas Madl, Michele Donini, Matthias W. Seeger

Proceedings of the Trustworthy Machine Learning for Healthcare, 2023

Hands-on Tutorial: "Explanations in AI: Methods, Stakeholders and Pitfalls".

Mia C. Mayer, Muhammad Bilal Zafar, Luca Franceschi, Huzefa Rangwala

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

Efficient fair PCA for fair representation learning.

Matthäus Kleindessner, Michele Donini, Chris Russell, Muhammad Bilal Zafar

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

What You Like: Generating Explainable Topical Recommendations for Twitter Using Social Annotations.

Parantapa Bhattacharya, Saptarshi Ghosh, Muhammad Bilal Zafar, Soumya K. Ghosh, Niloy Ganguly

CoRR, 2022

Diverse Counterfactual Explanations for Anomaly Detection in Time Series.

Déborah Sulem, Michele Donini, Muhammad Bilal Zafar, Francois-Xavier Aubet, Jan Gasthaus, Tim Januschowski, Sanjiv Das, Krishnaram Kenthapadi, Cédric Archambeau

CoRR, 2022

Amazon SageMaker Model Monitor: A System for Real-Time Insights into Deployed Machine Learning Models.

David Nigenda, Zohar Karnin, Muhammad Bilal Zafar, Raghu Ramesha, Alan Tan, Michele Donini, Krishnaram Kenthapadi

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

Generating Distributional Adversarial Examples to Evade Statistical Detectors.

Yigitcan Kaya, Muhammad Bilal Zafar, Sergül Aydöre, Nathalie Rauschmayr, Krishnaram Kenthapadi

Proceedings of the International Conference on Machine Learning, 2022

Pairwise Fairness for Ordinal Regression.

Matthäus Kleindessner, Samira Samadi, Muhammad Bilal Zafar, Krishnaram Kenthapadi, Chris Russell

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

More Than Words: Towards Better Quality Interpretations of Text Classifiers.

Muhammad Bilal Zafar, Philipp Schmidt, Michele Donini, Cédric Archambeau, Felix Biessmann, Sanjiv Ranjan Das, Krishnaram Kenthapadi

CoRR, 2021

DIVINE: Diverse Influential Training Points for Data Visualization and Model Refinement.

Umang Bhatt, Isabel Chien, Muhammad Bilal Zafar, Adrian Weller

CoRR, 2021

Multi-objective Asynchronous Successive Halving.

Robin Schmucker, Michele Donini, Muhammad Bilal Zafar, David Salinas, Cédric Archambeau

CoRR, 2021

Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability in the Cloud.

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

Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

Fair Bayesian Optimization.

Valerio Perrone, Michele Donini, Muhammad Bilal Zafar, Robin Schmucker, Krishnaram Kenthapadi, Cédric Archambeau

Proceedings of the AIES '21: AAAI/ACM Conference on AI, 2021

On the Lack of Robust Interpretability of Neural Text Classifiers.

Muhammad Bilal Zafar, Michele Donini, Dylan Slack, Cédric Archambeau, Sanjiv Das, Krishnaram Kenthapadi

Proceedings of the Findings of the Association for Computational Linguistics: ACL/IJCNLP 2021, 2021

2020

Unifying Model Explainability and Robustness via Machine-Checkable Concepts.

Vedant Nanda, Till Speicher, John P. Dickerson, Krishna P. Gummadi, Muhammad Bilal Zafar

CoRR, 2020

2019

Discrimination in Algorithmic Decision Making: From Principles to Measures and Mechanisms.

Muhammad Bilal Zafar

PhD thesis, 2019

Fairness Constraints: A Flexible Approach for Fair Classification.

Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez-Rodriguez, Krishna P. Gummadi

J. Mach. Learn. Res., 2019

Search bias quantification: investigating political bias in social media and web search.

Juhi Kulshrestha, Motahhare Eslami, Johnnatan Messias, Muhammad Bilal Zafar, Saptarshi Ghosh, Krishna P. Gummadi, Karrie Karahalios

Inf. Retr. J., 2019

Loss-Aversively Fair Classification.

Junaid Ali, Muhammad Bilal Zafar, Adish Singla, Krishna P. Gummadi

Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 2019

2018

A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual &Group Unfairness via Inequality Indices.

Till Speicher, Hoda Heidari, Nina Grgic-Hlaca, Krishna P. Gummadi, Adish Singla, Adrian Weller, Muhammad Bilal Zafar

Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018

Beyond Distributive Fairness in Algorithmic Decision Making: Feature Selection for Procedurally Fair Learning.

