You may also find a list of my publications at [Google Scholar]. 

Recent Preprints:

[A2]. Y. Fu , F. Hamman, S. Dutta, "T-Shirt: Token-Selective Hierarchical Data Selection for Instruction Tuning"  [Full-Paper]

[A1]. D. Egea, B. Halder, and S. Dutta, “VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation”  [Full-Paper]

Selected Conference Papers:

(Scroll down for a list of journal publications)

[C24]. F. Hamman, P. Dissanayake, S. Mishra, F. Lecue, S. Dutta, "Quantifying Prediction Consistency Under Fine-tuning Multiplicity in Tabular LLMs" International Conference on Machine Learning (ICML 2025).  [Full Paper]

[C23]. S. Meel, P. Dissanayake, M. Nomeir, S. Dutta, S. Ulukus, "Private Counterfactual Retrieval With Immutable Features"  IEEE International Symposium on Information Theory (ISIT 2025) 

[C22]. P. Dissanayake, F. Hamman, B. Halder, Q. Zhang, I. Sucholutsky, S. Dutta, "Quantifying Knowledge Distillation using Partial Information Decomposition" Artificial Intelligence and Statistics (AISTATS 2025).  [Full Paper]

[C21]. E. Noorani, P. Dissanayake, F. Hamman, S. Dutta, "Counterfactual Explanations for Model Ensembles Using Entropic Risk Measures," International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2025). [Full Paper]

[C20]. P. Dissanayake and S. Dutta, "Model Reconstruction Using Counterfactual Explanations: A Perspective From Polytope Theory," Advances in Neural Information Processing Systems (NeurIPS 2024). [Full Paper]

[C19]. F. Hamman and S. Dutta, "Demystifying Local & Global Fairness Trade-offs in Federated Learning Using Partial Information Decomposition,” International Conference on Learning Representations (ICLR 2024). [Full Paper]

[C18].  F. Hamman and S. Dutta, "A Unified View of Group Fairness Tradeoffs Using Partial Information Decomposition,"  IEEE International Symposium on Information Theory (ISIT 2024). [Full Paper]

[C17]. F. Hamman, E. Noorani, S. Mishra, D. Magazzeni, and S. Dutta, "Robust Counterfactual Explanations for Neural Networks with Probabilistic Guarantees,” International Conference on Machine Learning (ICML 2023). [Full Paper]

[C16]. S. Sharma, S. Dutta, E. Albini, F. Lecue, D. Magazzeni, and M. Veloso, "REFRESH: Responsible and Efficient Feature Reselection guided by SHAP values," AAAI/ACM Conference on AI, Ethics, and Society (AIES 2023).

[C15]. F. Hamman, J. Chen, and S. Dutta, "Can Querying for Bias Leak Protected Attributes? Achieving Privacy With Smooth Sensitivity," ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT 2023).  

[C14]. S. Garg, S. Dutta, M. Dalirrooyfard, A. Schneider, and Y. Nevmyvaka, "In- or Out-of-Distribution Detection via Dual Divergence Estimation," Conference on Uncertainty in Artificial Intelligence (UAI 2023). 

[C13]. P. Mathur, A T Neerkaje, M Chhibber, R Sawhney, F Guo, F Dernoncourt, S Dutta, and D Manocha, "MONOPOLY: Financial Prediction from MONetary POLicY Conference Videos Using Multimodal Cues," ACM Multimedia 2022 (ACM-MM 2022).

[C12]. S. Dutta, J Long, S Mishra, C Tilli, and D Magazzeni, "Robust Counterfactual Explanations for Tree-Based Ensembles," International Conference on Machine Learning (ICML 2022).  [Full Paper]

[C11]. P Venkatesh, S Dutta*, N Mehta*, and P Grover, "Can Information Flows Suggest Targets for Interventions in Neural Circuits?," Advances in Neural Information Processing Systems (NeurIPS 2021). [Full Paper]

[C10]. S. Dutta, D. Wei, H. Yueksel, P. Y. Chen, S. Liu, and K. R. Varshney, "Is There a Trade-Off Between Fairness and Accuracy? A Perspective Using Mismatched Hypothesis Testing," International Conference on Machine Learning (ICML 2020).  [Full Paper]

[C9]. P. Venkatesh, S. Dutta and P. Grover, "How else should we define Information Flow in Neural Circuits,” IEEE International Symposium on Information Theory (ISIT 2020).

