Image de couverture de livre

Approximation of group explainers with coalition structure using Monte Carlo sampling on the product space of coalitions and features

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Kotsiopoulos, Konstandinos, Miroshnikov, Alexey, Filom, Khashayar, Kannan, Arjun Ravi
Format:	Preprint
Publié:	2023
Sujets:	Machine Learning Probability
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Stability theory of game-theoretic group feature explanations for machine learning models
par: Miroshnikov, Alexey, et autres
Publié: (2021)

On marginal feature attributions of tree-based models
par: Filom, Khashayar, et autres
Publié: (2023)

Explainable post-training bias mitigation with distribution-based fairness metrics
par: Franks, Ryan, et autres
Publié: (2025)

Mechanistic interpretability of large language models with applications to the financial services industry
par: Golgoon, Ashkan, et autres
Publié: (2024)

MBExplainer: Multilevel bandit-based explanations for downstream models with augmented graph embeddings
par: Golgoon, Ashkan, et autres
Publié: (2024)

Reverse-BSDE Monte Carlo
par: Batista, Jairon H. N., et autres
Publié: (2025)

Approximating Langevin Monte Carlo with ResNet-like Neural Network architectures
par: Miranda, Charles, et autres
Publié: (2023)

Gaussian Approximation for Asynchronous Q-learning
par: Rubtsov, Artemy, et autres
Publié: (2026)

High-Order Langevin Monte Carlo Algorithms
par: Dang, Thanh, et autres
Publié: (2025)

Convergence of Kinetic Langevin Monte Carlo on Lie groups
par: Kong, Lingkai, et autres
Publié: (2024)

Regime-Switching Langevin Monte Carlo Algorithms
par: Wang, Xiaoyu, et autres
Publié: (2025)

Accelerating Langevin Monte Carlo Sampling: A Large Deviations Analysis
par: Yao, Nian, et autres
Publié: (2025)

Convergence Bounds for Sequential Monte Carlo on Multimodal Distributions using Soft Decomposition
par: Lee, Holden, et autres
Publié: (2024)

A hierarchical entropy method for the delocalization of bias in high-dimensional Langevin Monte Carlo
par: Lacker, Daniel, et autres
Publié: (2025)

Some aspects of robustness in modern Markov Chain Monte Carlo
par: Power, Sam, et autres
Publié: (2025)

Hamiltonian Monte Carlo with Asymmetrical Momentum Distributions
par: Ghosh, Soumyadip, et autres
Publié: (2021)

Algorithms and Scientific Software for Quasi-Monte Carlo, Fast Gaussian Process Regression, and Scientific Machine Learning
par: Sorokin, Aleksei G.
Publié: (2025)

Universal approximation property of Banach space-valued random feature models including random neural networks
par: Neufeld, Ariel, et autres
Publié: (2023)

Non-asymptotic Analysis of Diffusion Annealed Langevin Monte Carlo for Generative Modelling
par: Cordero-Encinar, Paula, et autres
Publié: (2025)

Sampling conditioned diffusions via Pathspace Projected Monte Carlo
par: Grafke, Tobias
Publié: (2025)

Parallelized Midpoint Randomization for Langevin Monte Carlo
par: Yu, Lu, et autres
Publié: (2024)

TB or Not TB: Coverage-Driven Direct Preference Optimization for Verilog Stimulus Generation
par: Nadimi, Bardia, et autres
Publié: (2025)

Monte Carlo Neural PDE Solver for Learning PDEs via Probabilistic Representation
par: Zhang, Rui, et autres
Publié: (2023)

Accelerating Multilevel Markov Chain Monte Carlo Using Machine Learning Models
par: Reddy, Sohail, et autres
Publié: (2024)

Complexity of Markov Chain Monte Carlo for Generalized Linear Models
par: Chak, Martin, et autres
Publié: (2025)

User-friendly guarantees for the Langevin Monte Carlo with inaccurate gradient
par: Dalalyan, Arnak S., et autres
Publié: (2017)

Learning Mixtures of Gaussians Using Diffusion Models
par: Gatmiry, Khashayar, et autres
Publié: (2024)

Langevin Monte-Carlo Provably Learns Depth Two Neural Nets at Any Size and Data
par: Kumar, Dibyakanti, et autres
Publié: (2025)

Steady-State Behavior of Constant-Stepsize Stochastic Approximation: Gaussian Approximation and Tail Bounds
par: Wang, Zedong, et autres
Publié: (2026)

Non-asymptotic estimates for accelerated high order Langevin Monte Carlo algorithms
par: Neufeld, Ariel, et autres
Publié: (2024)

Simple Approximation and Derivative Free Inference-Time Scaling for Diffusion Models via Sequential Monte Carlo on Path Measures
par: Wang, Chenyang, et autres
Publié: (2026)

Towards an Approximation Theory of Observable Operator Models
par: Anyszka, Wojciech
Publié: (2024)

On the convergence of dynamic implementations of Hamiltonian Monte Carlo and No U-Turn Samplers
par: Durmus, Alain, et autres
Publié: (2023)

Surrogate Modelling of Proton Dose with Monte Carlo Dropout Uncertainty Quantification
par: Pim, Aaron, et autres
Publié: (2025)

Performance of Rank-One Tensor Approximation on Incomplete Data
par: Lebeau, Hugo
Publié: (2025)

Approximating G(t)/GI/1 queues with deep learning
par: Sherzer, Eliran, et autres
Publié: (2024)

Extraction of informative statistical features in the problem of forecasting time series generated by It{ô}-type processes
par: Korolev, Victor, et autres
Publié: (2026)

Approximation to Deep Q-Network by Stochastic Delay Differential Equations
par: Lu, Jianya, et autres
Publié: (2025)

Tail-Sensitive KL and Rényi Convergence of Unadjusted Hamiltonian Monte Carlo via One-Shot Couplings
par: Bou-Rabee, Nawaf, et autres
Publié: (2026)

Stochastic Port-Hamiltonian Neural Networks: Universal Approximation with Passivity Guarantees
par: Di Persio, Luca, et autres
Publié: (2026)