Parametric Approximation

Parametric approximation focuses on efficiently representing complex functions or datasets using simpler, parameterized models. Current research emphasizes developing improved algorithms and model architectures, such as Gaussian processes, tensor networks, and neural networks, to achieve accurate approximations while managing computational complexity, particularly in high-dimensional spaces. This approach is proving valuable across diverse fields, including machine learning, dynamical systems modeling, and optimization problems, by enabling faster computation and improved scalability for challenging tasks. The resulting efficiency gains are particularly impactful in applications with large datasets or computationally expensive operations.

Papers

October 16, 2024

Triplet: Triangle Patchlet for Mesh-Based Inverse Rendering and Scene Parameters Approximation
Jiajie Yang
Inverse Rendering Image Patch Scene Structure Ray Tracing High Quality Geometry Parametric Approximation

January 10, 2024

Diversity-aware clustering: Computational Complexity and Approximation Algorithms
Suhas Thejaswi, Ameet Gadekar, Bruno Ordozgoiti, Aristides Gionis
Computational Complexity K$ Mean Approximation Algorithm Polynomial Time Approximation Parametric Approximation

December 19, 2023

Gaussian process learning of nonlinear dynamics
Dongwei Ye, Mengwu Guo
Gaussian Process Dynamical System Nonlinear Dynamic Dynamical Model Parametric Approximation

October 31, 2023

Projecting basis functions with tensor networks for Gaussian process regression
Clara Menzen, Eva Memmel, Kim Batselier, Manon Kok
Gaussian Process Gaussian Process Regression Tensor Network Basis Function Parametric Approximation

May 16, 2023

A Note on Dimensionality Reduction in Deep Neural Networks using Empirical Interpolation Method
Harbir Antil, Madhu Gupta, Randy Price
Deep Neural Network Dimensionality Reduction DNN Framework Short Note Parametric Approximation Empirical Interpolation Method

May 12, 2023

Parameterized Approximation for Robust Clustering in Discrete Geometric Spaces
Fateme Abbasi, Sandip Banerjee, Jarosław Byrka, Parinya Chalermsook, Ameet Gadekar, Kamyar Khodamoradi, Dániel Marx, Roohani Sharma, Joachim Spoerhase
K$ Mean Robust Clustering Discrete Space Parametric Approximation K$ Center Disjoint Cluster High Dimensional Euclidean Space

April 6, 2023

Parameterized Approximation Schemes for Clustering with General Norm Objectives
Fateme Abbasi, Sandip Banerjee, Jarosław Byrka, Parinya Chalermsook, Ameet Gadekar, Kamyar Khodamoradi, Dániel Marx, Roohani Sharma, Joachim Spoerhase
K$ Mean Parametric Approximation K$ Clustering Target Norm Geodesic Metric Space

March 8, 2023

Nonlinear Kalman Filtering with Reparametrization Gradients
San Gultekin, Brendan Kitts, Aaron Flores, John Paisley
State Space Model Kullback Leibler Divergence Parametric Approximation Nonlinear State Estimation Reparametrization Gradient

February 7, 2023

Efficient Parametric Approximations of Neural Network Function Space Distance
Nikita Dhawan, Sicong Huang, Juhan Bae, Roger Grosse
Efficient Approximation Linear Activation Parametric Approximation

November 16, 2022

Global Optimization with Parametric Function Approximation
Chong Liu, Yu-Xiang Wang
Bayesian Optimization Global Optimization UCB Algorithm Optimistic Exploration Parametric Approximation

May 11, 2022

Automated differential equation solver based on the parametric approximation optimization
Alexander Hvatov, Tatiana Tikhonova
Differential Equation Numerical Method Parametric Approximation Differential Equation Solver

January 1, 2022

The Parametric Cost Function Approximation: A new approach for multistage stochastic programming
Warren B Powell, Saeed Ghadimi
Novel Approach Stochastic Programming Parametric Approximation Multistage Stochastic Deterministic Optimization