arXiv Machine Learning

Fair Multi-View Determinantal Coresets via Adaptive NEPv

The paper introduces a method for selecting a small, diverse subset from a large pool by addressing multiple, potentially conflicting notions of diversity. It formulates a fair multi‑view determinant selection problem that maximizes the weakest per‑view log determinant of a size‑k subset, smooths and relaxes the objective to the Stiefel manifold, and derives an adaptive self‑consistent‑field solver with damping and level shifting. The solver operates using only feature‑map products for each view and includes a rounding step via leverage‑score screening followed by fair local refinement.

arXiv Machine Learning

Jun 19

Spectral DPPs via NEPv: A Scalable Continuous Relaxation of Determinantal MAP for Diversity-Aware Data Selection

arXiv:2606. 19411v1 Announce Type: new Abstract: Selecting a small, diverse, high-quality subset from a massive pool of candidates is a recurring primitive in modern machine learning -- data curation and coreset selection for training and fine-tuning large models, active-learning batch acquisition, prompt and exemplar selection for in-context learning, retrieval diversification, and experimental design.

By Richard Yi Da Xu

llms rag fine-tuning benchmarks

Hugging Face Trending Papers

Jul 9

Mixture of Enhanced-View Experts for Multi-Query Vehicle ReID and A Large-Scale Benchmark

Multi-query vehicle ReID aims to leverage complementary information from diverse views for robust feature learning. However, current methods suffer from simplistic feature fusion and thus easily ignores some important view information and cross-view relationships.

benchmarks safety

arXiv Machine Learning

2d ago

Phase-Consistent Magnetic Spectral Learning for Multi-View Clustering

arXiv:2602.18728v2 Announce Type: replace Abstract: Unsupervised multi-view clustering (MVC) aims to partition data into meaningful groups by leveraging complementary information from multiple views...

By Mingdong Lu, Zhikui Chen, Meng Liu, Shubin Ma, Zhengyang Tang

benchmarks safety

arXiv Machine Learning

Aug 6

Multimodal Alignment Through Joint Kernel Entropic Gromov--Wasserstein Optimal Transport

arXiv:2608. 04234v1 Announce Type: cross Abstract: We study the problem of aligning data from multiple modalities into a shared representation space, focusing on settings where strong pretrained unimodal encoders are available but cross-modal paired data are scarce.

By Yixuan Florence Wu, Yilun Zhu, Naichen Shi

rag diffusion multimodal safety

arXiv Machine Learning

Aug 17

High-dimensional nonparametric changepoint detection via low-rank degree-two density projection

arXiv:2608. 13922v1 Announce Type: new Abstract: Detecting distributional changes in high dimension is difficult when neither the pre-change nor post-change density is parametrically specified.

By Guoqing Zhang, Zhaixin Chen

efficiency benchmarks

arXiv Machine Learning

Jun 17

Variational autoencoders with latent high-dimensional steady geometric flows for dynamics

arXiv:2410. 10137v5 Announce Type: replace Abstract: We develop Riemannian approaches to variational autoencoders (VAEs) for PDE-type ambient data with regularizing geometric latent dynamics, which we refer to as VAE-DLM, or VAEs with dynamical latent manifolds.

By Andrew Gracyk

diffusion

arXiv Computer Vision

1d ago

GeoMAD: Geometry-Aware Multi-View Anomaly Detection via Deformable Fusion and Distributional Alignment

arXiv:2608.26724v1 Announce Type: new Abstract: Multi-view anomaly detection (MvAD) detects defects by exploiting complementary observations from multiple camera viewpoints. The central challenge is...

By Shang-Fu Chen, Jhih-Ciang Wu, Kuan-Chuan Peng, Wen-Huang Cheng, Kai-Lung Hua

safety

arXiv Machine Learning

Jun 9

Entropic Optimal Transport Eigenmaps for Nonlinear Alignment and Joint Embedding of High-Dimensional Datasets

arXiv:2407. 01718v2 Announce Type: replace-cross Abstract: Embedding high-dimensional data into a low-dimensional space is an indispensable component of data analysis.

By Boris Landa, Yuval Kluger, Rong Ma

rag diffusion safety

arXiv Machine Learning

Jul 24

Regularized Optimization on Grassmann Manifold: Theory, Algorithm and Applications

arXiv:2607. 21039v1 Announce Type: new Abstract: Spectral methods are among the most widely used techniques for community detection, clustering, and graph learning.

By Zhuan Liang, Zheng Zhai

arXiv Statistics ML

4d ago

Spectral partitioning for $k$-block averaging kernels of finite Markov chains

arXiv:2608.21466v1 Announce Type: new Abstract: We develop spectral algorithms for selecting state-space partitions that define averaging kernels for finite, ergodic and reversible Markov chains. For...

By Michael C. H. Choi, Youjia Wang

rag

arXiv Machine Learning

Aug 18

Spectral Certificates and Projection-DPP Rounding for Determinantal MAP Selection

arXiv:2606. 19411v3 Announce Type: replace Abstract: Selecting a fixed-size subset that maximizes the determinant of a positive semidefinite kernel is the MAP problem for a size-constrained determinantal point process and the classical maximum-entropy sampling problem.

By Richard Yi Da Xu

arXiv Machine Learning

Aug 11

Multi-kernel spectral clustering: Entrywise eigenvector perturbation bounds and exact recovery

arXiv:2608. 08704v1 Announce Type: cross Abstract: Kernel spectral clustering with a single bandwidth can be inadequate for data exhibiting multiple characteristic pairwise-distance scales, a problem particularly prevalent in the high-dimensional regime.

By Zeqin Lin, Guangming Pan, Zhixiang Zhang, Yinbing Zhou

rag

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