[C8]. S. Dutta, P. Venkatesh, P. Mardziel, A. Datta and P. Grover, "An Information-Theoretic Quantification of Discrimination with Exempt Features," AAAI Conference on Artificial Intelligence (AAAI 2020, ORAL).  [Full Paper] [Poster]

[C7]. P. Venkatesh, S. Dutta, and P. Grover, "How should we define Information Flow in Neural Circuits,” IEEE International Symposium on Information Theory (ISIT 2019).  [Conference Paper]  [Full Paper]  

[C6]. U. Sheth, S. Dutta, M. Chaudhari, H. Jeong, Y. Yang, J. Kohonen, T. Roos, and P. Grover, "An Application of Storage-Optimal MatDot Codes for Coded Matrix Multiplication: Fast k-Nearest Neighbors Estimation,” IEEE International Conference on Big Data (IEEE BigData 2018).     [Conference Paper] [Full Paper]

[C5]. S. Dutta*, Z. Bai*, H. Jeong, T. M. Low, and P. Grover, "A Unified Coded Deep Neural Network Training Strategy based on Generalized PolyDot Codes," IEEE International Symposium on Information Theory (ISIT 2018).  [Full Paper]

[C4]. S. Dutta, G. Joshi, P. Dube, S. Ghosh, and P. Nagpurkar, "Slow and stale gradients can win the race: Error-Runtime trade-offs in Distributed SGD," International Conference on Artificial Intelligence and Statistics (AISTATS 2018).[Conference Paper] [Full Paper]

[C3]. S. Dutta, V. Cadambe, and P. Grover, "Coded Convolution for parallel and distributed computing within a deadline," IEEE International Symposium on Information Theory (ISIT 2017).  [Conference Paper] [Full Paper]

[C2]. S. Dutta, V. Cadambe, and P. Grover, "Short-Dot: Computing Large Linear Transforms Distributedly using Coded Short Dot Products," Advances in Neural Information Processing Systems (NeurIPS 2016). [Conference Paper] [Full Paper] [Spotlight Video]

[C1]. S. Dutta and P. Grover, "Adaptivity provably helps: Information-theoretic limits on l0 cost of non-adaptive sensing," IEEE International Symposium on Information Theory (ISIT 2016). [Conference Paper] [Full Paper]

Selected Journals:

[J10]. F. Hamman, E. Noorani, S. Mishra, D. Magazzeni, and S. Dutta, "Robust Algorithmic Recourse Under Model Multiplicity with Probabilistic Guarantees,” IEEE Journal on Selected Areas in Information Theory: Information-Theoretic Methods for Trustworthy Machine Learning (JSAIT 2024).

[J9]. A. K. Veldanda, I. Brugere, S. Dutta, A. Mishler, and S. Garg, "Hyper-parameter Tuning for Fair Classification without Sensitive Attribute Access," Transactions on Machine Learning Research (TMLR 2024). 

[J8]. A. K. Veldanda, I. Brugere, J. Chen, S. Dutta, A. Mishler, and S. Garg, “Fairness via In-Processing in the Over-parameterized Regime: A Cautionary Tale,” Transactions on Machine Learning Research (TMLR 2023). 

[J7]. S. Dutta, F. Hamman, "A Review of Partial Information Decomposition in Algorithmic Fairness and Explainability," Entropy 2023.

[J6]. S. Dutta, P. Venkatesh, P. Mardziel, A. Datta, and P. Grover, "Fairness under Feature Exemptions: Counterfactual and Observational Measures," IEEE Transactions on Information Theory, Oct 2021. [Full Paper]

[J5]. S. Dutta, J. Wang, and G. Joshi, "Slow and stale gradients can win the race," IEEE Journal on Selected Areas in Information Theory, Sep 2021. 

[J4]. S. Dutta*, H. Jeong*, Y. Yang*, V. Cadambe, T. M. Low, and P. Grover, “Addressing Unreliability in Emerging Devices and Non-von Neumann Architectures Using Coded Computing,"  Proceedings of the IEEE, April 2020.

