2026
1.
Yassine Chemingui; Chenhua Fan; Honghao Wei; Janardhan Rao Doppa
SteinGate: Tail-Sensitive Safe Reinforcement Learning via Stein Discrepancy Miscellaneous
2026, (arXiv:2607.13175 [cs.LG]).
Abstract | Links | BibTeX | Tags: Computer Science - Artificial Intelligence, Computer Science - Machine Learning
@misc{chemingui_steingate_2026,
title = {SteinGate: Tail-Sensitive Safe Reinforcement Learning via Stein Discrepancy},
author = {Yassine Chemingui and Chenhua Fan and Honghao Wei and Janardhan Rao Doppa},
url = {http://arxiv.org/abs/2607.13175},
doi = {10.48550/arXiv.2607.13175},
year = {2026},
date = {2026-07-01},
urldate = {2026-07-01},
publisher = {arXiv},
abstract = {Safe reinforcement learning typically enforces safety by bounding expected cumulative costs, a criterion that often fails to detect rare but catastrophic tail events. To overcome these limitations, this paper introduces SteinGate, a boundary-aware distributional safety certificate that replaces fragile tail fitting with a robust consistency check using Kernelized Stein Discrepancy while accounting for boundary atoms induced by clipped costs. SteinGate evaluates whether observed policy rollout costs remain consistent with a safe reference distribution, providing a non-parametric safety certificate. This certificate is used to dynamically adapt the learning regime: favoring reward-improving policy updates when rollouts remain consistent with the safe reference and switching to recovery behavior when the cost tail deviates. Experiments on continuous-control benchmarks demonstrate that SteinGate significantly reduces both the frequency and severity of constraint violations during training while maintaining competitive returns relative to state-of-the-art baselines.},
note = {arXiv:2607.13175 [cs.LG]},
keywords = {Computer Science - Artificial Intelligence, Computer Science - Machine Learning},
pubstate = {published},
tppubtype = {misc}
}
Safe reinforcement learning typically enforces safety by bounding expected cumulative costs, a criterion that often fails to detect rare but catastrophic tail events. To overcome these limitations, this paper introduces SteinGate, a boundary-aware distributional safety certificate that replaces fragile tail fitting with a robust consistency check using Kernelized Stein Discrepancy while accounting for boundary atoms induced by clipped costs. SteinGate evaluates whether observed policy rollout costs remain consistent with a safe reference distribution, providing a non-parametric safety certificate. This certificate is used to dynamically adapt the learning regime: favoring reward-improving policy updates when rollouts remain consistent with the safe reference and switching to recovery behavior when the cost tail deviates. Experiments on continuous-control benchmarks demonstrate that SteinGate significantly reduces both the frequency and severity of constraint violations during training while maintaining competitive returns relative to state-of-the-art baselines.
2.
William Solow; Paola Pesantez-Cabrera; Markus Keller; Lav Khot; Sandhya Saisubramanian; Alan Fern
A Hybrid Modeling Framework for Crop Prediction Tasks via Dynamic Parameter Calibration and Multi-Task Learning Working paper
2026, (arXiv:2603.15411 [cs.AI]).
Abstract | Links | BibTeX | Tags: Computer Science - Artificial Intelligence, Computer Science - Machine Learning
@workingpaper{solow_hybrid_2026,
title = {A Hybrid Modeling Framework for Crop Prediction Tasks via Dynamic Parameter Calibration and Multi-Task Learning},
author = {William Solow and Paola Pesantez-Cabrera and Markus Keller and Lav Khot and Sandhya Saisubramanian and Alan Fern},
url = {http://arxiv.org/abs/2603.15411},
doi = {10.48550/arXiv.2603.15411},
year = {2026},
date = {2026-05-01},
urldate = {2026-05-01},
publisher = {arXiv},
abstract = {Accurate prediction of crop states (e.g., phenology stages and cold hardiness) is essential for timely farm management decisions such as irrigation, fertilization, and canopy management to optimize crop yield and quality. While traditional biophysical models can be used for season-long predictions, they lack the precision required for site-specific management. Deep learning methods are a compelling alternative, but can produce biologically unrealistic predictions and require large-scale data. We propose a textbackslashemphhybrid modeling approach that uses a neural network to parameterize a differentiable biophysical model and leverages multi-task learning for efficient data sharing across crop cultivars in data limited settings. By predicting the textbackslashemphparameters of the biophysical model, our approach improves the prediction accuracy while preserving biological realism. Empirical evaluation using real-world and synthetic datasets demonstrates that our method improves prediction accuracy by 60textbackslash% for phenology and 40textbackslash% for cold hardiness compared to deployed biophysical models.},
note = {arXiv:2603.15411 [cs.AI]},
keywords = {Computer Science - Artificial Intelligence, Computer Science - Machine Learning},
pubstate = {published},
tppubtype = {workingpaper}
}
Accurate prediction of crop states (e.g., phenology stages and cold hardiness) is essential for timely farm management decisions such as irrigation, fertilization, and canopy management to optimize crop yield and quality. While traditional biophysical models can be used for season-long predictions, they lack the precision required for site-specific management. Deep learning methods are a compelling alternative, but can produce biologically unrealistic predictions and require large-scale data. We propose a textbackslashemphhybrid modeling approach that uses a neural network to parameterize a differentiable biophysical model and leverages multi-task learning for efficient data sharing across crop cultivars in data limited settings. By predicting the textbackslashemphparameters of the biophysical model, our approach improves the prediction accuracy while preserving biological realism. Empirical evaluation using real-world and synthetic datasets demonstrates that our method improves prediction accuracy by 60textbackslash% for phenology and 40textbackslash% for cold hardiness compared to deployed biophysical models.
