Related papers: CI-RKM: A Class-Informed Approach to Robust Restricted Kernel Machines

Related papers

Multimodal Learning on Low-Quality Data with Conformal Predictive Self-Calibration [72.0672328514289]
Multimodal learning often grapples with the challenge of low-quality data.<n>We propose a unified framework, termed Conformal Predictive Self-Calibration.<n>Our framework consistently outperforms existing state-of-the-art methods.
arXiv Detail & Related papers (2026-05-05T14:48:52Z)
CLAIRE: Compressed Latent Autoencoder for Industrial Representation and Evaluation -- A Deep Learning Framework for Smart Manufacturing [51.56484100374058]
CLAIRE is a hybrid end-to-end learning framework that integrates unsupervised deep representation learning with supervised classification for intelligent quality control in smart manufacturing systems.<n>It employs an optimized deep autoencoder to transform raw input into a compact latent space, effectively capturing the intrinsic data structure while suppressing irrelevant or noisy features.<n>The proposed framework highlights the potential of integrating explainable AI with feature-aware regularization for robust fault detection.
arXiv Detail & Related papers (2026-03-06T15:11:58Z)
RADAR: Revealing Asymmetric Development of Abilities in MLLM Pre-training [59.493415006017635]
Pre-trained Multi-modal Large Language Models (MLLMs) provide a knowledge-rich foundation for post-training.<n>Current evaluation relies on testing after supervised fine-tuning, which introduces laborious additional training and autoregressive decoding costs.<n>We propose RADAR, an efficient ability-centric evaluation framework for Revealing Asymmetric Development of Abilities in MLLM pRe-training.
arXiv Detail & Related papers (2026-02-13T12:56:31Z)
R-Align: Enhancing Generative Reward Models through Rationale-Centric Meta-Judging [69.96389360650072]
We show that reasoning fidelity is highly predictive of downstream RLHF outcomes, beyond standard label accuracy.<n>We propose Rationale-Centric Alignment, R-Align, which augments training with gold judgments and explicitly supervises rationale alignment.
arXiv Detail & Related papers (2026-02-06T15:17:11Z)
SoliReward: Mitigating Susceptibility to Reward Hacking and Annotation Noise in Video Generation Reward Models [53.19726629537694]
Post-training alignment of video generation models with human preferences is a critical goal.<n>Current data collection paradigms, reliant on in-prompt pairwise annotations, suffer from labeling noise.<n>We propose SoliReward, a systematic framework for video RM training.
arXiv Detail & Related papers (2025-12-17T14:28:23Z)
MaP: A Unified Framework for Reliable Evaluation of Pre-training Dynamics [72.00014675808228]
Instability in Large Language Models evaluation process obscures true learning dynamics.<n>We introduce textbfMaP, a framework that integrates underlineMerging underlineand the underlinePass@k metric.<n>Experiments show that MaP yields significantly smoother performance curves, reduces inter-run variance, and ensures more consistent rankings.
arXiv Detail & Related papers (2025-10-10T11:40:27Z)
Dual-granularity Sinkhorn Distillation for Enhanced Learning from Long-tailed Noisy Data [67.25796812343454]
Real-world datasets for deep learning frequently suffer from the co-occurring challenges of class imbalance and label noise.<n>We propose Dual-granularity Sinkhorn Distillation (D-SINK), a novel framework that enhances dual robustness by distilling and integrating complementary insights.<n>Experiments on benchmark datasets demonstrate that D-SINK significantly improves robustness and achieves strong empirical performance in learning from long-tailed noisy data.
arXiv Detail & Related papers (2025-10-09T13:05:27Z)
Large-Scale Model Enabled Semantic Communication Based on Robust Knowledge Distillation [53.16213723669751]
Large-scale models (LSMs) can be an effective framework for semantic representation and understanding.<n>However, their direct deployment is often hindered by high computational complexity and resource requirements.<n>This paper proposes a novel knowledge distillation based semantic communication framework.
arXiv Detail & Related papers (2025-08-04T07:47:18Z)
RoHOI: Robustness Benchmark for Human-Object Interaction Detection [84.78366452133514]
Human-Object Interaction (HOI) detection is crucial for robot-human assistance, enabling context-aware support.<n>We introduce the first benchmark for HOI detection, evaluating model resilience under diverse challenges.<n>Our benchmark, RoHOI, includes 20 corruption types based on the HICO-DET and V-COCO datasets and a new robustness-focused metric.
arXiv Detail & Related papers (2025-07-12T01:58:04Z)
Semi-supervised Semantic Segmentation with Multi-Constraint Consistency Learning [81.02648336552421]
We propose a Multi-Constraint Consistency Learning approach to facilitate the staged enhancement of the encoder and decoder.<n>Self-adaptive feature masking and noise injection are designed in an instance-specific manner to perturb the features for robust learning of the decoder.<n> Experimental results on Pascal VOC2012 and Cityscapes datasets demonstrate that our proposed MCCL achieves new state-of-the-art performance.
arXiv Detail & Related papers (2025-03-23T03:21:33Z)
TRKM: Twin Restricted Kernel Machines for Classification and Regression [0.0]
TRKM combines the benefits of twin models with the robustness of the RKM framework to enhance classification and regression tasks.<n>We implement the TRKM model on the brain age dataset, demonstrating its efficacy in predicting brain age.
