:: Library Catalog

Saved in:

Bibliographic Details
Main Authors:	Bogoclu, Can, Vosshall, Robert, Cremanns, Kevin, Roos, Dirk
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2403.15908
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Quantifying Local Model Validity using Active Learning
by: Lämmle, Sven, et al.
Published: (2024)

Intelligent Optimization and Machine Learning Algorithms for Structural Anomaly Detection using Seismic Signals
by: Trapp, Maximilian, et al.
Published: (2024)

Novel Pivoted Cholesky Decompositions for Efficient Gaussian Process Inference
by: de Roos, Filip, et al.
Published: (2025)

Sample-Efficient Policy Constraint Offline Deep Reinforcement Learning based on Sample Filtering
by: Chen, Yuanhao, et al.
Published: (2025)

Sample Path Regularity of Gaussian Processes from the Covariance Kernel
by: Da Costa, Nathaël, et al.
Published: (2023)

Incorporating Navigation Context into Inland Vessel Trajectory Prediction: A Gaussian Mixture Model and Transformer Approach
by: Donandt, Kathrin, et al.
Published: (2024)

LoRANN: Low-Rank Matrix Factorization for Approximate Nearest Neighbor Search
by: Jääsaari, Elias, et al.
Published: (2024)

On the Trajectory Regularity of ODE-based Diffusion Sampling
by: Chen, Defang, et al.
Published: (2024)

MAGDiff: Covariate Data Set Shift Detection via Activation Graphs of Deep Neural Networks
by: Arnal, Charles, et al.
Published: (2023)

Decomposing Gaussians with Unknown Covariance
by: Dharamshi, Ameer, et al.
Published: (2024)

Efficient Covariance Estimation for Sparsified Functional Data
by: Zheng, Sijie, et al.
Published: (2025)

Deep Gaussian Process Proximal Policy Optimization
by: van der Lende, Matthijs, et al.
Published: (2025)

Sample Complexity of Preference-Based Nonparametric Off-Policy Evaluation with Deep Networks
by: Li, Zihao, et al.
Published: (2023)

GIPO: Gaussian Importance Sampling Policy Optimization
by: Lu, Chengxuan, et al.
Published: (2026)

Fast, Sample-Efficient, Affine-Invariant Private Mean and Covariance Estimation for Subgaussian Distributions
by: Brown, Gavin, et al.
Published: (2023)

Optimal Posterior Sampling for Policy Identification in Tabular Markov Decision Processes
by: Kone, Cyrille, et al.
Published: (2026)

Learning Joint and Individual Structure in Network Data with Covariates
by: James, Carson, et al.
Published: (2024)

Optimal Policy Adaptation under Covariate Shift
by: Liu, Xueqing, et al.
Published: (2025)

Towards Explainable Deep Learning for Ship Trajectory Prediction in Inland Waterways
by: Legel, Tom, et al.
Published: (2026)

Nearest Neighbor Sampling for Covariate Shift Adaptation
by: Portier, François, et al.
Published: (2023)

Spatial Covariance Constraints for Gaussian Mixture Models
by: Lu, Hanzhang, et al.
Published: (2026)

Gaussian and Non-Gaussian Universality of Data Augmentation
by: Huang, Kevin Han, et al.
Published: (2022)

Is Temperature Sample Efficient for Softmax Gaussian Mixture of Experts?
by: Nguyen, Huy, et al.
Published: (2024)

TBDFiltering: Sample-Efficient Tree-Based Data Filtering
by: Busa-Fekete, Robert Istvan, et al.
Published: (2026)

IMLE Policy: Fast and Sample Efficient Visuomotor Policy Learning via Implicit Maximum Likelihood Estimation
by: Rana, Krishan, et al.
Published: (2025)

Approximate Next Policy Sampling: Replacing Conservative Target Policy Updates in Deep RL
by: Sandhu, Dillon, et al.
Published: (2026)

Covariate-Adjusted Deep Causal Learning for Heterogeneous Panel Data Models
by: Zhou, Guanhao, et al.
Published: (2025)

Efficient Graph Knowledge Distillation from GNNs to Kolmogorov--Arnold Networks via Self-Attention Dynamic Sampling
by: Cui, Can, et al.
Published: (2025)

Deep Survival Analysis for Competing Risk Modeling with Functional Covariates and Missing Data Imputation
by: Gao, Penglei, et al.
Published: (2025)

Spatiotemporal Covariance Neural Networks
by: Cavallo, Andrea, et al.
Published: (2024)

Sparse Covariance Neural Networks
by: Cavallo, Andrea, et al.
Published: (2024)

Covariance Density Neural Networks
by: Roy, Om, et al.
Published: (2025)

Schur's Positive-Definite Network: Deep Learning in the SPD cone with structure
by: Pouliquen, Can, et al.
Published: (2024)

Posterior Covariance Structures in Gaussian Processes
by: Cai, Difeng, et al.
Published: (2024)

Private Adaptive Covariance Estimation via Gaussian Graphical Models
by: Ferrando, Cecilia, et al.
Published: (2026)

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks
by: Mayor, Walter, et al.
Published: (2025)

Guarantees for Nonlinear Representation Learning: Non-identical Covariates, Dependent Data, Fewer Samples
by: Zhang, Thomas T., et al.
Published: (2024)

Regional Ocean Forecasting with Hierarchical Graph Neural Networks
by: Holmberg, Daniel, et al.
Published: (2024)

Multi-Agent Reinforcement Learning for Sample-Efficient Deep Neural Network Mapping
by: Krishnan, Srivatsan, et al.
Published: (2025)

Distributionally Robust Safe Sample Elimination under Covariate Shift
by: Hanada, Hiroyuki, et al.
Published: (2024)