2024
3.
Syrine Belakaria; Benjamin Letham; Janardhan Rao Doppa; Barbara Engelhardt; Stefano Ermon; Eytan Bakshy
Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian Processes Proceedings Article
In: The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024.
Abstract | Links | BibTeX | Tags: Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Statistics - Machine Learning
@inproceedings{belakaria_active_2024,
title = {Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian Processes},
author = {Syrine Belakaria and Benjamin Letham and Janardhan Rao Doppa and Barbara Engelhardt and Stefano Ermon and Eytan Bakshy},
url = {https://openreview.net/forum?id=da0ZJatRCN},
doi = {10.48550/arXiv.2407.09739},
year = {2024},
date = {2024-11-06},
urldate = {2024-11-06},
publisher = {The Thirty-eighth Annual Conference on Neural Information Processing Systems},
abstract = {We consider the problem of active learning for global sensitivity analysis of expensive black-box functions. Our aim is to efficiently learn the importance of different input variables, e.g., in vehicle safety experimentation, we study the impact of the thickness of various components on safety objectives. Since function evaluations are expensive, we use active learning to prioritize experimental resources where they yield the most value. We propose novel active learning acquisition functions that directly target key quantities of derivative-based global sensitivity measures (DGSMs) under Gaussian process surrogate models. We showcase the first application of active learning directly to DGSMs, and develop tractable uncertainty reduction and information gain acquisition functions for these measures. Through comprehensive evaluation on synthetic and real-world problems, our study demonstrates how these active learning acquisition strategies substantially enhance the sample efficiency of DGSM estimation, particularly with limited evaluation budgets. Our work paves the way for more efficient and accurate sensitivity analysis in various scientific and engineering applications.},
keywords = {Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Statistics - Machine Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
We consider the problem of active learning for global sensitivity analysis of expensive black-box functions. Our aim is to efficiently learn the importance of different input variables, e.g., in vehicle safety experimentation, we study the impact of the thickness of various components on safety objectives. Since function evaluations are expensive, we use active learning to prioritize experimental resources where they yield the most value. We propose novel active learning acquisition functions that directly target key quantities of derivative-based global sensitivity measures (DGSMs) under Gaussian process surrogate models. We showcase the first application of active learning directly to DGSMs, and develop tractable uncertainty reduction and information gain acquisition functions for these measures. Through comprehensive evaluation on synthetic and real-world problems, our study demonstrates how these active learning acquisition strategies substantially enhance the sample efficiency of DGSM estimation, particularly with limited evaluation budgets. Our work paves the way for more efficient and accurate sensitivity analysis in various scientific and engineering applications.
4.
Yuanjie Shi; Subhankar Ghosh; Taha Belkhouja; Janardhan Rao Doppa; Yan Yan
Conformal Prediction for Class-wise Coverage via Augmented Label Rank Calibration Proceedings Article
In: The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024.
