Trustworthy AI Center of Excellence

Research Papers

All authors were affiliated with J.P. Morgan AI Research at the time of publication, unless otherwise specified.

Interpreting Language Reward Models via Contrastive Explanations

Junqi Jiang, Tom Bewley, Saumitra Mishra, Freddy Lecue, Manuela Veloso

International Conference on Learning Representations (ICLR), January 2025

PICE: Counterfactuals on the Decision Boundary for Piecewise Linear Networks

Mattia Villani, Emanuele Albini, Shubham Sharma, Salim Amoukou, Saumitra Mishra, Daniele Magazzeni, Manuela Veloso

AAAI Conference on AI, Ethics, and Society (AIES), October 2024

Sequential Harmful Shift Detection Without Labels

Salim I Amoukou, Tom Bewley, Saumitra Mishra, Freddy Lecue, Daniele Magazzeni, Manuela Veloso

Conference on Neural Information Processing Systems (NeurIPS), September 2024

Are Logistic Models Really Interpretable?

Danial Dervovic, Freddy Lecue, Nicolas Marchesotti, Daniele Magazzeni

International Joint Conference on Artificial Intelligence (IJCAI), August 2024

Progressive Inference: Explaining Decoder-Only Sequence Classification Models using Intermediate Predictions

Sanjay Kariyappa, Freddy Lecue, Saumitra Mishra, Christopher Pond, Daniele Magazzeni, Manuela Veloso

International Conference on Machine Learning (ICML), July 2024

Counterfactual Metarules for Local and Global Recourse

Tom Bewley, Salim Amoukou, Saumitra Mishra, Daniele Magazzeni, Manuela Veloso

International Conference on Machine Learning (ICML), July 2024

Bounding the Excess Risk for Linear Models Trained on Marginal-Preserving, Differentially-Private, Synthetic Data

Yvonne Zhou, Mingyu Liang, Ivan Brugere, Dana Dachman-Soled, Danial Dervovic, Antigoni Polychroniadou, Min Wu

International Conference on Machine Learning (ICML), July 2024

Robust Algorithmic Recourse Under Model Multiplicity With Probabilistic Guarantees

Faisal Hamman, Erfaun Noorani, Saumitra Mishra, Daniele Magazzeni, Sanghamitra Dutta

IEEE Journal on Selected Areas in Information Theory, Special Issue: Information-Theoretic Methods for Trustworthy and Reliable Machine Learning, May 2024

SHAP@k: Efficient and Probably Approximately Correct (PAC) Identification of Top-k Features

Sanjay Kariyappa, Leonidas Tsepenekas, Freddy Lecue, Daniele Magazzeni

AAAI Conference on Artificial Intelligence (AAAI), February 2024

SafeAR: Towards Safer Algorithmic Recourse by Risk-Aware Policies

Haochen Wu, Shubham Sharma, Sunandita Patra, and Sriram Gopalakrishnan

AAAI Conference on Artificial Intelligence (AAAI), February 2024

Comparing Apples to Oranges: Learning Similarity Functions for Data Produced by Different Distributions

Leonidas Tsepenekas, Ivan Brugere, Freddy Lecue, Daniele Magazzeni

Conference on Neural Information Processing Systems (NeurIPS), September 2023

REFRESH: Responsible and Efficient Feature Reselection guided by SHAP values

Shubham Sharma, Sanghamitra Dutta, Emanuele Albini, Freddy Lecue, Daniele Magazzeni, Manuela Veloso

AAAI/ACM Conference on AI Ethics and Society (AIES), May 2023

On the Connection between Game-Theoretic Feature Attributions and Counterfactual Explanations

Emanuele Albini, Shubham Sharma, Saumitra Mishra, Danial Dervovic, Daniele Magazzeni

AAAI/ACM Conference on AI Ethics and Society (AIES), May 2023

GLOBE-CE: A Translation Based Approach for Global Counterfactual Explanations

Dan Ley, Saumitra Mishra, Daniele Magazzeni

International Conference on Machine Learning (ICML), April 2023

Robust Counterfactual Explanations for Neural Networks With Probabilistic Guarantees

Faisal Hamman (external), Erfaun Noorani (external), Saumitra Mishra, Daniele Magazzeni, Sanghamitra Dutta (external)

International Conference on Machine Learning (ICML), April 2023

Bayesian Hierarchical Models for Counterfactual Estimation

Natraj Raman, Daniele Magazzeni, Sameena Shah

International Conference on Artificial Intelligence and Statistics (AISTATS), January 2023

CLEAR: Generative Counterfactual Explanations on Graphs

Jing Ma (external), Ruocheng Guo (external), Saumitra Mishra, Aidong Zhang (external), Jundong Li (external)

Neural Information Processing Systems (NeurIPS’22), December 2022

Robust Counterfactual Explanations for Tree-Based Ensembles | Poster| Slides

Sanghamitra Dutta, Jason Long, Saumitra Mishra, Cecilia Tilli, Daniele Magazzeni

International Conference on Machine Learning (ICML’22), July 2022

Explaining Preference-driven Schedules: the EXPRES Framework

Alberto Pozanco, Francesca Mosca, Parisa Zehtabi, Daniele Magazzeni, Sarit Kraus

ICAPS'22, June 2022

Towards Learning to Explain with Concept Bottleneck Models: Mitigating Information Leakage

Joshua Lockhart, Nicolas Marchesotti, Daniele Magazzeni, Manuela Veloso

ICLR'22 Workshop on Socially Responsible Machine Learning, April 2022

Global Counterfactual Explanations: Investigations, Implementations and Improvements

Dan Ley, Saumitra Mishra, Daniele Magazzeni

ICLR'22 Workshop on Privacy Accountability, Interpretability, Robustness, Reasoning on Structured Data, March 2022

Counterfactual Shapley Additive Explanations

Emanuele Albini, Jason Long, Danial Dervovic, Daniele Magazzeni

ACM Conference on Fairness, Accountability, and Transparency (FAccT'22), January 2022

A Survey on the Robustness of Feature Importance and Counterfactual Explanations

Saumitra Mishra, Sanghamitra Dutta, Jason Long, Daniele Magazzeni

ICAIF’21 Workshop on Explainable AI in Finance, November 2021

Counterfactual Explanations for Arbitrary Regression Models

Thomas Spooner, Danial Dervovic, Jason Long, Jon Shepard, Jiahao Chen, Daniele Magazzeni

ICML’21 Workshop on Algorithmic Recourse, July 2021