On the Lack of Robust Interpretability of Neural Text Classifiers. - Muhammad Bilal Zafar; Michele Donini; Dylan Slack; Cédric Archambeau; Sanjiv Das; Krishnaram Kenthapadi (ACL/IJCNLP (Findings), 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; et al. (KDD, 2021)

More Than Words: Towards Better Quality Interpretations of Text Classifiers. - Muhammad Bilal Zafar; Philipp Schmidt 002; Michele Donini; Cédric Archambeau; Felix Biessmann; Sanjiv Ranjan Das; Krishnaram Kenthapadi (CoRR, 2021)

Intelligent Probabilistic Forecasts of VIX and its Volatility using Machine Learning Methods. - Aerambamoorthy Thavaneswaran; You Liang; Sanjiv Das; Ruppa K. Thulasiram; Janakumar Bhanushali (CIFEr, 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)

A System for Analyzing Human Capability at Scale Using AI. - Daniel Zhu; Dave Ulrich; Sanjiv Das; Norm Smallwood (IntelliSys (3), 2023)

Utility-Preserving Privacy-Enabled Speech Embeddings for Emotion Detection. - Chandrashekhar Lavania; Sanjiv Das; Xin Huang; Kyu J. Han (INTERSPEECH, 2023)

PPLqa: An Unsupervised Information-Theoretic Quality Metric for Comparing Generative Large Language Models. - Gerald Friedland; Xin Huang; Yueying Cui; Vishaal Kapoor; Ashish Khetan; Sanjiv Das (CoRR, 2024)