Zoltan Szabo
23 papers · 2007–2026 · 5 venues · across top CS/AI venues
Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.
Achievements
Jump to papers ↓+6 more ↓ Show less ↑
🗺️ Taxonomy Completionist (31) 🏃 Academic Marathon · Full 🌍 Conference Polyglot · Silver 🌉 Interdisciplinary Bridge · Silver 🐣 Hot Topic Early Bird
🧭
Keyword Pioneer
🔬
Deep Specialist
(10)
🗃️
Keyword Collector
(98)
📈
Trend Setter
💎
Century Club · Bronze
🔥
Unstoppable · Bronze
Top co-authors
Research topics
Machine Learning
(22)
Core Methods
(18)
Mathematics & Optimization
(15)
Optimization & Theory
(14)
Mathematics
(9)
Optimization
(7)
Artificial Intelligence
(6)
Bayesian & Probabilistic · Machine Learning
(4)
Core AI
(3)
Bayesian & Probabilistic · Artificial Intelligence
(2)
Data Science & Analytics
(1)
Learning Paradigms
(1)
Methods
(1)
Keywords
kernel methods
(12)
reproducing kernel hilbert space
(7)
maximum mean discrepancy
(6)
hilbert-schmidt independence criterion
(4)
statistical test
(3)
random fourier feature
(3)
convex optimization
(3)
bayesian inference
(2)
distribution regression
(2)
stein's method
(2)
ridge regression
(2)
learning theory
(2)
second-order cone programming
(2)
shape constraint
(2)
statistical testing
(2)
kernel approximation
(2)
quantile regression
(2)
independence testing
(2)
goodness-of-fit test
(2)
dependency testing
(2)
Papers
Kernel Integrated R^2: A Measure of Dependence
UAI 2026
Nyström Kernel Stein Discrepancy
AISTATS 2025
The Minimax Rate of HSIC Estimation for Translation-Invariant Kernels
NeurIPS 2024
Kernelized Cumulants: Beyond Kernel Mean Embeddings
NeurIPS 2023
Nyström M-Hilbert-Schmidt independence criterion
UAI 2023
Functional Output Regression with Infimal Convolution: Exploring the Huber and $ε$-insensitive Losses
ICML 2022
Handling Hard Affine SDP Shape Constraints in RKHSs
JMLR 2022
Hard Shape-Constrained Kernel Machines
NeurIPS 2020
Orlicz Random Fourier Features
JMLR 2020
Infinite Task Learning in RKHSs
AISTATS 2019
MONK Outlier-Robust Mean Embedding Estimation by Median-of-Means
ICML 2019
On Kernel Derivative Approximation with Random Fourier Features
AISTATS 2019
Characteristic and Universal Tensor Product Kernels
JMLR 2018
A Linear-Time Kernel Goodness-of-Fit Test
NeurIPS 2017
An Adaptive Test of Independence with Analytic Kernel Embeddings
ICML 2017
Interpretable Distribution Features with Maximum Testing Power
NeurIPS 2016
Learning Theory for Distribution Regression
JMLR 2016
Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM)
NeurIPS 2015
Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families
NeurIPS 2015
Optimal Rates for Random Fourier Features
NeurIPS 2015
Two-stage sampled learning theory on distributions
AISTATS 2015
Information Theoretical Estimators Toolbox
JMLR 2014
Undercomplete Blind Subspace Deconvolution
JMLR 2007