Robust Loss Functions

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NAViLoss: An Underwater Navigation-Aware Dual-Residual Objective for Physics-Consistent Learning
Oct 7, 2026Arup Kumar Sahoo, Itzik KleinAutonomous Underwater Navigation Robust Loss Functions
The Hatter Department of Marine Technologies, Leon H. Charney School of Marine Sciences, University of Haifa, Haifa, 3498838, Israel
Robust Local Optimization Done Right
Oct 5, 2026James Pritts, Kevin KöserRelative Pose Estimation Nonconvex Optimization
Kiel University, Kiel, Germany
Robust LassoNet: Enhancing Feature Selection in Neural Networks via Robust Loss Functions
Sep 29, 2026Daniela De Canditiis, Italia De Feis, Paola StolfiRobust Loss Functions Feature Selection
Istituto per le Applicazioni del Calcolo “Mauro Picone”, Consiglio Nazionale delle Ricerche, Via dei Taurini, 19, Roma, 00185, Italia · Istituto per le Applicazioni del Calcolo “Mauro Picone”, Consiglio Nazionale delle Ricerche, Via Pietro Castellino, 111, Napoli, 80131, Italia
Beyond Sub-Gaussian Detector Scores: Robust Weighted Profile-Loss Change Point Detection for Human-LLM Text Segmentation
Sep 29, 2026Wan Tian, Zhongyi Li, Yawen Li +3Robust Loss Functions Change-Point Detection
Peking University · Beihang University · Capital Normal University +2
Tail-Weight Control and Localized Generalization in Nearly Low-Rank Adversarial Classification
Sep 20, 2026Kunyu Wang, Dehan Wang, Wenjun ChenAdversarial Robustness Robust Loss Functions
The Chinese University of Hong Kong · Columbia University
Bounded Adjustment with Reliability-Guided Embedding for Imbalanced Learning with Noisy Labels
Sep 14, 2026Mushir Akhtar, Akarsh J., M. Tanveer +1Class-Imbalanced Learning Long-Tail Learning
Department of Mathematics, Indian Institute of Technology Indore, India · Department of Electrical Engineering, Indian Institute of Technology Indore, India
Learning Sign Language Recognition under Label Noise: A Study of Noise-Robust Losses for Isolated and Continuous Settings
Sep 14, 2026Akihisa Shitara, Yoichi OchiaiNoisy-Label Learning Robust Loss Functions
R&D Center for Digital Nature, University of Tsukuba · Faculty of Library, Information and Media Science, University of Tsukuba · Pixie Dust Technologies, Inc.
Generalization Analysis of Distributed Kernel-based Robust Gradient Descent Algorithms
Sep 10, 2026Jun-Yi Meng, Zheng-Chu Guo, Yuan MaoKernel Regression Communication-Efficient Distributed Training
School of Mathematical Sciences, Zhejiang University, Hangzhou 310058, P. R. China · College of Informatics, Huazhong Agricultural University, Wuhan 430070, P. R. China
IFW-BLS: Dual-Robust Broad Learning System with Intuitionistic Fuzzy Wave Loss
Sep 2, 2026Mushir Akhtar, M. TanveerRobust Loss Functions
Indian Institute of Technology Indore, Simrol, Indore, India
Error-Type-Aware Loss Reweighting for Robust Named Entity Recognition with Noisy LLM Labels
Aug 31, 2026Elena Merdjanovska, Jonas Golde, Alan AkbikNamed Entity Recognition LLM-Assisted Annotation
Humboldt-Universität zu Berlin · Science of Intelligence
Robust Broad Learning System with Wave Loss for Classification under Data Uncertainty
Aug 30, 2026Mushir Akhtar, A. Varshney, A. Quadir +3Robust Loss Functions Learning with Noisy Labels
Department of Mathematics Indian Institute of Technology Indore
Sparse and robust geometric twin support vector machine via asymmetric RoBoSS loss function
Aug 12, 2026Kai Qi, Xinji Huang, Hongchun WangNoisy-Label Learning Robust Loss Functions
National Center for Applied Mathematics in Chongqing, Chongqing Normal University, Chongqing, 401331, China. · School of Mathematical Sciences, Chongqing Normal University, Chongqing, 401331, China.
No Unique Minimizer, No Problem: On the Consistency of Robust Neural Classifiers
Aug 9, 2026Subhabrata Majumdar, Anand Deo, Partha Pratim Saha +1Neural Network Generalization Neural Network Robustness
Indian Institute of Management Bangalore · Independent Researcher · Indian Statistical Institute, Kolkata
XGRVFL-MV: Residual-Coupled Graph-Embedded Multi-View Random Vector Functional Link Network with FleXi Guardian Loss
Jul 25, 2026Yogesh Kumar, Mudasir GanaieMulti-View Consistency Multi-View Learning
Department of Computer Science and Engineering, Indian Institute of Technology Ropar, Rupnagar, 140001, Punjab, India
Smart predict-then-robustly-optimize
Jul 23, 2026Aakil Caunhye, Xuefei Lu, Belen Martin-BarraganDistribution Shift Robustness Predict-Then-Optimize
Robust Losses from Univariate Base Functions for Noisy-Label Learning
Jul 18, 2026Peng Hu, Jianwei MaNoisy-Label Learning Robust Loss Functions
School of Mathematics, Harbin Institute of Technology, Harbin, China · Institute for Artificial Intelligence and the School of Earth and Space Sciences, Peking University, Beijing, China · Institute for Artificial Intelligence and the School of Mathematics, Harbin Institute of Technology, Harbin, China
Distributionally Robust Listwise Preference Optimization
Jul 2, 2026Xudong Wu, Jian Qian, Pangpang Liu +2LLM Alignment Distributionally Robust Optimization
The University of Hong Kong · Hong Kong SAR · Yale University +3
Robust Diffusion Models via Divergence-Induced Weighted Denoising
Jun 21, 2026Lei Li, Yuexiao DongNeural Network Robustness Robust Loss Functions
LunarAI LLC · Newark, DE 19702 · Fox School of Business +1
Alternate loss functions and regression models that achieve robustness to outliers by modulating the learning rate
Jun 20, 2026Mathew Mithra Noel, Arindam Banerjee, Yug D. Oswal +2Robust Regression Robust Loss Functions
aSchool of Electrical Engineering, Vellore Institute of Technology, Vellore, 632 014, Tamil Nadu, India · bErnst and Young GDS, Godrej Waterside, Ring Rd, DP Block, Sector V, Bidhannagar, Kolkata, 700 091, West Bengal, India · cSchool of Computer Science and Engineering, Vellore Institute of Technology, Vellore, 632 014, Tamil Nadu, India
Neural Bayesian Anomaly Mitigation: A Robust Loss that Doubles as an Unsupervised Contamination Classifier
Jun 15, 2026S. A. K. Leeney, W. J. Handley, H. T. J. Bevins +1Neural Network Robustness Noisy-Label Learning
Astrophysics Group, Cavendish Laboratory University of Cambridge · Institute of Astronomy University of Cambridge
ALCL: An Adaptive Log-Correntropy Loss for Robust Learning under Non-Gaussian Noise
Jun 14, 2026Mainak Kundu, Ria Kanjilal, Ismail UysalImage Corruption Robustness Robust Loss Functions
Department of Electrical Engineering, University of South Florida, Tampa, FL 33620 USA · Department of Computer Engineering, California Polytechnic State University, San Luis Obispo, CA 93407 USA
Distributional Loss for Robust Classification
Jun 11, 2026Kathleen Anderson, Thomas MartinetzRobust Loss Functions Classification
Institute for Neuro- and Bioinformatics, University of Lübeck, Germany
Robust and sparse support vector machine via hybrid truncated loss for supervised classification
Jun 4, 2026Yuliang Yang, Chen Chen, Yuxiang Liu +1Multi-View Learning Robust Loss Functions
School of Science, Beijing Forestry University, No.35 Qinghua East Road, 100083 Haidian, Beijing, China · Translational Cancer Research Center, Peking University First Hospital, No. 8 Xishiku Street, 100034 Xicheng, Beijing, China
The Matching Principle: A Geometric Theory of Loss Functions for Nuisance-Robust Representation Learning
May 21, 2026Vishal RajputDistribution Shift Robustness Jacobian Regularization
KU Leuven
Symmetrization of Loss Functions for Robust Training of Neural Networks in the Presence of Noisy Labels
May 19, 2026Alexandre Lemire Paquin, Brahim Chaib-Draa, Philippe GiguèreNeural Network Robustness Noisy-Label Learning
Department of Computer Science and Software Engineering Université Laval, Québec, Canada
Component-Adaptive and Lesion-Level Supervision for Improved Small Structure Segmentation in Brain MRI
Apr 9, 2026Minh Sao Khue Luu, Evgeniy N. Pavlovskiy, Bair N. TuchinovMedical Image Segmentation Robust Loss Functions
The Artificial Intelligence Research Center of Novosibirsk State University, Novosibirsk 630090, Russia
DPG loss functions for learning parameter-to-solution maps by neural networks
Jun 23, 2025Pablo Cortés Castillo, Wolfgang Dahmen, Jay GopalakrishnanPDE Solving PDE Surrogate Modeling

