Search Results for author:
Found 8 papers, 6 papers with code
Multi-Armed Bandits and Quantum Channel Oracles
Here we introduce further bandit models where we only have limited access to the randomness of the rewards, but we can still query the arms in superposition.
AutoML Two-Sample Test
Two-sample tests are important in statistics and machine learning, both as tools for scientific discovery as well as to detect distribution shifts.
Causal Inference Through the Structural Causal Marginal Problem
We introduce an approach to counterfactual inference based on merging information from multiple datasets.
Quantum machine learning beyond kernel methods
In this work, we identify a constructive framework that captures all standard models based on parametrized quantum circuits: that of linear quantum models.
The Inductive Bias of Quantum Kernels
Quantum computers offer the possibility to efficiently compute inner products of exponentially large density operators that are classically hard to compute.
A Witness Two-Sample Test
That is, the test set is used to simultaneously estimate the expectations and define the basis points, while the training set only serves to select the kernel and is discarded.
Learning Kernel Tests Without Data Splitting
Modern large-scale kernel-based tests such as maximum mean discrepancy (MMD) and kernelized Stein discrepancy (KSD) optimize kernel hyperparameters on a held-out sample via data splitting to obtain the most powerful test statistics.
Quantum Mean Embedding of Probability Distributions
The kernel mean embedding of probability distributions is commonly used in machine learning as an injective mapping from distributions to functions in an infinite dimensional Hilbert space.
