Invariant Representation Learning

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21 papers in the last four weeks, up 110% on the four weeks before. 0.2% of all new papers.

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Aligning Molecular Graph Explanations with Chemical Identity via InChIfied Invariants
May 23, 2026Emanuele Guidotti, Sara PuglioliMolecular Property Prediction Invariant Representation Learning
University of Lugano, Switzerland · Philochem AG, Switzerland
SPACE: Unifying Symmetric and Asymmetric Routing Problems for Generalist Neural Solver
May 23, 2026Rongsheng Chen, Changliang Zhou, Canhong Yu +4Vehicle Routing Invariant Representation Learning
School of Automation and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen, China · Pengcheng Laboratory, Shenzhen, China · Guangdong Provincial Key Laboratory of Fully Actuated System Control Theory and Technology, Southern University of Science and Technology, Shenzhen, China +1
The Matching Principle: A Geometric Theory of Loss Functions for Nuisance-Robust Representation Learning
May 21, 2026Vishal RajputDistribution Shift Robustness Jacobian Regularization
KU Leuven
AnyMo: Geometry-Aware Setup-Agnostic Modeling of Human Motion in the Wild
May 21, 2026Baiyu Chen, Zechen Li, Wilson Wongso +5Human Activity Recognition Wearable Sensing
The University of New South Wales · The Hong Kong University of Science and Technology (Guangzhou) · The Hong Kong University of Science and Technology
Improving Viewpoint-Invariance and Temporal Consistency for Action Detection
May 21, 2026Yannick Porto, Renato Martins, Thomas Chalumeau +1Video Action Recognition Invariant Representation Learning
⋆Universit Bourgogne Europe, CNRS, ICB · † TEB Group, Prynel SAS
BioFormer: Rethinking Cross-Subject Generalization via Spectral Structural Alignment in Biomedical Time-Series
May 21, 2026Guikang Du, Haoran Li, Xinyu Liu +3Frequency-Domain Feature Learning Invariant Representation Learning
College of Computer Science, Nankai University, Tianjin, China · College of Cyber Science, Tianjin Key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation, Key Lab of Data and Intelligent System Security, Frontiers Science Center for New Organic Matter, Nankai University, Tianjin, China
GenHAR: Generalizing Cross-domain Human Activity Recognition for Last-mile Delivery
May 21, 2026Zhiqing Hong, Zelong Li, Xiubin Fan +5Human Activity Recognition Domain Generalization
HKUST (GZ) Guangzhou, China · JD Logistics Beijing, China · Beijing Normal–Hong Kong Baptist University Zhuhai, China +2
A Mechanistic Study of Tabular Foundation Models
May 20, 2026Marin Biloš, James T. Wilson, Anderson Schneider +1Tabular Foundation Models Mechanistic Interpretability
Machine Learning Research, Morgan Stanley
Protein Fold Classification at Scale: Benchmarking and Pretraining
May 18, 2026Dexiong Chen, Andrei Manolache, Mathias Niepert +1Masked Autoencoders Self-Supervised Pre-Training
Max Planck Institute of Biochemistry, Martinsried, Germany · Computer Science Department, University of Stuttgart, Germany · Bitdefender, Romania
DARE-EEG: A Foundation Model for Mining Dual-Aligned Representation of EEG
May 18, 2026Yang Shao, Peiliang Gong, Qun Dai +1Masked Autoencoders Self-Supervised Pre-Training
College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics Nanjing, Jiangsu, China
Multi-task learning on partially labeled datasets via invariant/equivariant semi-supervised learning
May 17, 2026Miquel Martí i Rabadán, Alessandro Pieropan, Hossein Azizpour +1Multi-Task Learning Object Detection
Univrses AB, Stockholm, Sweden · KTH Royal Institute of Technology, Stockholm, Sweden
The Geometry of Projection Heads: Conditioning, Invariance, and Collapse
May 16, 2026Faris ChaudhryContrastive Learning Neural Representation Geometry
Department of Computing, Imperial College London, United Kingdom.
SHED: Style-Homogenized Embedding Alignment for Domain Generalization
May 16, 2026Kai Gan, Tong WeiVLM Robustness VLM Adaptation
School of Computer Science and Engineering, Southeast University, Nanjing 210096, China · Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, China
GraViti: Graph-Level Variational Autoencoders with Relaxed Permutation Invariance
May 15, 2026Roman Bresson, Konstantinos Divriotis, Johannes F. Lutzeyer +2Variational Autoencoders Molecular Graph Generation
Mohamed bin Zayed University of Artificial Intelligence · LIX, CNRS, École Polytechnique, IP Paris
Constrained latent state modeling: A unifying perspective on representation learning under competing constraints
May 15, 2026Gwenolé QuellecRepresentation Learning Predictive Representation Learning
LaTIM UMR 1101, Inserm, Brest, 29200, France.
Invaria: Learning Scale and Density Invariance in Point Clouds via Next-Resolution Prediction
May 15, 2026Chun-Peng Chang, Shaoxiang Wang, Alain Pagani +2Point Cloud Learning 3D Semantic Segmentation
TU Delft · DFKI
Continual Learning of Domain-Invariant Representations
May 15, 2026Pascal Janetzky, Tobias Schlagenhauf, Stefan FeuerriegelOOD Generalization Cross-Domain Generalization
LMU Munich, Munich, Germany · Munich Center for Machine Learning, Munich, Germany · Bosch Center for Artificial Intelligence, Renningen, Germany +1
SCAR: Self-Supervised Continuous Action Representation Learning
May 13, 2026Hongjia Liu, Fan Feng, Minghao Fu +3Latent Action Learning Representation Learning
University of California, San Diego · KTH Royal Institute of Technology
Twincher: Bijective Representation Learning for Robust Inversion of Continuous Systems
May 13, 2026Arkady GonoskovRepresentation Learning Invariant Representation Learning
Department of Physics, University of Gothenburg, SE-41296 Gothenburg, Sweden
DeconDTN-Toolkit: A Library for Evaluation and Enhancement of Robustness to Provenance Shift
May 11, 2026Yongsen Tan, Zhecheng Sheng, Xiruo Ding +2Distribution Shift Robustness OOD Generalization
University of Washington · University of Minnesota
Birds of a Feather Flock Together: Background-Invariant Representations via Linear Structure in VLMs
May 11, 2026Youssef Zaazou, Mark ThomasVLM Robustness Vision-Language Models
Independent Researcher
DRIFT: Drift-Resilient Invariant-Feature Transformer for DGA Detection
May 11, 2026Chaeyoung Lee, Chaeri Jung, Seonghoon JeongTransformer Concept Drift
Division of Artificial Intelligence Engineering Sookmyung Women’s University Seoul, Republic of Korea
S2P-Net: A Spectral-Spatial Polar Network for Rotation-Invariant Object Recognition in Low-Data Regimes
May 10, 2026Albert HeruthConvolutional Neural Networks Invariant Representation Learning
Unaffiliated Researcher · Heide, Schleswig-Holstein, Germany · Schleswig-Holstein
When and How to Canonize: A Generalization Perspective
May 10, 2026Yonatan Sverdlov, Benjamin Friedman, Snir Hordan +1Invariant Representation Learning Generalization Bounds
Technion – Israel Institute of Technology
CalibFree: Self-Supervised View Feature Separation for Calibration-Free Multi-Camera Multi-Object Tracking
May 10, 2026Ruiqi Xian, Deep Patel, Iain Melvin +3Multi-View Learning Invariant Representation Learning
University of Maryland, College Park, MD, USA · NEC Laboratories America, Princeton, NJ, USA · University of North Carolina, Greenboro, NC, USA
Objective-Specific Privileged Bases via Full-Prefix Matryoshka Learning
May 9, 2026Arghamitra Talukder, Philippe Chlenski, Itsik Pe'erRepresentation Learning Representation Identifiability
Computer Science, Columbia University
Embedding Dimension Lower Bounds for Universality of Deep Sets and Janossy Pooling
May 8, 2026Ali Syed, Aditya Nambiar, Jonathan W. SiegelRepresentation Learning Deep Sets
Department of Mathematics Texas A&M University College Station, TX 77843
bispectrum: Selective $G$ -Bispectra Made Practical
May 8, 2026Johan Mathe, Adele Myers, Simon Mataigne +1Invariant Representation Learning
Atmo, Inc. San Francisco, CA · UC Santa Barbara Santa Barbara, CA · UCLouvain Louvain-la-Neuve, Belgium
When Symbol Names Should Not Matter: A Logistic Theory of Fresh-Symbol Classification
May 8, 2026Wenjie Guan, Jelena BradicTransformer Classification
Department of Statistics and Data Science, Cornell University
Operator-Guided Invariance Learning for Continuous Reinforcement Learning
May 7, 2026Zuyuan Zhang, Fei Xu Yu, Tian LanReinforcement Learning Continuous-Time RL
The George Washington University

