Continuous Optimization for Feature Selection with Permutation-Invariant Embedding and Policy-Guided Search

• URL: http://arxiv.org/abs/2505.11601v2
• Date: Sun, 28 Sep 2025 23:56:46 GMT
• Title: Continuous Optimization for Feature Selection with Permutation-Invariant Embedding and Policy-Guided Search
• Authors: Rui Liu, Rui Xie, Zijun Yao, Yanjie Fu, Dongjie Wang,
• Abstract summary: We develop an encoder-decoder paradigm to preserve feature selection knowledge into a continuous embedding space.<n>We also employ a policy-based reinforcement learning approach to guide the exploration of the embedding space.
• Score: 31.460557834760873
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Feature selection removes redundant features to enhanc performance and computational efficiency in downstream tasks. Existing works often struggle to capture complex feature interactions and adapt to diverse scenarios. Recent advances in this domain have incorporated generative intelligence to address these drawbacks by uncovering intricate relationships between features. However, two key limitations remain: 1) embedding feature subsets in a continuous space is challenging due to permutation sensitivity, as changes in feature order can introduce biases and weaken the embedding learning process; 2) gradient-based search in the embedding space assumes convexity, which is rarely guaranteed, leading to reduced search effectiveness and suboptimal subsets. To address these limitations, we propose a new framework that can: 1) preserve feature subset knowledge in a continuous embedding space while ensuring permutation invariance; 2) effectively explore the embedding space without relying on strong convex assumptions. For the first objective, we develop an encoder-decoder paradigm to preserve feature selection knowledge into a continuous embedding space. This paradigm captures feature interactions through pairwise relationships within the subset, removing the influence of feature order on the embedding. Moreover, an inducing point mechanism is introduced to accelerate pairwise relationship computations. For the second objective, we employ a policy-based reinforcement learning (RL) approach to guide the exploration of the embedding space. The RL agent effectively navigates the space by balancing multiple objectives. By prioritizing high-potential regions adaptively and eliminating the reliance on convexity assumptions, the RL agent effectively reduces the risk of converging to local optima. Extensive experiments demonstrate the effectiveness, efficiency, robustness and explicitness of our model.

