Metric Learning

ICML 2024arXiv:2312.00995

#2

Second-Order Uncertainty Quantification: A Distance-Based Approach

Yusuf Sale, Viktor Bengs, Michele Caprio et al.

uncertainty quantificationsecond-order probability distributionspredictive uncertaintyclassification setting+2

33

ICML 2024arXiv:2406.17098

#3

Learning Temporal Distances: Contrastive Successor Features Can Provide a Metric Structure for Decision-Making

Vivek Myers, Chongyi Zheng, Anca Dragan et al.

temporal distancescontrastive learningsuccessor featurestriangle inequality+4

33

AAAI 2024arXiv:2306.03364

#4

Learning Representations on the Unit Sphere: Investigating Angular Gaussian and Von Mises-Fisher Distributions for Online Continual Learning

Nicolas Michel, Giovanni Chierchia, Romain Negrel et al.

unit sphere representationsangular gaussian distributionvon mises-fisher distributiononline continual learning+2

14

ICML 2025arXiv:2407.09297

#5

Learning Distances from Data with Normalizing Flows and Score Matching

Peter Sorrenson, Daniel Behrend-Uriarte, Christoph Schnörr et al.

7

CVPR 2024arXiv:2403.16937

#6

Hyperspherical Classification with Dynamic Label-to-Prototype Assignment

Mohammad Saadabadi Saadabadi, Ali Dabouei, Sahar Rahimi Malakshan et al.

5

AAAI 2025arXiv:2506.15120

#7

Advancing Loss Functions in Recommender Systems: A Comparative Study with a Rényi Divergence-Based Solution

Shengjia Zhang, Jiawei Chen, Changdong Li et al.

loss functionsrecommender systemsrényi divergencedistributional robust optimization+3

5

point cloud learningchamfer distanceearth mover's distancepoint set objective functions+4

#8

Towards a Density Preserving Objective Function for Learning on Point Sets

Haritha Jayasinghe, Ioannis Brilakis

ECCV 2024

3

AAAI 2025arXiv:2409.04411

#9

Implicit Relative Labeling-Importance Aware Multi-Label Metric Learning

Jun-Xiang Mao, Yong Rui, Min-Ling Zhang

Approximating Metric Magnitude of Point Sets

Rayna Andreeva, James Ward, Primoz Skraba et al.

metric magnitudeconvex optimizationsubset selectionstochastic gradient descent+3

3

CVPR 2025arXiv:2405.18560

#11

Potential Field Based Deep Metric Learning

Shubhang Bhatnagar, Narendra Ahuja

deep metric learningpotential field modelingembedding interactionsattractive repulsive potentials+4

2

ICLR 2024arXiv:2306.15368

#12

Mean Field Theory in Deep Metric Learning

Takuya Furusawa

2

NEURIPS 2025arXiv:2509.08104

#13

APML: Adaptive Probabilistic Matching Loss for Robust 3D Point Cloud Reconstruction

Sasan Sharifipour, Constantino Álvarez Casado, Mohammad Sabokrou et al.

point cloud reconstructionprobabilistic matching lossshape completion3d point cloud generation+4

1

ICLR 2025arXiv:2410.03052

#14

Learning Structured Representations by Embedding Class Hierarchy with Fast Optimal Transport

Siqi Zeng, Sixian Du, Makoto Yamada et al.

1

NEURIPS 2025arXiv:2510.20644

#15

Meta-Learning Dynamic Center Distance: Hard Sample Mining for Learning with Noisy Labels

Chenyu Mu, Yijun Qu, Jiexi Yan et al.

Connecting Jensen–Shannon and Kullback–Leibler Divergences: A New Bound for Representation Learning

Reuben Dorent, Polina Golland, William (Sandy) Wells

mutual information estimationrepresentation learningjensen-shannon divergencekullback-leibler divergence+3

multivariate linear regressionmissing data imputationl2 loss functionl1 loss function+4

#17

Regression Learning with Limited Observations of Multivariate Outcomes and Features

Yifan Sun, Grace Yi

ICML 2024

CVPR 2025arXiv:2505.11216

#18

Stabilizing Sample Similarity in Representation via Mitigating Random Consistency

Jieting Wang, ZhangZelong Zhang, Feijiang Li et al.

GeoMM: On Geodesic Perspective for Multi-modal Learning

Shibin Mei, Hang Wang, Bingbing Ni

geodesic distancemultimodal learningnonlinear manifoldsgraph structure construction+3

ICML 2025arXiv:2505.24642

#20

WILTing Trees: Interpreting the Distance Between MPNN Embeddings

Masahiro Negishi, Thomas Gärtner, Pascal Welke

AAAI 2025arXiv:2503.10993

#21

Volume-Aware Distance for Robust Similarity Learning

Shuo Chen, Chen Gong, Jun Li et al.

Riemannian Geometric-based Meta Learning

JuneYoung Park, YuMi Lee, Tae-Joon Kim et al.

#23

Not All Classes Stand on Same Embeddings: Calibrating a Semantic Distance with Metric Tensor

Jae Hyeon Park, Gyoomin Lee, Seunggi Park et al.

T-MDML: Triplet-based Multiple Distance Metric Learning for Multi-Instance Multi-Label Classification with Label Correlation

Dongyeon Kim, Yejin Kan, Gangman Yi

Deep Disentangled Metric Learning

Jinhee Park, Jisoo Park, Dagyeong Na et al.

Graph Minimum Factor Distance and Its Application to Large-Scale Graph Data Clustering

Jicong Fan

ICML 2025