May 23, 2026, cs.LGJ/K move · Enter open · S save

Aligning Molecular Graph Explanations with Chemical Identity via InChIfied Invariants

Emanuele Guidotti, Sara Puglioli

University of Lugano, Switzerland · Philochem AG, Switzerland

Molecular Property Prediction Invariant Representation Learning GNN Explainability Molecular Representation Learning

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Obtaining consistent explanations for machine learning on molecular graphs requires predictions and attributions to be aligned with chemical identity. However, chemically equivalent drawings of the same molecule can induce different molecular representations, leading to inconsistent predictions and explanations. Here, we introduce InChIfied Invariants, a class of node, edge, and graph features based on the International Chemical Identifier (InChI) and designed to be invariant under transformations that preserve chemical identity. Using one million molecular graphs from PubChem Substances, we show that InChIfied Invariants produce identical representations for chemically equivalent graphs in 99.62% of cases, whereas standard Daylight invariants do so in only 0.35% of cases. Across MoleculeNet tasks, InChIfied Invariants preserve predictive performance while significantly improving prediction consistency across alternative graph depictions of the same molecules. We further perform a quantitative attribution analysis and show that explanations produced with standard molecular featurization methods vary substantially across chemically equivalent graphs, while InChIfied Invariants enforce consistent attributions by construction. We release open-source software implementing InChIfied Invariants, which can be used as a drop-in replacement for standard molecular graph features.

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