Compositional Q-learning for electrolyte repletion with imbalanced patient sub-populations
Fuente:
arXiv
Saved in:
| Main Authors: | Mandyam, Aishwarya, Jones, Andrew, Yao, Jiayu, Laudanski, Krzysztof, Engelhardt, Barbara |
|---|---|
| Format: | Preprint |
| Published: |
2021
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Kernel Density Bayesian Inverse Reinforcement Learning
by: Mandyam, Aishwarya, et al.
Published: (2023)
by: Mandyam, Aishwarya, et al.
Published: (2023)
APRIL: Annotations for Policy evaluation with Reliable Inference from LLMs
by: Mandyam, Aishwarya, et al.
Published: (2025)
by: Mandyam, Aishwarya, et al.
Published: (2025)
Adaptive Interventions with User-Defined Goals for Health Behavior Change
by: Mandyam, Aishwarya, et al.
Published: (2023)
by: Mandyam, Aishwarya, et al.
Published: (2023)
CANDOR: Counterfactual ANnotated DOubly Robust Off-Policy Evaluation
by: Mandyam, Aishwarya, et al.
Published: (2024)
by: Mandyam, Aishwarya, et al.
Published: (2024)
Anchor-based oversampling for imbalanced tabular data via contrastive and adversarial learning
by: Mohammadi, Hadi, et al.
Published: (2025)
by: Mohammadi, Hadi, et al.
Published: (2025)
CLIMB: Class-imbalanced Learning Benchmark on Tabular Data
by: Liu, Zhining, et al.
Published: (2025)
by: Liu, Zhining, et al.
Published: (2025)
VAE-Inf: A statistically interpretable generative paradigm for imbalanced classification
by: Wu, Hongfei, et al.
Published: (2026)
by: Wu, Hongfei, et al.
Published: (2026)
Deep Double Q-learning
by: Nagarajan, Prabhat, et al.
Published: (2025)
by: Nagarajan, Prabhat, et al.
Published: (2025)
Specialty detection in the context of telemedicine in a highly imbalanced multi-class distribution
by: Alomari, Alaa, et al.
Published: (2024)
by: Alomari, Alaa, et al.
Published: (2024)
Preference-Guided Diffusion for Multi-Objective Offline Optimization
by: Annadani, Yashas, et al.
Published: (2025)
by: Annadani, Yashas, et al.
Published: (2025)
Non-Myopic Multi-Objective Bayesian Optimization
by: Belakaria, Syrine, et al.
Published: (2024)
by: Belakaria, Syrine, et al.
Published: (2024)
MinMaxMin $Q$-learning
by: Soffair, Nitsan, et al.
Published: (2024)
by: Soffair, Nitsan, et al.
Published: (2024)
Is Q-learning an Ill-posed Problem?
by: Wissmann, Philipp, et al.
Published: (2025)
by: Wissmann, Philipp, et al.
Published: (2025)
Exploring the potential of prototype-based soft-labels data distillation for imbalanced data classification
by: Rosu, Radu-Andrei, et al.
Published: (2024)
by: Rosu, Radu-Andrei, et al.
Published: (2024)
PERRY: Policy Evaluation with Confidence Intervals using Auxiliary Data
by: Mandyam, Aishwarya, et al.
Published: (2025)
by: Mandyam, Aishwarya, et al.
Published: (2025)
Distance-informed Neural Processes
by: Venkataramanan, Aishwarya, et al.
Published: (2025)
by: Venkataramanan, Aishwarya, et al.
Published: (2025)
Stabilizing Extreme Q-learning by Maclaurin Expansion
by: Omura, Motoki, et al.
Published: (2024)
by: Omura, Motoki, et al.
Published: (2024)
In-Context Compositional Q-Learning for Offline Reinforcement Learning
by: Xu, Qiushui, et al.
Published: (2025)
by: Xu, Qiushui, et al.
Published: (2025)
Q-learning with Adjoint Matching
by: Li, Qiyang, et al.
Published: (2026)
by: Li, Qiyang, et al.
Published: (2026)
Automated Loss function Search for Class-imbalanced Node Classification
by: Guo, Xinyu, et al.
Published: (2024)
by: Guo, Xinyu, et al.
Published: (2024)
Object-centric Denoising Diffusion Models for Physical Reasoning
by: Lange, Moritz, et al.
Published: (2025)
by: Lange, Moritz, et al.
Published: (2025)
Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian Processes
by: Belakaria, Syrine, et al.
