publications

Selected publications in reversed chronological order. Full list of papers: Google Scholar.

• 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 Sontag
  Transactions on Machine Learning Research (TMLR), 2025.
• Correlated Attention in Transformers for Multivariate Time Series
  Quang Nguyen, Lam Nguyen, Subhro Das
  IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2025.
• Dilated Convolution for Time Series Learning
  Wang Zhang, Subhro Das, Lam Nguyen, Luca Daniel
  IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2025.
• Variance-reduced clipping for non-convex optimization
  Amirhossein Reisizadeh, Haochuan Li, Subhro Das, Ali Jadbabaie
  IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2025.
• Improved Evidential Deep Learning via a Mixture of Dirichlet Distributions
  J. Jon Ryu, Maohao Shen, Soumya Ghosh, Yuheng Bu, Prasanna Sattigeri, Subhro Das, Gregory Wornell
  Neural Information Processing Systems (NeurIPS), 2024.
• Neural Network Reparametrization for Accelerated Optimization in Molecular Simulations
  Nima Dehmamy, Csaba Both, Jeet Mohapatra, Subhro Das, Tommi Jaakkola
  Neural Information Processing Systems (NeurIPS), 2024.
• Thermometer: Towards Universal Calibration for Large Language Models
  Maohao Shen, Subhro Das, Kristjan Greenewald, Prasanna Sattigeri, Gregory Wornell, Soumya Ghosh
  International Conference on Machine Learning (ICML), 2024.
• One step closer to unbiased aleatoric uncertainty estimation
  Wang Zhang, Martin Ma, Subhro Das, Lily Weng, Alexandre Megretski, Luca Daniel, Lam Nguyen
  AAAI Conference on Artificial Intelligence (AAAI), 2024.
• Decentralized fused-learner architectures for Bayesian reinforcement learning
  Augustin Saucan, Subhro Das, Moe Win
  Artificial Intelligence Journal, 2024.
• A model for estimating the economic costs of computer vision systems that use deep learning
  Neil Thompson, Martin Fleming, Benny Tang, Anna Pastwa, Nicholas Borge, Brian Goehring, Subhro Das
  Conference on Innovative Applications of Artificial Intelligence (IAAI), 2024.
• Effective Human-AI Teams via Learned Natural Language Rules and Onboarding
  Hussein Mozannar, Jimin J Lee, Dennis Wei, Prasanna Sattigeri, Subhro Das, David Sontag
  Neural Information Processing Systems (NeurIPS), 2023.
• ConCerNet: A contrastive learning based framework for automated conservation law discovery
  Wang Zhang, Tsui-Wei Weng, Subhro Das, Alexandre Megretski, Luca Daniel, Lam Nguyen
  International Conference on Machine Learning (ICML), 2023.
• Label-free Concept Bottleneck Models
  Tuomas Oikarinen, Subhro Das, Lam M. Nguyen, Tsui-Wei Weng
  International Conference on Learning Representations (ICLR), 2023.
• Post-hoc uncertainty learning using a dirichlet meta-model
  Maohao Shen, Yuheng Bu, Prasanna Sattigeri, Soumya Ghosh, Subhro Das, Gregory Wornell
  AAAI Conference on Artificial Intelligence (AAAI), 2023.
• Exact algorithms for learning to defer with halfspaces
  Hussein Mozannar, Hunter Lang, Dennis Wei, Prasanna Sattigeri, Subhro Das, and David Sontag
  International Conference on Artificial Intelligence and Statistics (AISTATS), 2023.
• Reliable gradient-free and likelihood-free prompt tuning
  Maohao Shen, Soumya Ghosh, Prasanna Sattigeri, Subhro Das, Yuheng Bu, Gregory Wornell
  European Chapter of the Association for Computational Linguistics Conference (EACL), 2023.
• Attacking c-MARL more effectively: A data driven approach
  Nhan Pham, Lam Nguyen, Jie Chen, Hoang Thanh Lam, Subhro Das, Tsui-Wei Weng
  IEEE International Conference on Data Mining (ICDM), 2023.
• Variance reduction for faster decentralized general convex optimization
  Ran Xin, Subhro Das, Soummya Kar, Usman Khan
  IEEE Conference on Decision and Control (CDC), 2023.
• GAT-GMM: Generative Adversarial Training for Gaussian Mixture Models
  Farzan Farnia, William Wang, Subhro Das, Ali Jadbabaie
  SIAM Journal on Mathematics of Data Science (SIMODS), 2022.
