:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Shen, Zhaiming, Wang, Menglun, Cheng, Guang, Lai, Ming-Jun, Mu, Lin, Huang, Ruihao, Liu, Qi, Zhu, Hao
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2405.03060
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Graph-based Semi-supervised Local Clustering with Few Labeled Nodes
by: Shen, Zhaiming, et al.
Published: (2022)

Maximal Volume Matrix Cross Approximation for Image Compression and Least Squares Solution
by: Allen, Kenneth, et al.
Published: (2023)

Local Clustering for Lung Cancer Image Classification via Sparse Solution Technique
by: Hamel, Jackson, et al.
Published: (2024)

Understanding In-Context Learning for Nonlinear Regression with Transformers: Attention as Featurizer
by: Hsu, Alexander, et al.
Published: (2026)

Understanding In-Context Learning on Structured Manifolds: Bridging Attention to Kernel Methods
by: Shen, Zhaiming, et al.
Published: (2025)

Advancing Local Clustering on Graphs via Compressive Sensing: Semi-supervised and Unsupervised Methods
by: Shen, Zhaiming, et al.
Published: (2025)

Transformers for Learning on Noisy and Task-Level Manifolds: Approximation and Generalization Insights
by: Shen, Zhaiming, et al.
Published: (2025)

Isolation-based Spherical Ensemble Representations for Anomaly Detection
by: Cao, Yang, et al.
Published: (2025)

The Kolmogorov Superposition Theorem can Break the Curse of Dimensionality When Approximating High Dimensional Functions
by: Lai, Ming-Jun, et al.
Published: (2021)

The Optimal Linear B-splines Approximation via Kolmogorov Superposition Theorem and its Application
by: Lai, Ming-Jun, et al.
Published: (2024)

Disentangled Graph Prompting for Out-Of-Distribution Detection
by: Yang, Cheng, et al.
Published: (2026)

Bounded and Uniform Energy-based Out-of-distribution Detection for Graphs
by: Yang, Shenzhi, et al.
Published: (2025)

Out-of-distribution Generalization for Total Variation based Invariant Risk Minimization
by: Wang, Yuanchao, et al.
Published: (2025)

Reward Learning From Preference With Ties
by: Liu, Jinsong, et al.
Published: (2024)

Mining In-distribution Attributes in Outliers for Out-of-distribution Detection
by: Lei, Yutian, et al.
Published: (2024)

Enhanced Credit Score Prediction Using Ensemble Deep Learning Model
by: Xing, Qianwen, et al.
Published: (2024)

Revisiting Deep Ensemble for Out-of-Distribution Detection: A Loss Landscape Perspective
by: Fang, Kun, et al.
Published: (2023)

FOOGD: Federated Collaboration for Both Out-of-distribution Generalization and Detection
by: Liao, Xinting, et al.
Published: (2024)

Out-of-Distribution Detection on Graphs: A Survey
by: Cai, Tingyi, et al.
Published: (2025)

Human Texts Are Outliers: Detecting LLM-generated Texts via Out-of-distribution Detection
by: Zeng, Cong, et al.
Published: (2025)

Conformalized Quantum DeepONet Ensembles for Scalable Operator Learning with Distribution-Free Uncertainty
by: Matlia, Purav, et al.
Published: (2026)

On the Learnability of Out-of-distribution Detection
by: Fang, Zhen, et al.
Published: (2024)

Negative as Positive: Enhancing Out-of-distribution Generalization for Graph Contrastive Learning
by: Wang, Zixu, et al.
Published: (2024)

GAIA: Delving into Gradient-based Attribution Abnormality for Out-of-distribution Detection
by: Chen, Jinggang, et al.
Published: (2023)

Class Relevance Learning For Out-of-distribution Detection
by: Xiong, Butian, et al.
Published: (2023)

Learning Multi-Manifold Embedding for Out-Of-Distribution Detection
by: Li, Jeng-Lin, et al.
Published: (2024)

Sharpness-Aware Geometric Defense for Robust Out-Of-Distribution Detection
by: Li, Jeng-Lin, et al.
Published: (2025)

Beyond Penalization: Diffusion-based Out-of-Distribution Detection and Selective Regularization in Offline Reinforcement Learning
by: Wang, Qingjun, et al.
Published: (2026)

DOS: Diverse Outlier Sampling for Out-of-Distribution Detection
by: Jiang, Wenyu, et al.
Published: (2023)

Neuron Activation Coverage: Rethinking Out-of-distribution Detection and Generalization
by: Liu, Yibing, et al.
Published: (2023)

Spurious Feature Diversification Improves Out-of-distribution Generalization
by: Lin, Yong, et al.
Published: (2023)

TrustLoRA: Low-Rank Adaptation for Failure Detection under Out-of-distribution Data
by: Zhu, Fei, et al.
Published: (2025)

Targeted Vaccine: Safety Alignment for Large Language Models against Harmful Fine-Tuning via Layer-wise Perturbation
by: Liu, Guozhi, et al.
Published: (2024)

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement
by: Chen, Hao, et al.
Published: (2025)

Transferable Reward Learning by Dynamics-Agnostic Discriminator Ensemble
by: Luo, Fan-Ming, et al.
Published: (2022)

Contextual Online Pricing with (Biased) Offline Data
by: Zhang, Yixuan, et al.
Published: (2025)

Efficient and Interpretable Bandit Algorithms
by: Mukherjee, Subhojyoti, et al.
Published: (2023)

Satisficing Regret Minimization in Bandits: Constant Rate and Light-Tailed Distribution
by: Feng, Qing, et al.
Published: (2024)

Risk-Aware Linear Bandits: Theory and Applications in Smart Order Routing
by: Ji, Jingwei, et al.
Published: (2022)

On the Peril of (Even a Little) Nonstationarity in Satisficing Regret Minimization
by: Zhang, Yixuan, et al.
Published: (2026)