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NAViLoss: An Underwater Navigation-Aware Dual-Residual Objective for Physics-Consistent Learning

Arup Kumar Sahoo, Itzik Klein

The Hatter Department of Marine Technologies, Leon H. Charney School of Marine Sciences, University of Haifa, Haifa, 3498838, Israel

Autonomous Underwater Navigation Robust Loss Functions

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Autonomous underwater vehicles (AUVs) commonly rely on inertial navigation systems (INS) aided by Doppler velocity logs (DVLs) for reliable underwater navigation. Accurate DVL velocity estimation is therefore essential for successful operation. Recent learning-based methods have demonstrated improved DVL velocity estimation, particularly under degraded measurement conditions. However, their training objectives typically rely on conventional regression losses that are highly sensitive to large residuals and corrupted observations. Additionally, they do not explicitly account for the physical consistency and measurement uncertainty associated with the underlying sensing process. To address these limitations, this paper introduces navigation-aware loss (NAViLoss), a robust and uncertainty-aware objective function for learning-based AUV velocity estimation. NAViLoss jointly penalizes the velocity-estimation residual in the navigation-state domain and the beam-consistency residual in the DVL measurement domain. Its bounded formulation limits the influence of large residuals, while an adaptive mechanism regulates the uncertainty in beam geometry. Furthermore, NAViLoss is integrated with a DeepONet architecture to form a novel NAVi-DeepONet model for seamless estimation of an underwater vehicle's velocity. Lastly, our model is evaluated using approximately 10,000m of semi-synthetic AUV experimental data collected during multiple real-world sea trials. Experimental results demonstrate a 44% improvement in velocity-estimation accuracy compared with conventional and learning-based baselines. These results demonstrate the effectiveness of navigation-aware and uncertainty-adaptive loss design for robust learning-based underwater velocity estimation.

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