Nina Grgic-Hlaca, Muhammad Bilal Zafar, Krishna P. Gummadi, Adrian Weller

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017

On Fairness, Diversity and Randomness in Algorithmic Decision Making.

Nina Grgic-Hlaca, Muhammad Bilal Zafar, Krishna P. Gummadi, Adrian Weller

CoRR, 2017

Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment.

Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez-Rodriguez, Krishna P. Gummadi

Proceedings of the 26th International Conference on World Wide Web, 2017

From Parity to Preference-based Notions of Fairness in Classification.

Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez-Rodriguez, Krishna P. Gummadi, Adrian Weller

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Quantifying Search Bias: Investigating Sources of Bias for Political Searches in Social Media.

Juhi Kulshrestha, Motahhare Eslami, Johnnatan Messias, Muhammad Bilal Zafar, Saptarshi Ghosh, Krishna P. Gummadi, Karrie Karahalios

Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing, 2017

Fairness Constraints: Mechanisms for Fair Classification.

Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez-Rodriguez, Krishna P. Gummadi

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016

Listening to Whispers of Ripple: Linking Wallets and Deanonymizing Transactions in the Ripple Network.

Pedro Moreno-Sanchez, Muhammad Bilal Zafar, Aniket Kate

Proc. Priv. Enhancing Technol., 2016

The Case for Temporal Transparency: Detecting Policy Change Events in Black-Box Decision Making Systems.

Miguel Ferreira, Muhammad Bilal Zafar, Krishna P. Gummadi

CoRR, 2016

Message Impartiality in Social Media Discussions.

Muhammad Bilal Zafar, Krishna P. Gummadi, Cristian Danescu-Niculescu-Mizil

Proceedings of the Tenth International Conference on Web and Social Media, 2016

On the Wisdom of Experts vs. Crowds: Discovering Trustworthy Topical News in Microblogs.

Muhammad Bilal Zafar, Parantapa Bhattacharya, Niloy Ganguly, Saptarshi Ghosh, Krishna P. Gummadi

Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing, 2016

2015

Sampling Content from Online Social Networks: Comparing Random vs. Expert Sampling of the Twitter Stream.

Muhammad Bilal Zafar, Parantapa Bhattacharya, Niloy Ganguly, Krishna P. Gummadi, Saptarshi Ghosh

ACM Trans. Web, 2015

Fairness Constraints: A Mechanism for Fair Classification.

Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez-Rodriguez, Krishna P. Gummadi

CoRR, 2015

Characterizing Information Diets of Social Media Users.

Juhi Kulshrestha, Muhammad Bilal Zafar, Lisette Espin Noboa, Krishna P. Gummadi, Saptarshi Ghosh

Proceedings of the Ninth International Conference on Web and Social Media, 2015

Strength in Numbers: Robust Tamper Detection in Crowd Computations.

Bimal Viswanath, Muhammad Ahmad Bashir, Muhammad Bilal Zafar, Simon Bouget, Saikat Guha, Krishna P. Gummadi, Aniket Kate, Alan Mislove

Proceedings of the 2015 ACM on Conference on Online Social Networks, 2015

2014

Inferring user interests in the Twitter social network.

Parantapa Bhattacharya, Muhammad Bilal Zafar, Niloy Ganguly, Saptarshi Ghosh, Krishna P. Gummadi

Proceedings of the Eighth ACM Conference on Recommender Systems, 2014

Deep Twitter diving: exploring topical groups in microblogs at scale.

Parantapa Bhattacharya, Saptarshi Ghosh, Juhi Kulshrestha, Mainack Mondal, Muhammad Bilal Zafar, Niloy Ganguly, Krishna P. Gummadi

Proceedings of the Computer Supported Cooperative Work, 2014

2013

SplitBuff: Improving the interaction of heterogeneous RTT flows on the Internet.

Shahida Jabeen, Muhammad Bilal Zafar, Ihsan Ayyub Qazi, Zartash Afzal Uzmi

Proceedings of IEEE International Conference on Communications, 2013

On sampling the wisdom of crowds: random vs. expert sampling of the twitter stream.

Saptarshi Ghosh, Muhammad Bilal Zafar, Parantapa Bhattacharya, Naveen Kumar Sharma, Niloy Ganguly, P. Krishna Gummadi

Proceedings of the 22nd ACM International Conference on Information and Knowledge Management, 2013