Distributed Kalman filtering and Network Tracking Capacity. - Subhro Das; José M. F. Moura (ACSSC, 2013)

Distributed linear estimation of dynamic random fields. - Subhro Das; José M. F. Moura (Allerton, 2013)

Distributed Kalman filtering. - Subhro Das; José M. F. Moura (EUSIPCO, 2013)

Distributed state estimation in multi-agent networks. - Subhro Das; José M. F. Moura (ICASSP, 2013)

Distributed Kalman Filtering With Dynamic Observations Consensus. - Subhro Das; José M. F. Moura (IEEE Trans. Signal Process., 2015)

Distributed Linear Filtering and Prediction of Time-varying Random Fields. - Subhro Das (2016)

Interpretable Clustering for Prototypical Patient Understanding: A Case Study of Hypertension and Depression Subgroup Behavioral Profiling in National Health and Nutrition Examination Survey Data. - Pei-Yun Sabrina Hsueh; Subhro Das (AMIA, 2017)

A Personalized Pacing System for Real-Time Physical Activity Advising. - Hung-Yang Chang; Zhiguo Li; Subhro Das; Tian Hao; Chandramouli Maduri; Chohreh Partovian; James V. Codella; Ching-Hua Chen (CHASE, 2017)

Making Sense of Patient-Generated Health Data for Interpretable Patient-Centered Care: The Transition from "More" to "Better". - Pei-Yun Sabrina Hsueh; Sanjoy Dey; Subhro Das; Thomas Wetter (MedInfo, 2017)

Distributed Estimation of Dynamic Fields over Multi-agent Networks. - Subhro Das; José M. F. Moura (CoRR, 2017)

Consensus+Innovations Distributed Kalman Filter With Optimized Gains. - Subhro Das; José M. F. Moura (IEEE Trans. Signal Process., 2017)

Learning to Personalize from Practice: A Real World Evidence Approach of Care Plan Personalization based on Differential Patient Behavioral Responses in Care Management Records. - Pei-Yun Sabrina Hsueh; Subhro Das; Chandramouli Maduri; Karie Kelly (AMIA, 2018)

Learning Patient Engagement in Care Management: Performance vs. Interpretability. - Subhro Das; Chandramouli Maduri; Ching-Hua Chen; Pei-Yun Sabrina Hsueh (CoRR, 2019)

An Adaptive, Data-Driven Personalized Advisor for Increasing Physical Activity. - Zhiguo Li; Subhro Das; James V. Codella; Tian Hao; Kun Lin; Chandramouli Maduri; Ching-Hua Chen (IEEE J. Biomed. Health Informatics, 2019)

Learning Occupational Task-Shares Dynamics for the Future of Work. - Subhro Das; Sebastian Steffen; Wyatt Clarke; Prabhat Reddy; Erik Brynjolfsson; Martin Fleming (AIES, 2020)

Interpretable subgroup discovery in treatment effect estimation with application to opioid prescribing guidelines. - Chirag Nagpal; Dennis Wei; Bhanukiran Vinzamuri; Monica Shekhar; Sara E. Berger; Subhro Das; Kush R. Varshney (CHIL, 2020)

Learning Interpretable Behavioral Engagement for Care Management. - Subhro Das; Chandramouli Maduri; Ching-Hua Chen; Pei-Yun Sabrina Hsueh (MIE, 2020)

Model adaptation and unsupervised learning with non-stationary batch data under smooth concept drift. - Subhro Das; Prasanth Lade; Soundar Srinivasan (CoRR, 2020)

Stochastic Optimization with Non-stationary Noise. - Jingzhao Zhang; Hongzhou Lin; Subhro Das; Suvrit Sra; Ali Jadbabaie (CoRR, 2020)

Formal Verification of End-to-End Learning in Cyber-Physical Systems: Progress and Challenges. - Nathan Fulton; Nathan Hunt; Nghia Hoang; Subhro Das (CoRR, 2020)

A Dynamical Systems Approach for Convergence of the Bayesian EM Algorithm. - Orlando Romero; Subhro Das; Pin-Yu Chen; Sérgio Pequito (CoRR, 2020)

Verifiably Safe Exploration for End-to-End Reinforcement Learning. - Nathan Hunt; Nathan Fulton; Sara Magliacane; Nghia Hoang; Subhro Das; Armando Solar-Lezama (CoRR, 2020)

Online Optimal Control with Affine Constraints. - Yingying Li; Subhro Das; Na Li 002 (AAAI, 2021)

IF: Iterative Fractional Optimization. - Sarthak Chatterjee; Subhro Das; Sérgio Pequito (ESANN, 2021)

