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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

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0111 · Dec 202219922001200920172026
5 results for Cost-Driven

Study learns state representations from observations for control, proving guarantees.

problem Learning state representations from high-dimensional observations for control.
method Cost-driven approach, learning latent state model to predict costs.
result Proves finite-sample guarantees for near-optimal state representation and controller.

Study cost-driven state representation learning for control from partial observations.

problem Learning state representation for control from partial and high-dimensional observations.
method Cost-driven state representation learning via predicting cumulative costs.
result Established finite-sample guarantees for near-optimal representation and controller.

RETR improves indoor radar perception with a novel transformer model.

problem Indoor radar perception lacks models tailored for multi-view radar settings.
method RETR extends DETR architecture with depth-prioritized feature similarity, tri-plane loss, and radar-to-camera transformation.
result RETR outperforms state-of-the-art methods by 15.38+ AP for object detection and 11.91+ IoU for instance segmentation.

New pricing framework allocates costs of operating reserves and transmission.

problem Allocating costs of operating reserves and transmission efficiently.
method Causation-based framework using contingency-constrained scheduling models.
result More comprehensive and efficient cost-reflective market operations.

SANE improves exploration of noisy, multimodal functions by finding multiple optima.

problem Finding multiple optima in noisy, non-differentiable functions.
method Strategic Autonomous Non-Smooth Exploration (SANE) with a cost-driven acquisition function and human knowledge gate.
result SANE outperforms classical Bayesian optimization in discovering multiple optima.