Related papers

• TopoCurate:Modeling Interaction Topology for Tool-Use Agent Training [53.93696896939915]
  Training tool-use agents typically rely on Supervised Fine-Tuning (SFT) on successful trajectories and Reinforcement Learning (RL) on pass-rate-selected tasks.<n>We propose TopoCurate, an interaction-aware framework that projects multi-trial rollouts from the same task into a unified semantic quotient topology.<n>TopoCurate achieves consistent gains of 4.2% (SFT) and 6.9% (RL) over state-of-the-art baselines.
  arXiv  Detail & Related papers  (2026-03-02T10:38:54Z)
• Rethinking Multi-Condition DiTs: Eliminating Redundant Attention via Position-Alignment and Keyword-Scoping [61.459927600301654]
  Multi-condition control is bottlenecked by the conventional concatenate-and-attend'' strategy.<n>Our analysis reveals that much of this cross-modal interaction is spatially or semantically redundant.<n>We propose Position-aligned and Keyword-scoped Attention (PKA), a highly efficient framework designed to eliminate these redundancies.
  arXiv  Detail & Related papers  (2026-02-06T16:39:10Z)
• Permutation-Invariant Representation Learning for Robust and Privacy-Preserving Feature Selection [28.951637174740203]
  Existing methods often struggle to capture intricate feature interactions and adapt across diverse application scenarios.<n>We introduce a novel framework that integrates permutation-invariant embedding with policy-guided search.<n>In practice, data across local clients is highly imbalanced, heterogeneous and constrained by strict privacy regulations.
  arXiv  Detail & Related papers  (2025-10-07T02:53:32Z)
• Semantic-Inductive Attribute Selection for Zero-Shot Learning [4.083977531653519]
  We study two complementary feature-selection strategies for Zero-Shot Learning (ZSL)<n>The first adapts embedded feature selection to the demands of ZSL, turning model-driven rankings into meaningful semantic pruning.<n>The second leverages evolutionary computation to directly explore the space of attribute subsets more broadly.
  arXiv  Detail & Related papers  (2025-09-26T15:14:36Z)
• Q-function Decomposition with Intervention Semantics with Factored Action Spaces [51.01244229483353]
  We consider Q-functions defined over a lower dimensional projected subspace of the original action space, and study the condition for the unbiasedness of decomposed Q-functions.<n>This leads to a general scheme which we call action decomposed reinforcement learning that uses the projected Q-functions to approximate the Q-function in standard model-free reinforcement learning algorithms.
  arXiv  Detail & Related papers  (2025-04-30T05:26:51Z)
• Knockoff-Guided Feature Selection via A Single Pre-trained Reinforced Agent [44.84307718534031]
  We introduce an innovative framework for feature selection guided by knockoff features and optimized through reinforcement learning. A deep Q-network, pre-trained with the original features and their corresponding pseudo labels, is employed to improve the efficacy of the exploration process. A new epsilon-greedy strategy is used, incorporating insights from the pseudo labels to make the feature selection process more effective.
  arXiv  Detail & Related papers  (2024-03-06T19:58:19Z)
• SNE-RoadSegV2: Advancing Heterogeneous Feature Fusion and Fallibility Awareness for Freespace Detection [29.348921424716057]
  This paper presents a novel heterogeneous feature fusion block, comprising a holistic attention module, a heterogeneous feature contrast descriptor, and an affinity-weighted feature recalibrator. It incorporates both inter-scale and intra-scale skip connections into the decoder architecture while eliminating redundant ones, leading to both improved accuracy and computational efficiency. It introduces two fallibility-aware loss functions that separately focus on semantic-transition and depth-inconsistent regions, collectively contributing to greater supervision during model training.
  arXiv  Detail & Related papers  (2024-02-29T07:20:02Z)
• Double Duality: Variational Primal-Dual Policy Optimization for Constrained Reinforcement Learning [132.7040981721302]
  We study the Constrained Convex Decision Process (MDP), where the goal is to minimize a convex functional of the visitation measure. Design algorithms for a constrained convex MDP faces several challenges, including handling the large state space.
  arXiv  Detail & Related papers  (2024-02-16T16:35:18Z)
• Feature Interaction Aware Automated Data Representation Transformation [27.26916497306978]
  We develop a hierarchical reinforcement learning structure with cascading Markov Decision Processes to automate feature and operation selection. We reward agents based on the interaction strength between selected features, resulting in intelligent and efficient exploration of the feature space that emulates human decision-making.
  arXiv  Detail & Related papers  (2023-09-29T06:48:16Z)
• Object Segmentation by Mining Cross-Modal Semantics [68.88086621181628]
  We propose a novel approach by mining the Cross-Modal Semantics to guide the fusion and decoding of multimodal features. Specifically, we propose a novel network, termed XMSNet, consisting of (1) all-round attentive fusion (AF), (2) coarse-to-fine decoder (CFD), and (3) cross-layer self-supervision.
  arXiv  Detail & Related papers  (2023-05-17T14:30:11Z)
• Tree ensemble kernels for Bayesian optimization with known constraints over mixed-feature spaces [54.58348769621782]
  Tree ensembles can be well-suited for black-box optimization tasks such as algorithm tuning and neural architecture search. Two well-known challenges in using tree ensembles for black-box optimization are (i) effectively quantifying model uncertainty for exploration and (ii) optimizing over the piece-wise constant acquisition function. Our framework performs as well as state-of-the-art methods for unconstrained black-box optimization over continuous/discrete features and outperforms competing methods for problems combining mixed-variable feature spaces and known input constraints.
  arXiv  Detail & Related papers  (2022-07-02T16:59:37Z)
• Gleo-Det: Deep Convolution Feature-Guided Detector with Local Entropy Optimization for Salient Points [5.955667705173262]
  We propose to achieve fine constraint based on the requirement of repeatability while coarse constraint with guidance of deep convolution features. With the guidance of convolution features, we define the cost function from both positive and negative sides.
  arXiv  Detail & Related papers  (2022-04-27T12:40:21Z)
• Encouraging Disentangled and Convex Representation with Controllable Interpolation Regularization [15.725515910594725]
  We focus on controllable disentangled representation learning (C-Dis-RL) We propose a simple yet efficient method: Controllable Interpolation Regularization (CIR)
  arXiv  Detail & Related papers  (2021-12-06T16:52:07Z)
• Regressive Domain Adaptation for Unsupervised Keypoint Detection [67.2950306888855]
  Domain adaptation (DA) aims at transferring knowledge from a labeled source domain to an unlabeled target domain. We present a method of regressive domain adaptation (RegDA) for unsupervised keypoint detection. Our method brings large improvement by 8% to 11% in terms of PCK on different datasets.
  arXiv  Detail & Related papers  (2021-03-10T16:45:22Z)
• Discrete Action On-Policy Learning with Action-Value Critic [72.20609919995086]
  Reinforcement learning (RL) in discrete action space is ubiquitous in real-world applications, but its complexity grows exponentially with the action-space dimension. We construct a critic to estimate action-value functions, apply it on correlated actions, and combine these critic estimated action values to control the variance of gradient estimation. These efforts result in a new discrete action on-policy RL algorithm that empirically outperforms related on-policy algorithms relying on variance control techniques.
  arXiv  Detail & Related papers  (2020-02-10T04:23:09Z)
• Contradictory Structure Learning for Semi-supervised Domain Adaptation [67.89665267469053]
  Current adversarial adaptation methods attempt to align the cross-domain features. Two challenges remain unsolved: 1) the conditional distribution mismatch and 2) the bias of the decision boundary towards the source domain. We propose a novel framework for semi-supervised domain adaptation by unifying the learning of opposite structures.
  arXiv  Detail & Related papers  (2020-02-06T22:58:20Z)

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.