Abstract | Links | BibTeX | Tags: Computer Science - Machine Learning
@inproceedings{shi_conformal_2024,
title = {Conformal Prediction for Class-wise Coverage via Augmented Label Rank Calibration},
author = {Yuanjie Shi and Subhankar Ghosh and Taha Belkhouja and Janardhan Rao Doppa and Yan Yan},
url = {https://dl.acm.org/doi/10.5555/3737916.3742116},
doi = {10.5555/3737916.3742116},
year = {2024},
date = {2024-06-11},
urldate = {2024-06-11},
publisher = {The Thirty-eighth Annual Conference on Neural Information Processing Systems},
abstract = {Conformal prediction (CP) is an emerging uncertainty quantification framework that allows us to construct a prediction set to cover the true label with a pre-specified marginal or conditional probability. Although the valid coverage guarantee has been extensively studied for classification problems, CP often produces large prediction sets which may not be practically useful. This issue is exacerbated for the setting of class-conditional coverage on imbalanced classification tasks. This paper proposes the Rank Calibrated Class-conditional CP (RC3P) algorithm to reduce the prediction set sizes to achieve class-conditional coverage, where the valid coverage holds for each class. In contrast to the standard class-conditional CP (CCP) method that uniformly thresholds the class-wise conformity score for each class, the augmented label rank calibration step allows RC3P to selectively iterate this class-wise thresholding subroutine only for a subset of classes whose class-wise top-k error is small. We prove that agnostic to the classifier and data distribution, RC3P achieves class-wise coverage. We also show that RC3P reduces the size of prediction sets compared to the CCP method. Comprehensive experiments on multiple real-world datasets demonstrate that RC3P achieves class-wise coverage and 26.25% reduction in prediction set sizes on average.},
keywords = {Computer Science - Machine Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
Conformal prediction (CP) is an emerging uncertainty quantification framework that allows us to construct a prediction set to cover the true label with a pre-specified marginal or conditional probability. Although the valid coverage guarantee has been extensively studied for classification problems, CP often produces large prediction sets which may not be practically useful. This issue is exacerbated for the setting of class-conditional coverage on imbalanced classification tasks. This paper proposes the Rank Calibrated Class-conditional CP (RC3P) algorithm to reduce the prediction set sizes to achieve class-conditional coverage, where the valid coverage holds for each class. In contrast to the standard class-conditional CP (CCP) method that uniformly thresholds the class-wise conformity score for each class, the augmented label rank calibration step allows RC3P to selectively iterate this class-wise thresholding subroutine only for a subset of classes whose class-wise top-k error is small. We prove that agnostic to the classifier and data distribution, RC3P achieves class-wise coverage. We also show that RC3P reduces the size of prediction sets compared to the CCP method. Comprehensive experiments on multiple real-world datasets demonstrate that RC3P achieves class-wise coverage and 26.25% reduction in prediction set sizes on average.
5.
Mohammed Amine Gharsallaoui; Bhupinderjeet Singh; Supriya Savalkar; Aryan Deshwal; Yan Yan; Ananth Kalyanaraman; Kirti Rajagopalan; Janardhan Rao Doppa
Streamflow Prediction with Uncertainty Quantification for Water Management: A Constrained Reasoning and Learning Approach Miscellaneous
2024, (arXiv:2406.00133 [cs]).
Abstract | Links | BibTeX | Tags: Computer Science - Artificial Intelligence, Computer Science - Machine Learning
@misc{gharsallaoui_streamflow_2024,
title = {Streamflow Prediction with Uncertainty Quantification for Water Management: A Constrained Reasoning and Learning Approach},
author = {Mohammed Amine Gharsallaoui and Bhupinderjeet Singh and Supriya Savalkar and Aryan Deshwal and Yan Yan and Ananth Kalyanaraman and Kirti Rajagopalan and Janardhan Rao Doppa},
url = {https://www.ijcai.org/proceedings/2024/0804.pdf},
doi = {10.24963/ijcai.2024/804},
year = {2024},
date = {2024-05-01},
urldate = {2024-05-01},
publisher = {arXiv},
abstract = {Predicting the spatiotemporal variation in streamflow along with uncertainty quantification enables decision-making for sustainable management of scarce water resources. Process-based hydrological models (aka physics-based models) are based on physical laws, but using simplifying assumptions which can lead to poor accuracy. Data-driven approaches offer a powerful alternative, but they require large amount of training data and tend to produce predictions that are inconsistent with physical laws. This paper studies a constrained reasoning and learning (CRL) approach where physical laws represented as logical constraints are integrated as a layer in the deep neural network. To address small data setting, we develop a theoretically-grounded training approach to improve the generalization accuracy of deep models. For uncertainty quantification, we combine the synergistic strengths of Gaussian processes (GPs) and deep temporal models (i.e., deep models for time-series forecasting) by passing the learned latent representation as input to a standard distance-based kernel. Experiments on multiple real-world datasets demonstrate the effectiveness of both CRL and GP with deep kernel approaches over strong baseline methods.},
note = {arXiv:2406.00133 [cs]},
keywords = {Computer Science - Artificial Intelligence, Computer Science - Machine Learning},
pubstate = {published},
tppubtype = {misc}
}
Predicting the spatiotemporal variation in streamflow along with uncertainty quantification enables decision-making for sustainable management of scarce water resources. Process-based hydrological models (aka physics-based models) are based on physical laws, but using simplifying assumptions which can lead to poor accuracy. Data-driven approaches offer a powerful alternative, but they require large amount of training data and tend to produce predictions that are inconsistent with physical laws. This paper studies a constrained reasoning and learning (CRL) approach where physical laws represented as logical constraints are integrated as a layer in the deep neural network. To address small data setting, we develop a theoretically-grounded training approach to improve the generalization accuracy of deep models. For uncertainty quantification, we combine the synergistic strengths of Gaussian processes (GPs) and deep temporal models (i.e., deep models for time-series forecasting) by passing the learned latent representation as input to a standard distance-based kernel. Experiments on multiple real-world datasets demonstrate the effectiveness of both CRL and GP with deep kernel approaches over strong baseline methods.
