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Showing 1 to 5 of 5 for “"Importance weighting"”.

  1. Addressing two issues in machine learning : interpretability and dataset shift

    … distributions. In particular, the existing importance weighting approach to handling covariate shift suffers from high variance if the two covariate distributions are very different. I develop a dimension reduction procedure that reduces this variance, at the expense of increased bias. …

    mit Repository record for Addressing two issues in machine learning : interpretability and dataset shift (opens in a new tab)

  2. Retrospective Policy Gradient

    … propose a power-mean correction for the multiple importance weighting estimator and introduce the Retrospective Policy Gradient (RPG), a PG algorithm that integrates both past and current trajectories for policy updates. Our implementation builds upon standard RL frameworks and supports both …

    uic

  3. Multi-Objective Generation of Pareto-Optimal Perception Architectures for Autonomous Robotic Systems

    … and revealing sensitivity to voxel size and importance weighting. In the ANYmal-C study, the compact, uniformly weighted ROI yields a flatter Pareto front with 25 Pareto-optimal designs, and underscores how intrinsic sensor parameters (e.g. angular resolution, and Field of View) dominate …

    mit Repository record for Multi-Objective Generation of Pareto-Optimal Perception Architectures for Autonomous Robotic Systems (opens in a new tab)

  4. One-pass algorithms for large and shifting data sets

    … test phases using two different techniques: an importance weighting scheme and kernel mean matching. Our results on a toy problem and the real-world KDD ’99 data show an increase in performance to our VS framework. Our final contribution involves applying the one-pass VS algorithm, along with …

    soton Repository record for One-pass algorithms for large and shifting data sets (opens in a new tab)

  5. Distribution distance measures in generative and privacy models

    … three main contributions: (1) a novel use of importance weights to modify the output distribution of a generative model, (2) an application and evaluation of a generative model for medical data privacy, and (3) a novel method for private data synthesis using support points and differential …

    texas Repository record for Distribution distance measures in generative and privacy models (opens in a new tab)