Alex Schubert
@alexmschubert
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PhD Student in Computational Precision Health at @UCBerkeley
Joined December 2021
This work was co-led by Ali Shirali and me, along with @_ahmedmalaa. For more information see: . arXiv:
ieeexplore.ieee.org
Medical treatments often involve a sequence of decisions, each informed by previous outcomes. This process closely aligns with reinforcement learning (RL), a framework for optimizing sequential...
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We evaluate our method in off-policy and offline settings using simulated environments and real health records from intensive care units, showing strong performance compared to common offline RL techniques.
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RL approaches in healthcare often only reward models based on sparse outcomes such as 30-day mortality. Our solution? Prune the action space before learning. By doing so, we can effectively integrate intermediate biomarker signals without distorting the main objective.
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How can reinforcement learning better leverage ICU biomarker signals for decision-making? Our new paper introduces PrunedQL—a two-stage algorithm that identifies actions to avoid using these signals, then optimizes patient outcomes. Check it out! .
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RT @_ahmedmalaa: Excited to share the camera ready of our #ICLR2024 paper "InstructCV: Instruction-Tuned Text-to-Image Diffusion Models as….
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RT @_ahmedmalaa: Excited to share a new preprint! InstructCV is a language interface for vision tasks trained by instruction-tuning a text-….
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RT @_ahmedmalaa: Generative models can synthesize medical images but assessing quality & clinical validity is challenging. In this work w/….
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