• On Convergence of Gradient Descent Ascent: A Tight Local Analysis
  Haochuan Li, Farzan Farnia, Subhro Das, Ali Jadbabaie
  International Conference on Machine Learning (ICML), 2022.
• Beyond Worst-Case Analysis in Stochastic Approximation: Moment Estimation Improves Instance Complexity
  Jingzhao Zhang, Hongzhou Lin, Subhro Das, Suvrit Sra, Ali Jadbabaie
  International Conference on Machine Learning (ICML), 2022.
• Selective Regression under Fairness Criteria
  Abhin Shah, Yuheng Bu, Joshua Lee, Subhro Das, Rameswar Panda, Prasanna Sattigeri, Gregory Wornell
  International Conference on Machine Learning (ICML), 2022.
• On observability and optimal gain design for distributed linear filtering and prediction
  Subhro Das
  European Signal Processing Conference (EUSIPCO), 2022.
• Practical Skills Demand Forecasting via Representation Learning of Temporal Dynamics
  Maysa Macedo, Wyatt Clarke, Eli Lucherini, Tyler Baldwin, Dilermando Neto, Rogerio Paula, Subhro Das
  AAAI/ACM Conference on AI, Ethics, and Society (AIES), 2022.
• Better Skill-based Job Representations, Assessed via Job Transition Data
  Tyler Baldwin, Wyatt Clarke, Maysa Macedo, Rogerio Paula, Subhro Das
  IEEE International Conference on Big Data (BigData), 2022.
• Online Optimal Control with Affine Constraints
  Yingying Li, Subhro Das, Na Li
  AAAI Conference on Artificial Intelligence (AAAI), 2021.
• Fair selective classification via sufficiency
  Joshua Lee, Yuheng Bu, Deepta Rajan, Prasanna Sattigeri, Rameswar Panda, Subhro Das, Greg Wornell
  International Conference on Machine Learning (ICML), 2021.
• Verifiably Safe Exploration for End-to-End Reinforcement Learning
  Nathan Hunt, Nathan Fulton, Sara Magliacane, Nghia Hoang, Subhro Das, Armando Solar-Lezama
  ACM International Conference on Hybrid Systems: Computation and Control (HSCC), 2021.
• NEO: NEuro-Inspired Optimization — A Fractional Time Series Approach
  Sarthak Chatterjee, Subhro Das, Sérgio Pequito
  Frontiers in physiology, 2021.
• Learning Occupational Task-Shares Dynamics for the Future of Work
  Subhro Das, Sebastian Steffen, Wyatt Clarke, Prabhat Reddy, Erik Brynjolfsson, Martin Fleming
  AAAI/ACM Conference on AI, Ethics, and Society (AIES), 2020.
• Efficient Goal Attainment and Engagement in a Care Manager System Using Unstructured Notes
  Sara Rosenthal, Subhro Das, Pei-Yun Hsueh, Ken Barker, Ching-Hua Chen
  Journal of the American Medical Informatics Association (JAMIA) Open, 2020.
• Interpretable Subgroup Discovery in Treatment Effect Estimation with Application to Opioid Prescribing Guidelines
  Chirag Nagpal, Dennis Wei, Bhanukiran Vinzamuri, Monica Shekhar, Sara Berger, Subhro Das, Kush Varshney
  ACM Conference on Health, Inference, and Learning (CHIL), 2020.
• An adaptive, data-driven personalized advisor for increasing physical activity
  Zhiguo Li, Subhro Das, James Codella, Tian Hao, Kun Lin, Chandramouli Maduri, Ching-Hua Chen
  IEEE Journal of Biomedical and Health Informatics (JBHI), 2018.
• Learning to Personalize from Practice: A RWE Approach of Care Plan Personalization based on Differential Patient Behavioral Responses
  Pei-Yun Hsueh, Subhro Das, Chandramouli Maduri, Karie Kelly
  (AMIA) Annual Symposium Proceedings, 2018.
• Consensus+ innovations distributed Kalman filter with optimized gains
  Subhro Das and José MF Moura
  IEEE Transactions on Signal Processing (TSP), 2017.
• Distributed Kalman filtering with dynamic observations consensus
  Subhro Das and José M. F. Moura
  IEEE Transactions on Signal Processing (TSP), 2015.
• Distributed state estimation in multi-agent networks
  Subhro Das and José M. F. Moura
  IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2013.