Preference-Optimal Multi-Metric Weighting for Parallel Coordinate Plots

• URL: http://arxiv.org/abs/2507.02905v1
• Date: Tue, 24 Jun 2025 06:48:09 GMT
• Title: Preference-Optimal Multi-Metric Weighting for Parallel Coordinate Plots
• Abstract summary: We propose a principled formulation for calculating the optimal weight based on a specific preferred metric combination.<n>We achieved this using various radar charts to visualize the metric trade-offs on the 2D plane reduced by UMAP.
• Score: 6.5422577283150245
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Parallel coordinate plots (PCPs) are a prevalent method to interpret the relationship between the control parameters and metrics. PCPs deliver such an interpretation by color gradation based on a single metric. However, it is challenging to provide such a gradation when multiple metrics are present. Although a naive approach involves calculating a single metric by linearly weighting each metric, such weighting is unclear for users. To address this problem, we first propose a principled formulation for calculating the optimal weight based on a specific preferred metric combination. Although users can simply select their preference from a two-dimensional (2D) plane for bi-metric problems, multi-metric problems require intuitive visualization to allow them to select their preference. We achieved this using various radar charts to visualize the metric trade-offs on the 2D plane reduced by UMAP. In the analysis using pedestrian flow guidance planning, our method identified unique patterns of control parameter importance for each user preference, highlighting the effectiveness of our method.

