Deep learning predicts SAH patient mortality from initial CT scans.
problem High mortality rates in SAH patients.
method CNN-based algorithm using transfer learning on CT scans.
result Model accurately predicts mortality (74% accuracy, 82% AUC).
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15 results for “SAH”
We show that an orientable pseudo-Anosov homeomorphism has vanishing Sah-Arnoux-Fathi invariant if and only if the minimal polynomial of its dilatation is not reciprocal. We relate this to works of Margalit-Spallone and Birman, Brinkmann and Kawamuro. Mainly, we use Veech's construction of pseudo-Anosov maps to give ex…
Foam cobordism groups linked to interval exchange automorphisms.
problem Understanding cobordism groups of foams.
method Using the Sah-Arnoux-Fathi invariant and K-theory.
result Identified cobordism groups with homology and K-groups.
AI tool automates blood segmentation from head CT scans after SAH.
problem Accurate volumetric assessment of SAH patients for clinical and prognostic implications.
method Transformer-based Swin UNETR architecture for noncontrast CT scans.
result High accuracy and robust performance across internal and external validation cohorts.
Improved reinforcement learning for episodes with varying action sets.
problem Reinforcement learning with context-dependent action sets.
method Extends MVP algorithm to handle adversarial and stochastic contexts.
result Established minimax regret bounds of for adversarial contexts.
New algorithm reduces RL policy optimization gap.
problem Insufficient theoretical understanding of policy optimization methods.
method Reference-based Policy Optimization with Stable at Any Time guarantee (RPO-SAT)
result Achieves nearly minimax optimal policy-based algorithm for tabular RL.
New algorithm reduces sample complexity for safe reinforcement learning.
problem Safe reinforcement learning in constrained MDPs with performance and safety constraints.
method Model-based primal-dual algorithm balancing regret and bounded constraint violations.
result Proves near-optimal policies with bounded violations or zero violations in CMDPs.
Taxicab correspondence analysis visualizes sparse text data sets.
problem Visualization of extremely sparse contingency tables.
method Robust variant of correspondence analysis for sparse data.
result Visualized an 8265-dimensional textual data set.
New approach for reward-free exploration reduces estimation error.
problem Reward-free exploration in reinforcement learning.
method Adaptive approach reducing MDP estimation error.
result Reward-free UCRL algorithm improves sample complexity.
New algorithm for personalized healthcare with privacy guarantees.
problem Online exploration in reinforcement learning with differential privacy constraints.
method ε-JDP algorithm with privately released exploration bonuses and visitation statistics.
result Regret bound of matching information-theoretic lower bound.
New algorithms find optimal policies without knowing MDP span.
problem Finding optimal policies in MDPs without knowing span.
method Horizon calibration and span penalization techniques.
result First algorithms achieving optimal span-based complexity without prior knowledge.
A new RL framework allows removing user data without affecting performance.
problem Efficiently removing user data from a reinforcement learning model without affecting performance.
method Formulated a -TV-stable RL algorithm for tabular MDPs that supports exact unlearning.
result Achieved a nearly minimax optimal regret bound of for -TV-stable RL algorithms.
New algorithm finds optimal policies without knowing reward functions.
problem Reward-agnostic exploration in reinforcement learning.
method Designs an algorithm that explores without reward information, achieving minimax optimality.
result Achieves provable minimax optimality in finding optimal policies for multiple reward functions.
New RL algorithm reduces sample complexity for optimal learning.
problem Achieving optimal learning with minimal samples in RL.
method Early-settled variance reduction method with Q-learning sequences.
result Near-optimal regret achieved with sample size .
New algorithm reduces MDP regret by accounting for state suboptimality gaps and variance.
problem Reducing regret in episodic MDPs with varying state suboptimality gaps.
method Introduced MVP algorithm with variance-aware gap-dependent regret bound.
result Achieved a variance-aware gap-dependent regret bound for MDPs.