Published: (2024)
by: Belakaria, Syrine, et al.
Published: (2024)
Exclusively Penalized Q-learning for Offline Reinforcement Learning
by: Yeom, Junghyuk, et al.
Published: (2024)
by: Yeom, Junghyuk, et al.
Published: (2024)
Yes, Q-learning Helps Offline In-Context RL
by: Tarasov, Denis, et al.
Published: (2025)
by: Tarasov, Denis, et al.
Published: (2025)
UNIQ: Offline Inverse Q-learning for Avoiding Undesirable Demonstrations
by: Hoang, Huy, et al.
Published: (2024)
by: Hoang, Huy, et al.
Published: (2024)
A Real-time Anomaly Detection Using Convolutional Autoencoder with Dynamic Threshold
by: Maitra, Sarit, et al.
Published: (2024)
by: Maitra, Sarit, et al.
Published: (2024)
Symmetric Q-learning: Reducing Skewness of Bellman Error in Online Reinforcement Learning
by: Omura, Motoki, et al.
Published: (2024)
by: Omura, Motoki, et al.
Published: (2024)
Sharpe Ratio-Guided Active Learning for Preference Optimization in RLHF
by: Belakaria, Syrine, et al.
Published: (2025)
by: Belakaria, Syrine, et al.
Published: (2025)
QLASS: Boosting Language Agent Inference via Q-Guided Stepwise Search
by: Lin, Zongyu, et al.
Published: (2025)
by: Lin, Zongyu, et al.
Published: (2025)
Chemical Reaction Extraction from Long Patent Documents
by: Jadhav, Aishwarya, et al.
Published: (2024)
by: Jadhav, Aishwarya, et al.
Published: (2024)
Shared Parameter Subspaces and Cross-Task Linearity in Emergently Misaligned Behavior
by: Arturi, Daniel Aarao Reis, et al.
Published: (2025)
by: Arturi, Daniel Aarao Reis, et al.
Published: (2025)
WISER: Weak supervISion and supErvised Representation learning to improve drug response prediction in cancer
by: Shubham, Kumar, et al.
Published: (2024)
by: Shubham, Kumar, et al.
Published: (2024)
Understanding the theoretical properties of projected Bellman equation, linear Q-learning, and approximate value iteration
by: Lim, Han-Dong, et al.
Published: (2025)
by: Lim, Han-Dong, et al.
Published: (2025)
DIAR: Diffusion-model-guided Implicit Q-learning with Adaptive Revaluation
by: Park, Jaehyun, et al.
Published: (2024)
by: Park, Jaehyun, et al.
Published: (2024)
Cluster-guided Contrastive Class-imbalanced Graph Classification
by: Ju, Wei, et al.
Published: (2024)
by: Ju, Wei, et al.
Published: (2024)
Expert Q-learning: Deep Reinforcement Learning with Coarse State Values from Offline Expert Examples
by: Meng, Li, et al.
Published: (2021)
by: Meng, Li, et al.
Published: (2021)
Enhancing Robustness of Offline Reinforcement Learning Under Data Corruption via Sharpness-Aware Minimization
by: Xu, Le, et al.
Published: (2025)
by: Xu, Le, et al.
Published: (2025)
Stochastic Q-learning for Large Discrete Action Spaces
by: Fourati, Fares, et al.
Published: (2024)
by: Fourati, Fares, et al.
Published: (2024)
A finite time analysis of distributed Q-learning
by: Lim, Han-Dong, et al.
Published: (2024)
by: Lim, Han-Dong, et al.
Published: (2024)
Kernel-Based Distributed Q-Learning: A Scalable Reinforcement Learning Approach for Dynamic Treatment Regimes
by: Wang, Di, et al.
Published: (2023)
by: Wang, Di, et al.
Published: (2023)
Similar Items
-
Kernel Density Bayesian Inverse Reinforcement Learning
by: Mandyam, Aishwarya, et al.
Published: (2023) -
APRIL: Annotations for Policy evaluation with Reliable Inference from LLMs
by: Mandyam, Aishwarya, et al.
Published: (2025) -
Adaptive Interventions with User-Defined Goals for Health Behavior Change
by: Mandyam, Aishwarya, et al.
Published: (2023) -
CANDOR: Counterfactual ANnotated DOubly Robust Off-Policy Evaluation
by: Mandyam, Aishwarya, et al.
Published: (2024) -
Anchor-based oversampling for imbalanced tabular data via contrastive and adversarial learning
by: Mohammadi, Hadi, et al.
Published: (2025)