Verifiably safe exploration for end-to-end reinforcement learning. - Nathan Hunt; Nathan Fulton; Sara Magliacane; Trong Nghia Hoang; Subhro Das; Armando Solar-Lezama (HSCC, 2021)

Fair Selective Classification Via Sufficiency. - Joshua K. Lee; Yuheng Bu; Deepta Rajan; Prasanna Sattigeri; Rameswar Panda; Subhro Das; Gregory W. Wornell (ICML, 2021)

On Multisensor Activation Policies for Bernoulli Tracking. - Augustin-Alexandru Saucan; Subhro Das; Moe Z. Win (MILCOM, 2021)

Safe Adaptive Learning-based Control for Constrained Linear Quadratic Regulators with Regret Guarantees. - Yingying Li; Subhro Das; Jeff S. Shamma; Na Li 002 (CoRR, 2021)

Practical Skills Demand Forecasting via Representation Learning of Temporal Dynamics. - Maysa Malfiza Garcia de Macedo; Wyatt Clarke; Eli Lucherini; Tyler Baldwin; Dilermando Queiroz Neto; Rogério Abreu de Paula; Subhro Das (AIES, 2022)

Better Skill-based Job Representations, Assessed via Job Transition Data. - Tyler Baldwin; Wyatt Clarke; Maysa M. Garcia de Macedo; Rogério de Paula; Subhro Das (IEEE Big Data, 2022)

Learning skills adjacency representations for optimized reskilling recommendations. - Saksham Gandhi; Raj Nagesh; Subhro Das (IEEE Big Data, 2022)

On observability and optimal gain design for distributed linear filtering and prediction. - Subhro Das (EUSIPCO, 2022)

On Convergence of Gradient Descent Ascent: A Tight Local Analysis. - Haochuan Li; Farzan Farnia; Subhro Das; Ali Jadbabaie (ICML, 2022)

Selective Regression under Fairness Criteria. - Abhin Shah; Yuheng Bu; Joshua K. Lee; Subhro Das; Rameswar Panda; Prasanna Sattigeri; Gregory W. Wornell (ICML, 2022)

Beyond Worst-Case Analysis in Stochastic Approximation: Moment Estimation Improves Instance Complexity. - Jingzhao Zhang; Hongzhou Lin; Subhro Das; Suvrit Sra; Ali Jadbabaie (ICML, 2022)

An Alternative Approach for Distributed Parameter Estimation Under Gaussian Settings. - Subhro Das (MLSP, 2022)

Evaluating Robustness of Cooperative MARL: A Model-based Approach. - Nhan H. Pham; Lam M. Nguyen; Jie Chen; Hoang Thanh Lam; Subhro Das; Tsui-Wei Weng (CoRR, 2022)

Post-hoc Uncertainty Learning Using a Dirichlet Meta-Model. - Maohao Shen; Yuheng Bu; Prasanna Sattigeri; Soumya Ghosh; Subhro Das; Gregory W. Wornell (AAAI, 2023)

Who Should Predict? Exact Algorithms For Learning to Defer to Humans. - Hussein Mozannar; Hunter Lang; Dennis Wei; Prasanna Sattigeri; Subhro Das; David A. Sontag (AISTATS, 2023)

Reliable Gradient-free and Likelihood-free Prompt Tuning. - Maohao Shen; Soumya Ghosh; Prasanna Sattigeri; Subhro Das; Yuheng Bu; Gregory W. Wornell (EACL (Findings), 2023)

Attacking c-MARL More Effectively: A Data Driven Approach. - Nhan H. Pham; Lam M. Nguyen; Jie Chen; Hoang Thanh Lam; Subhro Das; Tsui-Wei Weng (ICDM, 2023)

Label-free Concept Bottleneck Models. - Tuomas P. Oikarinen; Subhro Das; Lam M. Nguyen; Tsui-Wei Weng (ICLR, 2023)

ConCerNet: A Contrastive Learning Based Framework for Automated Conservation Law Discovery and Trustworthy Dynamical System Prediction. - Wang Zhang; Tsui-Wei Weng; Subhro Das; Alexandre Megretski; Luca Daniel; Lam M. Nguyen (ICML, 2023)

Effective Human-AI Teams via Learned Natural Language Rules and Onboarding. - Hussein Mozannar; Jimin J. Lee; Dennis Wei; Prasanna Sattigeri; Subhro Das; David A. Sontag (NeurIPS, 2023)

Group Fairness with Uncertainty in Sensitive Attributes. - Abhin Shah; Maohao Shen; Jongha Jon Ryu; Subhro Das; Prasanna Sattigeri; Yuheng Bu; Gregory W. Wornell (CoRR, 2023)

