:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Talitckii, Aleksandr, Colbert, Brendon K., Peet, Matthew M.
Format:	Preprint
Published:	2023
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2304.07472
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Picard Iteration for Parameter Estimation in Nonlinear Dynamic Models of Aircraft and Spacecraft
by: Talitckii, Aleksandr, et al.
Published: (2026)

Picard Iteration for Parameter Estimation in Nonlinear Ordinary Differential Equations
by: Talitckii, Aleksandr, et al.
Published: (2024)

A $μ$-Analysis and Synthesis Framework for Partial Integral Equations using IQCs
by: Lenssen, Thijs, et al.
Published: (2025)

Optimal and Efficient Algorithms for Decentralized Online Convex Optimization
by: Wan, Yuanyu, et al.
Published: (2024)

Dual Adaptivity: Universal Algorithms for Minimizing the Adaptive Regret of Convex Functions
by: Zhang, Lijun, et al.
Published: (2025)

Combining Reinforcement Learning and Behavior Trees for NPCs in Video Games with AMD Schola
by: Liu, Tian, et al.
Published: (2025)

In-and-Out: Algorithmic Diffusion for Sampling Convex Bodies
by: Kook, Yunbum, et al.
Published: (2024)

Sequential Kernelized Independence Testing
by: Podkopaev, Aleksandr, et al.
Published: (2022)

Distributed Online Convex Optimization with Efficient Communication: Improved Algorithm and Lower bounds
by: Yang, Sifan, et al.
Published: (2026)

Scaling the Convex Barrier with Sparse Dual Algorithms
by: De Palma, Alessandro, et al.
Published: (2021)

Path Generation and Evaluation in Video Games: A Nonparametric Statistical Approach
by: Campa, Daniel, et al.
Published: (2025)

Algorithms for Learning Kernels Based on Centered Alignment
by: Cortes, Corinna, et al.
Published: (2012)

Learning Algorithm Hyperparameters for Fast Parametric Convex Optimization
by: Sambharya, Rajiv, et al.
Published: (2024)

Learning Analysis of Kernel Ridgeless Regression with Asymmetric Kernel Learning
by: He, Fan, et al.
Published: (2024)

Data-Efficient Kernel Methods for Learning Differential Equations and Their Solution Operators: Algorithms and Error Analysis
by: Jalalian, Yasamin, et al.
Published: (2025)

Universal Architectures for the Learning of Polyhedral Norms and Convex Regularizers
by: Unser, Michael, et al.
Published: (2025)

Kernel Recursive Least Squares Dictionary Learning Algorithm
by: Alipoor, Ghasem, et al.
Published: (2025)

Shuffling Momentum Gradient Algorithm for Convex Optimization
by: Tran, Trang H., et al.
Published: (2024)

Convex-Geometric Error Bounds for Positive-Weight Kernel Quadrature
by: Hayakawa, Satoshi
Published: (2026)

Convergence of Clipped-SGD for Convex $(L_0,L_1)$-Smooth Optimization with Heavy-Tailed Noise
by: Chezhegov, Savelii, et al.
Published: (2025)

Convex Distillation: Efficient Compression of Deep Networks via Convex Optimization
by: Varshney, Prateek, et al.
Published: (2024)

Verifying Well-Posedness of Linear PDEs using Convex Optimization
by: Jagt, Declan S., et al.
Published: (2026)

An Asymptotically Optimal Algorithm for the Convex Hull Membership Problem
by: Qiao, Gang, et al.
Published: (2023)

Nearly-Optimal Algorithm for Adversarial Kernelized Bandits
by: Iwazaki, Shogo
Published: (2026)

Simplifying Graph Kernels for Efficient
by: Wang, Lin, et al.
Published: (2025)

Bayesian Hierarchical Models and the Maximum Entropy Principle
by: Brewer, Brendon J.
Published: (2026)

Ito Diffusion Approximation of Universal Ito Chains for Sampling, Optimization and Boosting
by: Ustimenko, Aleksei, et al.
Published: (2023)

MOCK: an Algorithm for Learning Nonparametric Differential Equations via Multivariate Occupation Kernel Functions
by: Rielly, Victor, et al.
Published: (2023)

Qronos: Correcting the Past by Shaping the Future... in Post-Training Quantization
by: Zhang, Shihao, et al.
Published: (2025)

Projection-free Algorithms for Online Convex Optimization with Adversarial Constraints
by: Sarkar, Dhruv, et al.
Published: (2025)

Optimal Anytime Algorithms for Online Convex Optimization with Adversarial Constraints
by: Sarkar, Dhruv, et al.
Published: (2025)

fastkqr: A Fast Algorithm for Kernel Quantile Regression
by: Tang, Qian, et al.
Published: (2024)

Near-Optimal Algorithm for Non-Stationary Kernelized Bandits
by: Iwazaki, Shogo, et al.
Published: (2024)

The Impact of Isolation Kernel on Agglomerative Hierarchical Clustering Algorithms
by: Han, Xin, et al.
Published: (2020)

A New Framework for Convex Clustering in Kernel Spaces: Finite Sample Bounds, Consistency and Performance Insights
by: Pan, Shubhayan, et al.
Published: (2025)

Bridging Algorithmic Information Theory and Machine Learning: A New Approach to Kernel Learning
by: Hamzi, Boumediene, et al.
Published: (2023)

A2Q+: Improving Accumulator-Aware Weight Quantization
by: Colbert, Ian, et al.
Published: (2024)

Universality of Kernel Random Matrices and Kernel Regression in the Quadratic Regime
by: Pandit, Parthe, et al.
Published: (2024)

Communication-Efficient Federated Learning with Adaptive Number of Participants
by: Skorik, Sergey, et al.
Published: (2025)

Representation of PDE Systems with Delay and Stability Analysis using Convex Optimization -- Extended Version
by: Jagt, Declan S., et al.
Published: (2022)