Related papers

• Fixed-Parameter Tractability of Private Synthetic Data Generation [84.99100741615423]
  We study the problem of generating synthetic data under differential privacy.<n>Our algorithms attain optimal error rates across all regimes and are realized by two different approaches.
  arXiv  Detail & Related papers  (2026-06-09T15:14:11Z)
• Beyond Chamfer Distance: Granular Order-aware Evaluation Metric For Online Mapping [9.868903360882262]
  State-of-the-art (SOTA) methods commonly predict map elements as ordered sequences of points that form polylines and polygons.<n>This framework lacks sensitivity to point ordering and provides limited granularity in assessing geometric quality.<n>We introduce sequence optimal sub-pattern assignment (SOSPA), an order-aware metric that enables fine-grained evaluation of individual geometries.
  arXiv  Detail & Related papers  (2026-05-21T14:51:52Z)
• Meta-Learned Basis Adaptation for Parametric Linear PDEs [0.0]
  We propose a framework for solving families of parametric linear partial differential equations (PDEs) by combining a meta-learned predictor with a least-squares corrector.<n>A lightweight meta-network maps PDE parameters to basis centers, widths, and activity patterns, thereby learning how the approximation space should adapt.<n>We evaluate the method on four linear PDE families spanning diffusion, transport, mixed advection--diffusion, and variable-speed transport.
  arXiv  Detail & Related papers  (2026-04-10T13:00:03Z)
• GIST: Targeted Data Selection for Instruction Tuning via Coupled Optimization Geometry [4.94446914034065]
  GIST (Gradient Isometric Subspace Transformation) replaces axis-aligned scaling with robust subspace alignment.<n>We show that GIST matches or outperforms the state-of-the-art baseline with only 0.29% of the storage and 25% of the computational time.
  arXiv  Detail & Related papers  (2026-02-20T19:44:24Z)
• Tuning-Free Structured Sparse Recovery of Multiple Measurement Vectors using Implicit Regularization [13.378211527081582]
  We introduce a tuning-free framework to recover sparse signals in multiple measurement vectors.<n>We show that the optimization dynamics exhibit a "momentum-like" effect, causing the norms of rows in the true support to grow significantly faster than others.
  arXiv  Detail & Related papers  (2025-12-03T02:53:11Z)
• PIPA: Preference Alignment as Prior-Informed Statistical Estimation [57.24096291517857]
  We introduce Pior-Informed Preference Alignment (PIPA), a unified, RL-free probabilistic framework.<n> PIPA accommodates both paired and unpaired data, as well as answer and step-level annotations.<n>By integrating different types of prior information, we developed two variations of PIPA: PIPA-M and PIPA-N.
  arXiv  Detail & Related papers  (2025-02-09T04:31:30Z)
• An Iterative Bayesian Approach for System Identification based on Linear Gaussian Models [86.05414211113627]
  We tackle the problem of system identification, where we select inputs, observe the corresponding outputs from the true system, and optimize the parameters of our model to best fit the data.<n>We propose a flexible and computationally tractable methodology that is compatible with any system and parametric family of models.
  arXiv  Detail & Related papers  (2025-01-28T01:57:51Z)
• Improved Diversity-Promoting Collaborative Metric Learning for Recommendation [127.08043409083687]
  Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems. This paper focuses on a challenging scenario where a user has multiple categories of interests. We propose a novel method called textitDiversity-Promoting Collaborative Metric Learning (DPCML)
  arXiv  Detail & Related papers  (2024-09-02T07:44:48Z)
• Adaptive Preference Scaling for Reinforcement Learning with Human Feedback [103.36048042664768]
  Reinforcement learning from human feedback (RLHF) is a prevalent approach to align AI systems with human values. We propose a novel adaptive preference loss, underpinned by distributionally robust optimization (DRO) Our method is versatile and can be readily adapted to various preference optimization frameworks.
  arXiv  Detail & Related papers  (2024-06-04T20:33:22Z)
• SPARE: Symmetrized Point-to-Plane Distance for Robust Non-Rigid Registration [76.40993825836222]
  We propose SPARE, a novel formulation that utilizes a symmetrized point-to-plane distance for robust non-rigid registration. The proposed method greatly improves the accuracy of non-rigid registration problems and maintains relatively high solution efficiency.
  arXiv  Detail & Related papers  (2024-05-30T15:55:04Z)
• OSPC: Online Sequential Photometric Calibration [0.0]
  Photometric calibration is essential to many computer vision applications. We propose a novel method that solves for photometric parameters using a sequential estimation approach.
  arXiv  Detail & Related papers  (2023-05-28T09:44:58Z)
• An iterative multi-fidelity approach for model order reduction of multi-dimensional input parametric PDE systems [0.0]
  We propose a sampling parametric strategy for the reduction of large-scale PDE systems with multidimensional input parametric spaces. It is achieved by exploiting low-fidelity models throughout the parametric space to sample points using an efficient sampling strategy. Since the proposed methodology leverages the use of low-fidelity models to assimilate the solution database, it significantly reduces the computational cost in the offline stage.
  arXiv  Detail & Related papers  (2023-01-23T15:25:58Z)
• Tight Cram\'{e}r-Rao type bounds for multiparameter quantum metrology through conic programming [61.98670278625053]
  It is paramount to have practical measurement strategies that can estimate incompatible parameters with best precisions possible. Here, we give a concrete way to find uncorrelated measurement strategies with optimal precisions. We show numerically that there is a strict gap between the previous efficiently computable bounds and the ultimate precision bound.
  arXiv  Detail & Related papers  (2022-09-12T13:06:48Z)
• Robust Multi-view Registration of Point Sets with Laplacian Mixture Model [25.865100974015412]
  We propose a novel probabilistic generative method to align multiple point sets based on the heavy-tailed Laplacian distribution. We demonstrate the advantages of our method by comparing it with representative state-of-the-art approaches on benchmark challenging data sets.
  arXiv  Detail & Related papers  (2021-10-26T14:49:09Z)
• A time-weighted metric for sets of trajectories to assess multi-object tracking algorithms [3.3989643515413235]
  This paper proposes a metric for sets of trajectories to evaluate multi-object tracking algorithms. The proposed metric extends the metric in [1] by including weights to the costs associated to different time steps.
  arXiv  Detail & Related papers  (2021-10-26T07:15:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.