[J3]. P. Venkatesh, S. Dutta, and P. Grover, "Information Flow in Computational Systems,” IEEE Transactions on Information Theory, Sep 2020. [Full Paper]

[J2]. S. Dutta*, M. Fahim*, H. Jeong*, F. Haddadpour*, V. Cadambe, and P. Grover, "On the Optimal Recovery Threshold of Coded Matrix Multiplication," IEEE Transactions on Information Theory, Jan 2020.  [Full Paper]  

[J1]. S. Dutta, V. Cadambe, and P. Grover, "Short-Dot: Computing Large Linear Transforms Distributedly using Coded Short Dot Products," IEEE Transactions on Information Theory, Oct 2019.  [Full Paper]

Other Papers (Peer-Reviewed Workshops/Conferences):

[W12]. P. Dissanayake, F. Hamman, B. Halder, I. Sucholutsky, Q. Zhang, and S. Dutta. “Formalizing Limits of Knowledge Distillation Using Partial Information Decomposition”. In: NeurIPS Workshop on Machine Learning and Compression (2024).

[W11]. B. Halder, F. Hamman, P. Dissanayake, Q. Zhang, I. Sucholutsky, and S. Dutta, "Quantifying Spuriousness of Biased Datasets Using Partial Information Decomposition," ICML Workshop on Data-centric Machine Learning Research: Datasets for Foundation Models (ICML-DMLR Workshop 2024).

[W10]. F. Hamman and S. Dutta, "Demystifying Local & Global Fairness Trade-offs in Federated Learning Using Information Theory,” ICML Workshop on Federated Learning and Analytics in Practice (ICML-FL Workshop 2023).

[W9]. F. Hamman, J. Chen, and S. Dutta, "Can Querying for Bias Leak Protected Attributes? Achieving Privacy With Smooth Sensitivity," NeurIPS Workshop on Algorithmic Fairness through the Lens of Causality and Privacy (NeurIPS-AFCP Workshop 2022).

[W8]. S Mishra, S Dutta, J Long, and D Magazzeni, "A Survey on the Robustness of Feature Importance and Counterfactual Explanations," Explainable AI in Finance (XAI-FIN21). [Full Paper]

[W7]. S Dutta, P Venkatesh, and P Grover, "Quantifying Feature Contributions to Overall Disparity Using Information Theory," AAAI Workshop on Information-Theoretic Methods for Causal Inference and Discovery (AAAI-ITCI Workshop 2022).

[W6]. C. Jiang*, B. Wu*, S. Dutta, and P. Grover, "Bursting the Bubbles: Debiasing Recommendation Systems While Allowing for Chosen Category Exemptions," BIAS Workshop at ECIR (ECIR Workshop 2021).

[W5]. S. Dutta, L. Ma, T. K. Saha, D. Liu, J. Tetreault, and A. Jaimes, "GTN-ED: Event Detection Using Graph Transformer Networks," TextGraphs Workshop at NAACL (NAACL Workshop 2021).

[W4]. S. Dutta, Z. Bai, T. M. Low, and P. Grover, "CodeNet: Training Large Scale Neural Networks in Presence of Soft-Errors," Coding Theory For Large-scale Machine Learning Workshop at ICML (CodML Workshop, ICML 2019, Spotlight).[Short Workshop Paper] [Full Paper] 

[W3].  M. Fahim*, H. Jeong*, F. Haddadpour, S. Dutta, V. Cadambe, and P. Grover, "On the Optimal Recovery Threshold of Coded Matrix Multiplication," Communication, Control and Computing (Allerton 2017).  [Full Paper]

[W2]. S. Dutta, Y. Yang, N. Wang, E. Pop, V. Cadambe, and P. Grover, “Reliable Matrix Multiplication using Error-prone Dot-product Nanofunctions with an application to logistic regression” (SRC Techcon, 2016).

[W1]. S. Dutta and A. De, "Sparse UltraWideBand Radar Imaging in a Locally Adapting Matching Pursuit (LAMP) Framework," IEEE International Radar Conference (RADAR 2015).  [Conference Paper] [Full Paper].

Patents/Invention Disclosures

[P4]. S. Mishra, F. Lecue, C. Tilli, D. Magazzeni, S. Dutta, and J. Long, "Method And System For Computing Unstability Factors In Predictive Model," Application No. 18112272, 2024.

[P3]. Ivan Brugere et al., "Method And System For Improving Model Fairness By Using Explainability Techniques," Application No. 17968220. 2024.

[P2]. P. Grover, H. Jeong, Y. Yang, S. Dutta, Z. Bal, T. M. Low, M. Fahim, F. Haddadpour, and V. Cadambe, “Coded computation strategies for distributed matrix-matrix and matrix-vector products,” Application No. 16588990, 2020.

[P1]. P. Dube, S. Dutta, G. Joshi, and P. Nagpurkar, “Adaptive learning rate schedule in distributed stochastic gradient descent,” Application No. 15938830, 2019.

*Equal Contribution