Non-asymptotic System Identification for Linear Systems with Nonlinear Policies. - Yingying Li; Tianpeng Zhang; Subhro Das; Jeff S. Shamma; Na Li 002 (CoRR, 2023)

GAT-GMM: Generative Adversarial Training for Gaussian Mixture Models. - Farzan Farnia; William W. Wang; Subhro Das; Ali Jadbabaie (SIAM J. Math. Data Sci., 2023)

A Model for Estimating the Economic Costs of Computer Vision Systems That Use Deep Learning. - Neil Thompson; Martin Fleming; Benny J. Tang; Anna M. Pastwa; Nicholas Borge; Brian C. Goehring; Subhro Das (AAAI, 2024)

One Step Closer to Unbiased Aleatoric Uncertainty Estimation. - Wang Zhang; Ziwen Martin Ma; Subhro Das; Tsui-Wei Lily Weng; Alexandre Megretski; Luca Daniel; Lam M. Nguyen (AAAI, 2024)

Thermometer: Towards Universal Calibration for Large Language Models. - Maohao Shen; Subhro Das; Kristjan H. Greenewald; Prasanna Sattigeri; Gregory W. Wornell; Soumya Ghosh (ICML, 2024)

Group Fairness with Uncertain Sensitive Attributes. - Abhin Shah; Maohao Shen; Jongha Jon Ryu; Subhro Das; Prasanna Sattigeri; Yuheng Bu; Gregory W. Wornell (ISIT, 2024)

Neural Network Reparametrization for Accelerated Optimization in Molecular Simulations. - Nima Dehmamy; Csaba Both; Jeet Mohapatra; Subhro Das; Tommi Jaakkola (NeurIPS, 2024)

Are Uncertainty Quantification Capabilities of Evidential Deep Learning a Mirage? - Maohao Shen; Jongha Jon Ryu; Soumya Ghosh; Yuheng Bu; Prasanna Sattigeri; Subhro Das; Gregory W. Wornell (NeurIPS, 2024)

Decentralized fused-learner architectures for Bayesian reinforcement learning. - Augustin-Alexandru Saucan; Subhro Das; Moe Z. Win (Artif. Intell., 2024)

Improved Evidential Deep Learning via a Mixture of Dirichlet Distributions. - Jongha Jon Ryu; Maohao Shen; Soumya Ghosh; Yuheng Bu; Prasanna Sattigeri; Subhro Das; Gregory W. Wornell (CoRR, 2024)

Guaranteeing Conservation Laws with Projection in Physics-Informed Neural Networks. - Anthony Baez; Wang Zhang; Ziwen Martin Ma; Subhro Das; Lam M. Nguyen; Luca Daniel (CoRR, 2024)

Correlated Attention in Transformers for Multivariate Time Series. - Quang Minh Nguyen; Lam M. Nguyen; Subhro Das (ICASSP, 2025)

Variance-reduced Clipping for Non-convex Optimization. - Amirhossein Reisizadeh; Haochuan Li; Subhro Das; Ali Jadbabaie (ICASSP, 2025)

Dilated Convolution for Time Series Learning. - Wang Zhang; Subhro Das; Lam M. Nguyen; Luca Daniel (ICASSP, 2025)

The RealHumanEval: Evaluating Large Language Models' Abilities to Support Programmers. - Hussein Mozannar; Valerie Chen; Mohammed Alsobay; Subhro Das; Sebastian Zhao; Dennis Wei; Manish Nagireddy; Prasanna Sattigeri; Ameet Talwalkar; David A. Sontag (Trans. Mach. Learn. Res., 2025)

Satori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM Reasoning via Autoregressive Search. - Maohao Shen; Guangtao Zeng; Zhenting Qi; Zhang-Wei Hong; Zhenfang Chen; Wei Lu; Gregory W. Wornell; Subhro Das; David Cox; Chuang Gan 001 (CoRR, 2025)

Granite Vision: a lightweight, open-source multimodal model for enterprise Intelligence. - Leonid Karlinsky; Assaf Arbelle; Abraham Daniels; Ahmed Nassar; Amit Alfassi; Bo Wu 018; Eli Schwartz; Dhiraj Joshi; Jovana Kondic; Nimrod Shabtay; Pengyuan Li 001; Roei Herzig; et al. (CoRR, 2025)

Satori-SWE: Evolutionary Test-Time Scaling for Sample-Efficient Software Engineering. - Guangtao Zeng; Maohao Shen; Delin Chen; Zhenting Qi; Subhro Das; Dan Gutfreund; David Cox; Gregory W. Wornell; Wei Lu; Zhang-Wei Hong; Chuang Gan 001 (CoRR, 2025)