Imagen de Portada

Near-optimal algorithms for private estimation and sequential testing of collision probability

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Busa-Fekete, Robert, Syed, Umar
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning Artificial Intelligence
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Data value estimation on private gradients
por: Zhou, Zijian, et al.
Publicado: (2024)

Uncovering mesa-optimization algorithms in Transformers
por: von Oswald, Johannes, et al.
Publicado: (2023)

Fairness under uncertainty in sequential decisions
por: Lee, Michelle Seng Ah, et al.
Publicado: (2026)

Explanatory machine learning for sequential human teaching
por: Ai, Lun, et al.
Publicado: (2022)

Concealed Adversarial attacks on neural networks for sequential data
por: Sokerin, Petr, et al.
Publicado: (2025)

Longitudinal Ensemble Integration for sequential classification with multimodal data
por: Susman, Aviad, et al.
Publicado: (2024)

Post-detection inference for sequential changepoint localization
por: Saha, Aytijhya, et al.
Publicado: (2025)

Escaping Collapse: The Strength of Weak Data for Large Language Model Training
por: Amin, Kareem, et al.
Publicado: (2025)

Nearly Optimal Sample Complexity for Learning with Label Proportions
por: Busa-Fekete, Robert, et al.
Publicado: (2025)

Prior-informed optimization of treatment recommendation via bandit algorithms trained on large language model-processed historical records
por: Nessari, Saman, et al.
Publicado: (2025)

DeepLogit: A sequentially constrained explainable deep learning modeling approach for transport policy analysis
por: Oon, Jeremy, et al.
Publicado: (2025)

QS4D: Quantization-aware training for efficient hardware deployment of structured state-space sequential models
por: Siegel, Sebastian, et al.
Publicado: (2025)

Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models
por: Ma, Olivia, et al.
Publicado: (2024)

An algorithmic framework for the optimization of deep neural networks architectures and hyperparameters
por: Keisler, Julie, et al.
Publicado: (2023)

Failure to Mix: Large language models struggle to answer according to desired probability distributions
por: Yang, Ivy Yuqian, et al.
Publicado: (2025)

A tale of two goals: leveraging sequentiality in multi-goal scenarios
por: Serris, Olivier, et al.
Publicado: (2025)

Carrying over algorithm in transformers
por: Kruthoff, Jorrit
Publicado: (2024)

Embedded Quantum Machine Learning in Embedded Systems: Feasibility, Hybrid Architectures, and Quantum Co-Processors
por: Dey, Somdip, et al.
Publicado: (2026)

MolPaQ: Modular Quantum-Classical Patch Learning for Interpretable Molecular Generation
por: Naqvi, Syed Rameez, et al.
Publicado: (2026)

Lower-dimensional projections of cellular expression improves cell type classification from single-cell RNA sequencing
por: Umar, Muhammad, et al.
Publicado: (2024)

Which algorithm to select in sports timetabling?
por: Van Bulck, David, et al.
Publicado: (2023)

Transformers converge to invariant algorithmic cores
por: Schiffman, Joshua S.
Publicado: (2026)

From Predictive Importance to Causality: Which Machine Learning Model Reflects Reality?
por: Arshad, Muhammad Arbab, et al.
Publicado: (2024)

Towards Achieving Near-optimal Utility for Privacy-Preserving Federated Learning via Data Generation and Parameter Distortion
por: Zhang, Xiaojin, et al.
Publicado: (2023)

A Generalization Bound for Nearly-Linear Networks
por: Golikov, Eugene
Publicado: (2024)

The GECo algorithm for Graph Neural Networks Explanation
por: Calderaro, Salvatore, et al.
Publicado: (2024)

TurboQuant: Online Vector Quantization with Near-optimal Distortion Rate
por: Zandieh, Amir, et al.
Publicado: (2025)

Clustering and Median Aggregation Improve Differentially Private Inference
por: Amin, Kareem, et al.
Publicado: (2025)

Why Gradients Rapidly Increase Near the End of Training
por: Defazio, Aaron
Publicado: (2025)

Near-Optimal Online Deployment and Routing for Streaming LLMs
por: Li, Shaoang, et al.
Publicado: (2025)

Realizable Abstractions: Near-Optimal Hierarchical Reinforcement Learning
por: Cipollone, Roberto, et al.
Publicado: (2025)

Competition-Aware CPC Forecasting with Near-Market Coverage
por: Frey, Sebastian, et al.
Publicado: (2026)

Beyond Uniform Sampling: Synergistic Active Learning and Input Denoising for Robust Neural Operators
por: Roy, Samrendra, et al.
Publicado: (2026)

Bellman operator convergence enhancements in reinforcement learning algorithms
por: Kadurha, David Krame, et al.
Publicado: (2025)

Be aware of overfitting by hyperparameter optimization!
por: Tetko, Igor V., et al.
Publicado: (2024)

Multiple kernel concept factorization algorithm based on global fusion
por: Li, Fei, et al.
Publicado: (2024)

Generalizing soft actor-critic algorithms to discrete action spaces
por: Zhang, Le, et al.
Publicado: (2024)

FRRI: a novel algorithm for fuzzy-rough rule induction
por: Bollaert, Henri, et al.
Publicado: (2024)

Understanding the performance gap between online and offline alignment algorithms
por: Tang, Yunhao, et al.
Publicado: (2024)

Auxiliary task discovery through generate-and-test
por: Rafiee, Banafsheh, et al.
Publicado: (2022)