:: Library Catalog

Imagen de Portada

Guardado en:

Detalles Bibliográficos
Autores principales:	Vinod, Ashwin, Bajaj, Chandrajit
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning
Acceso en línea:	https://arxiv.org/abs/2504.04079
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Motion Code: Robust Time Series Classification and Forecasting via Sparse Variational Multi-Stochastic Processes Learning
por: Bajaj, Chandrajit, et al.
Publicado: (2024)

Self-Balancing, Memory Efficient, Dynamic Metric Space Data Maintenance, for Rapid Multi-Kernel Estimation
por: Ellendula, Aditya S, et al.
Publicado: (2025)

When Descent Is Too Stable: Event-Triggered Hamiltonian Learning to Optimize
por: Wang, Yi, et al.
Publicado: (2026)

Sample Efficient Learning of Factored Embeddings of Tensor Fields
por: Heo, Taemin, et al.
Publicado: (2022)

A Differential and Pointwise Control Approach to Reinforcement Learning
por: Nguyen, Minh, et al.
Publicado: (2024)

Low-cost Robust Night-time Aerial Material Segmentation through Hyperspectral Data and Sparse Spatio-Temporal Learning
por: Bajaj, Chandrajit, et al.
Publicado: (2024)

PHAST: Port-Hamiltonian Architecture for Structured Temporal Dynamics Forecasting
por: Bhardwaj, Shubham, et al.
Publicado: (2026)

Learning Material-Aware Hamiltonian Risk Fields for Safe Navigation
por: Ellendula, Aditya Sai, et al.
Publicado: (2026)

GRL-SNAM: Geometric Reinforcement Learning with Path Differential Hamiltonians for Simultaneous Navigation and Mapping in Unknown Environments
por: Ellendula, Aditya Sai, et al.
Publicado: (2025)

Queryable LoRA: Instruction-Regularized Routing Over Shared Low-Rank Update Atoms
por: Vaidya, Omatharv Bharat, et al.
Publicado: (2026)

Reinforcement Learning for Molecular Dynamics Optimization: A Stochastic Pontryagin Maximum Principle Approach
por: Bajaj, Chandrajit, et al.
Publicado: (2022)

Learning Sparse Codes with Entropy-Based ELBOs
por: Velychko, Dmytro, et al.
Publicado: (2023)

Learning Generalized Hamiltonian Dynamics with Stability from Noisy Trajectory Data
por: McLennan, Luke, et al.
Publicado: (2025)

Beyond ELBOs: A Large-Scale Evaluation of Variational Methods for Sampling
por: Blessing, Denis, et al.
Publicado: (2024)

Harnessing Data from Clustered LQR Systems: Personalized and Collaborative Policy Optimization
por: Kanakeri, Vinay, et al.
Publicado: (2025)

Generative Models with ELBOs Converging to Entropy Sums
por: Warnken, Jan, et al.
Publicado: (2024)

Physics-informed neural networks via stochastic Hamiltonian dynamics learning
por: Bajaj, Chandrajit, et al.
Publicado: (2021)

Adaptive, Robust and Scalable Bayesian Filtering for Online Learning
por: Duran-Martin, Gerardo
Publicado: (2025)

Towards Robust and Scalable Density-based Clustering via Graph Propagation
por: Zheng, Yingtao, et al.
Publicado: (2026)

CoHiRF: Hierarchical Consensus for Interpretable Clustering Beyond Scalability Limits
por: Meziani, Katia, et al.
Publicado: (2025)

Choosing the Right Regularizer for Applied ML: Simulation Benchmarks of Popular Scikit-learn Regularization Frameworks
por: Knight, Benjamin S., et al.
Publicado: (2026)

Fast, Scalable, Energy-Efficient Non-element-wise Matrix Multiplication on FPGA
por: Zhu, Xuqi, et al.
Publicado: (2024)

qPOTS: Efficient batch multiobjective Bayesian optimization via Pareto optimal Thompson sampling
por: Renganathan, Ashwin, et al.
Publicado: (2023)

Scalable Bayesian Learning with posteriors
por: Duffield, Samuel, et al.
Publicado: (2024)

A Bayesian Approach to Clustering via the Proper Bayesian Bootstrap: the Bayesian Bagged Clustering (BBC) algorithm
por: Quetti, Federico Maria, et al.
Publicado: (2024)

Individualised Treatment Effects Estimation with Composite Treatments and Composite Outcomes
por: Chauhan, Vinod Kumar, et al.
Publicado: (2025)

Bayesian Supervised Causal Clustering
por: Wang, Luwei, et al.
Publicado: (2026)

Analysis, Identification and Prediction of Parkinson Disease Sub-Types and Progression through Machine Learning
por: Ram, Ashwin
Publicado: (2023)

Towards Scalable Bayesian Optimization via Gradient-Informed Bayesian Neural Networks
por: Makrygiorgos, Georgios, et al.
Publicado: (2025)

Co-Learning Bayesian Optimization
por: Guo, Zhendong, et al.
Publicado: (2025)

Scalable Monte Carlo for Bayesian Learning
por: Fearnhead, Paul, et al.
Publicado: (2024)

Enhancing Kernel Power K-means: Scalable and Robust Clustering with Random Fourier Features and Possibilistic Method
por: Chen, Yixi, et al.
Publicado: (2025)

Robust Federated Personalised Mean Estimation for the Gaussian Mixture Model
por: Managoli, Malhar A., et al.
Publicado: (2025)

Scalable Varied-Density Clustering via Graph Propagation
por: Pham, Ninh, et al.
Publicado: (2025)

A Scalable Global Optimization Algorithm For Constrained Clustering
por: Chumpitaz-Flores, Pedro, et al.
Publicado: (2025)

Scalable and Adaptive Spectral Embedding for Attributed Graph Clustering
por: Liu, Yunhui, et al.
Publicado: (2024)

Robust and Scalable Variational Bayes
por: Padilla, Carlos Misael Madrid, et al.
Publicado: (2025)

Summaries as Centroids for Interpretable and Scalable Text Clustering
por: Diaz-Rodriguez, Jairo
Publicado: (2025)

Scalable Bayesian Inference for Nonlinear Conservation Laws
por: Weiland, Tim, et al.
Publicado: (2026)

Transformers can do Bayesian Clustering
por: Bhaskaran, Prajit, et al.
Publicado: (2025)