On-device research index

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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4 results for “R-Learning”

The Connection Between R-Learning and Inverse-Variance Weighting for Estimation of Heterogeneous Treatment Effectsstat.ME

R-Learning uses inverse-variance weights to estimate treatment effects more accurately.

problem Estimating heterogeneous treatment effects (CATEs) with stable and accurate methods.

method R-Learning with inverse-variance weights (IVWs) for pseudo-outcome regression.

result IVWs improve the stability and accuracy of CATE estimation.

R-Learning Inverse-Variance Weights CATEs Treatment Effects Pseudo-Outcome Regression

2023-07-19abs ↗pdf ↗

Noisy Linear Convergence of Stochastic Gradient Descent for CV@R Statistical Learning under Polyak-Łojasiewicz Conditionscs.LG

Stochastic GD converges linearly for CV@R learning under certain conditions.

problem Optimizing CV@R in statistical learning with non-convex loss functions.

method Stochastic Gradient Descent with Polyak-Łojasiewicz condition.

result Stochastic GD achieves linear convergence for CV@R learning.

CV@R Stochastic Gradient Descent Polyak-Łojasiewicz Linear Convergence Risk-Aware Learning

2020-12-14abs ↗pdf ↗

Deep R-Learning for Continual Area Sweepingcs.LG

Paper tackles non-uniform coverage planning for robots.

problem Non-uniform coverage planning for robots that need to visit some points more frequently.

method Proposes a novel reinforcement learning approach in a Semi-Markov Decision Process.

result Significant improvement over existing greedy approach in simulations.

reinforcement learning coverage planning robotics semi-Markov decision process service robotics

2020-05-31abs ↗pdf ↗

Combining T-learning and DR-learning: a framework for oracle-efficient estimation of causal contrastsstat.ML

EP-learning framework improves causal contrast estimation efficiency.

problem Estimating heterogeneous causal contrasts efficiently and stably.

method EP-learning framework combining T-learning and DR-learning.

result EP-learners are oracle-efficient and outperform competitors.

EP-learning causal contrasts T-learning DR-learning oracle efficiency

2024-02-03abs ↗pdf ↗