The Flexible Socio Spatial Group Queries. - Bishwamittra Ghosh; Mohammed Eunus Ali; Farhana Murtaza Choudhury; Sajid Hasan Apon; Timos Sellis; Jianxin Li 001 (Proc. VLDB Endow., 2018)

IMLI: An Incremental Framework for MaxSAT-Based Learning of Interpretable Classification Rules. - Bishwamittra Ghosh; Kuldeep S. Meel (AIES, 2019)

A MaxSAT-Based Framework for Group Testing. - Lorenzo Ciampiconi; Bishwamittra Ghosh; Jonathan Scarlett; Kuldeep S. Meel (AAAI, 2020)

Classification Rules in Relaxed Logical Form. - Bishwamittra Ghosh; Dmitry Malioutov; Kuldeep S. Meel (ECAI, 2020)

A Formal Language Approach to Explaining RNNs. - Bishwamittra Ghosh; Daniel Neider (CoRR, 2020)

Probably Approximately Correct Explanations of Machine Learning Models via Syntax-Guided Synthesis. - Daniel Neider; Bishwamittra Ghosh (CoRR, 2020)

Justicia: A Stochastic SAT Approach to Formally Verify Fairness. - Bishwamittra Ghosh; Debabrota Basu; Kuldeep S. Meel (AAAI, 2021)

Algorithmic Fairness Verification with Graphical Models. - Bishwamittra Ghosh; Debabrota Basu; Kuldeep S. Meel (AAAI, 2022)

How Biased is Your Feature?: Computing Fairness Influence Functions with Global Sensitivity Analysis. - Bishwamittra Ghosh; Debabrota Basu; Kuldeep S. Meel (CoRR, 2022)

Efficient Learning of Interpretable Classification Rules. - Bishwamittra Ghosh; Dmitry Malioutov; Kuldeep S. Meel (J. Artif. Intell. Res., 2022)

Social-Spatial Group Queries with Keywords. - Sajid Hasan Apon; Mohammed Eunus Ali; Bishwamittra Ghosh; Timos Sellis (ACM Trans. Spatial Algorithms Syst., 2022)

"How Biased are Your Features?": Computing Fairness Influence Functions with Global Sensitivity Analysis. - Bishwamittra Ghosh; Debabrota Basu; Kuldeep S. Meel (FAccT, 2023)

Interpretability and Fairness in Machine Learning: A Formal Methods Approach. - Bishwamittra Ghosh (IJCAI, 2023)

Neighborhood-based Hypergraph Core Decomposition. - Naheed Anjum Arafat; Arijit Khan 001; Arpit Kumar Rai; Bishwamittra Ghosh (Proc. VLDB Endow., 2023)

Don't Forget What I did?: Assessing Client Contributions in Federated Learning. - Bishwamittra Ghosh; Debabrota Basu; Huazhu Fu; Yuan Wang 008; Renuga Kanagavelu; Jiang Jin Peng; Yong Liu 026; Rick Siow Mong Goh; Qingsong Wei (CoRR, 2024)

Towards Reliable Latent Knowledge Estimation in LLMs: In-Context Learning vs. Prompting Based Factual Knowledge Extraction. - Qinyuan Wu; Mohammad Aflah Khan; Soumi Das; Vedant Nanda; Bishwamittra Ghosh; Camila Kolling; Till Speicher; Laurent Bindschaedler; Krishna P. Gummadi; Evimaria Terzi (CoRR, 2024)

Understanding Memorisation in LLMs: Dynamics, Influencing Factors, and Implications. - Till Speicher; Mohammad Aflah Khan; Qinyuan Wu; Vedant Nanda; Soumi Das; Bishwamittra Ghosh; Krishna P. Gummadi; Evimaria Terzi (CoRR, 2024)

Active Fourier Auditor for Estimating Distributional Properties of ML Models. - Ayoub Ajarra; Bishwamittra Ghosh; Debabrota Basu (AAAI, 2025)

Logical Consistency of Large Language Models in Fact-Checking. - Bishwamittra Ghosh; Sarah Hasan; Naheed Anjum Arafat; Arijit Khan 001 (ICLR, 2025)

Towards Reliable Latent Knowledge Estimation in LLMs: Zero-Prompt Many-Shot Based Factual Knowledge Extraction. - Qinyuan Wu; Mohammad Aflah Khan; Soumi Das; Vedant Nanda; Bishwamittra Ghosh; Camila Kolling; Till Speicher; Laurent Bindschaedler; Krishna P. Gummadi; Evimaria Terzi (WSDM, 2025)

Revisiting Privacy, Utility, and Efficiency Trade-offs when Fine-Tuning Large Language Models. - Soumi Das; Camila Kolling; Mohammad Aflah Khan; Mahsa Amani; Bishwamittra Ghosh; Qinyuan Wu; Till Speicher; Krishna P. Gummadi (CoRR, 2025)

Rethinking Memorization Measures and their Implications in Large Language Models. - Bishwamittra Ghosh; Soumi Das; Qinyuan Wu; Mohammad Aflah Khan; Krishna P. Gummadi; Evimaria Terzi; Deepak Garg (CoRR, 2025)

Rote Learning Considered Useful: Generalizing over Memorized Data in LLMs. - Qinyuan Wu; Soumi Das; Mahsa Amani; Bishwamittra Ghosh; Mohammad Aflah Khan; Krishna P. Gummadi; Muhammad Bilal Zafar (CoRR, 2025)