arXiv Detail & Related papers (2025-02-13T05:13:46Z)
One Class Restricted Kernel Machines [0.0]
Restricted kernel machines (RKMs) have demonstrated a significant impact in enhancing generalization ability in the field of machine learning.<n>RKMs's efficacy can be compromised by the presence of outliers and other forms of contamination within the dataset.<n>To address this critical issue and robustness of the model, we propose the novel one-class RKM (OCRKM)<n>In the framework of OCRKM, we employ an energy function akin to that of the RBM, which integrates both visible and hidden variables in a nonprobabilistic setting.
arXiv Detail & Related papers (2025-02-11T07:11:20Z)
Enhancing Robust Fairness via Confusional Spectral Regularization [6.041034366572273]
We derive a robust generalization bound for the worst-class robust error within the PAC-Bayesian framework.<n>We propose a novel regularization technique to improve worst-class robust accuracy and enhance robust fairness.
arXiv Detail & Related papers (2025-01-22T23:32:19Z)
Nonparametric Sparse Online Learning of the Koopman Operator [11.710740395697128]
The Koopman operator provides a powerful framework for representing the dynamics of general nonlinear dynamical systems.<n>Data-driven techniques to learn the Koopman operator typically assume that the chosen function space is closed under system dynamics.<n>We present an operator approximation algorithm to learn the Koopman operator iteratively with control over the complexity of the representation.
arXiv Detail & Related papers (2024-05-13T02:18:49Z)
InfoRM: Mitigating Reward Hacking in RLHF via Information-Theoretic Reward Modeling [66.3072381478251]
Reward hacking, also termed reward overoptimization, remains a critical challenge. We propose a framework for reward modeling, namely InfoRM, by introducing a variational information bottleneck objective. We show that InfoRM's overoptimization detection mechanism is not only effective but also robust across a broad range of datasets.
arXiv Detail & Related papers (2024-02-14T17:49:07Z)
The Risk of Federated Learning to Skew Fine-Tuning Features and Underperform Out-of-Distribution Robustness [50.52507648690234]
Federated learning has the risk of skewing fine-tuning features and compromising the robustness of the model. We introduce three robustness indicators and conduct experiments across diverse robust datasets. Our approach markedly enhances the robustness across diverse scenarios, encompassing various parameter-efficient fine-tuning methods.
arXiv Detail & Related papers (2024-01-25T09:18:51Z)
CCNETS: A Modular Causal Learning Framework for Pattern Recognition in Imbalanced Datasets [0.0]
Causal Cooperative Networks (CCNETS) is a modular learning framework that integrates generation, inference, and reconstruction within a unified causal paradigm.<n>We evaluate CCNETS on a real-world credit card fraud detection dataset with extreme imbalance (fraud cases 0.2%)
arXiv Detail & Related papers (2024-01-07T14:41:31Z)
Doubly Robust Instance-Reweighted Adversarial Training [107.40683655362285]
We propose a novel doubly-robust instance reweighted adversarial framework. Our importance weights are obtained by optimizing the KL-divergence regularized loss function. Our proposed approach outperforms related state-of-the-art baseline methods in terms of average robust performance.
arXiv Detail & Related papers (2023-08-01T06:16:18Z)
ARBEx: Attentive Feature Extraction with Reliability Balancing for Robust Facial Expression Learning [5.648318448953635]
ARBEx is a novel attentive feature extraction framework driven by Vision Transformer. We employ learnable anchor points in the embedding space with label distributions and multi-head self-attention mechanism to optimize performance against weak predictions. Our strategy outperforms current state-of-the-art methodologies, according to extensive experiments conducted in a variety of contexts.
arXiv Detail & Related papers (2023-05-02T15:10:01Z)
Open-Set Recognition: A Good Closed-Set Classifier is All You Need [146.6814176602689]
We show that the ability of a classifier to make the 'none-of-above' decision is highly correlated with its accuracy on the closed-set classes. We use this correlation to boost the performance of the cross-entropy OSR 'baseline' by improving its closed-set accuracy. We also construct new benchmarks which better respect the task of detecting semantic novelty.
arXiv Detail & Related papers (2021-10-12T17:58:59Z)
Learning to Learn Kernels with Variational Random Features [118.09565227041844]
We introduce kernels with random Fourier features in the meta-learning framework to leverage their strong few-shot learning ability. We formulate the optimization of MetaVRF as a variational inference problem. We show that MetaVRF delivers much better, or at least competitive, performance compared to existing meta-learning alternatives.
arXiv Detail & Related papers (2020-06-11T18:05:29Z)
Multiplicative noise and heavy tails in stochastic optimization [62.993432503309485]
empirical optimization is central to modern machine learning, but its role in its success is still unclear. We show that it commonly arises in parameters of discrete multiplicative noise due to variance. A detailed analysis is conducted in which we describe on key factors, including recent step size, and data, all exhibit similar results on state-of-the-art neural network models.
arXiv Detail & Related papers (2020-06-11T09:58:01Z)
Robust Generative Restricted Kernel Machines using Weighted Conjugate Feature Duality [11.68800227521015]
We introduce weighted conjugate feature duality in the framework of Restricted Kernel Machines (RKMs) The RKM formulation allows for an easy integration of methods from classical robust statistics. Experiments show that the weighted RKM is capable of generating clean images when contamination is present in the training data.
arXiv Detail & Related papers (2020-02-04T09:23:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.