mailitics

Tag: representation

  • Contrastive Network Representation Learning

    Contrastive Network Representation Learning arXiv:2509.11316v1 Announce Type: new Abstract: Network representation learning seeks to embed networks into a low-dimensional space while preserving the structural and semantic properties, thereby facilitating downstream tasks such as classification, trait prediction, edge identification, and community detection. Motivated by challenges in brain connectivity data analysis that is characterized by subject-specific, high-dimensional,…

  • Proof of a perfect platonic representation hypothesis

    Proof of a perfect platonic representation hypothesis arXiv:2507.01098v1 Announce Type: cross Abstract: In this note, we elaborate on and explain in detail the proof given by Ziyin et al. (2025) of the “perfect” Platonic Representation Hypothesis (PRH) for the embedded deep linear network model (EDLN). We show that if trained with SGD, two EDLNs with…

  • Fair Representation Learning for Continuous Sensitive Attributes using Expectation of Integral Probability Metrics

    Fair Representation Learning for Continuous Sensitive Attributes using Expectation of Integral Probability Metrics arXiv:2505.06435v1 Announce Type: new Abstract: AI fairness, also known as algorithmic fairness, aims to ensure that algorithms operate without bias or discrimination towards any individual or group. Among various AI algorithms, the Fair Representation Learning (FRL) approach has gained significant interest in…

  • Feature Representation Transferring to Lightweight Models via Perception Coherence

    Feature Representation Transferring to Lightweight Models via Perception Coherence arXiv:2505.06595v1 Announce Type: new Abstract: In this paper, we propose a method for transferring feature representation to lightweight student models from larger teacher models. We mathematically define a new notion called textit{perception coherence}. Based on this notion, we propose a loss function, which takes into account…

  • GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds

    GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds arXiv:2505.02972v1 Announce Type: new Abstract: Multi-Task Learning (MTL) seeks to boost statistical power and learning efficiency by discovering structure shared across related tasks. State-of-the-art MTL representation methods, however, usually treat the latent representation matrix as a point in ordinary Euclidean space, ignoring its often non-Euclidean geometry, thus…

  • Fair Sufficient Representation Learning

    Fair Sufficient Representation Learning arXiv:2504.01030v1 Announce Type: new Abstract: The main objective of fair statistical modeling and machine learning is to minimize or eliminate biases that may arise from the data or the model itself, ensuring that predictions and decisions are not unjustly influenced by sensitive attributes such as race, gender, age, or other protected…

  • Disentangled Representation Learning for Causal Inference with Instruments

    Disentangled Representation Learning for Causal Inference with Instruments arXiv:2412.04641v1 Announce Type: cross Abstract: Latent confounders are a fundamental challenge for inferring causal effects from observational data. The instrumental variable (IV) approach is a practical way to address this challenge. Existing IV based estimators need a known IV or other strong assumptions, such as the existence…