Search Results for author:
Found 19 papers, 9 papers with code
RIS-Assisted Joint Uplink Communication and Imaging: Phase Optimization and Bayesian Echo Decoupling
Achieving integrated sensing and communication (ISAC) via uplink transmission is challenging due to the unknown waveform and the coupling of communication and sensing echoes.
GFlowCausal: Generative Flow Networks for Causal Discovery
Causal discovery aims to uncover causal structure among a set of variables.
Reframed GES with a Neural Conditional Dependence Measure
In this paper, we revisit the Greedy Equivalence Search (GES) algorithm, which is widely cited as a score-based algorithm for learning the MEC of the underlying causal structure.
Out-of-distribution Generalization with Causal Invariant Transformations
In this work, we obviate these assumptions and tackle the OOD problem without explicitly recovering the causal feature.
ZIN: When and How to Learn Invariance Without Environment Partition?
When data are divided into distinct environments according to the heterogeneity, recent invariant learning methods have proposed to learn robust and invariant models based on this environment partition.
A Semi-Synthetic Dataset Generation Framework for Causal Inference in Recommender Systems
To better support the studies of causal inference and further explanations in recommender systems, we propose a novel semi-synthetic data generation framework for recommender systems where causal graphical models with missingness are employed to describe the causal mechanism of practical recommendation scenarios.
Universality of parametric Coupling Flows over parametric diffeomorphisms
Invertible neural networks based on Coupling Flows CFlows) have various applications such as image synthesis and data compression.
gCastle: A Python Toolbox for Causal Discovery
$\texttt{gCastle}$ is an end-to-end Python toolbox for causal structure learning.
Improving OOD Generalization with Causal Invariant Transformations
In this work, we obviate these assumptions and tackle the OOD problem without explicitly recovering the causal feature.
Ordering-Based Causal Discovery with Reinforcement Learning
It is a long-standing question to discover causal relations among a set of variables in many empirical sciences.
A Local Method for Identifying Causal Relations under Markov Equivalence
We propose a local approach to identify whether a variable is a cause of a given target under the framework of causal graphical models of directed acyclic graphs (DAGs).
On Low Rank Directed Acyclic Graphs and Causal Structure Learning
Despite several advances in recent years, learning causal structures represented by directed acyclic graphs (DAGs) remains a challenging task in high dimensional settings when the graphs to be learned are not sparse.
A Graph Autoencoder Approach to Causal Structure Learning
Causal structure learning has been a challenging task in the past decades and several mainstream approaches such as constraint- and score-based methods have been studied with theoretical guarantees.
Masked Gradient-Based Causal Structure Learning
This paper studies the problem of learning causal structures from observational data.
Causal Discovery by Kernel Intrinsic Invariance Measure
We show our algorithm can be reduced to an eigen-decomposition task on a kernel matrix measuring intrinsic deviance/invariance.
Asymptotically Optimal One- and Two-Sample Testing with Kernels
With Sanov's theorem, we derive a sufficient condition for one-sample tests to achieve the optimal error exponent in the universal setting, i. e., for any distribution defining the alternative hypothesis.
Causal Discovery with Reinforcement Learning
The reward incorporates both the predefined score function and two penalty terms for enforcing acyclicity.
Exponentially Consistent Kernel Two-Sample Tests
Given two sets of independent samples from unknown distributions $P$ and $Q$, a two-sample test decides whether to reject the null hypothesis that $P=Q$.
Universal Hypothesis Testing with Kernels: Asymptotically Optimal Tests for Goodness of Fit
We show that two classes of Maximum Mean Discrepancy (MMD) based tests attain this optimality on $\mathbb R^d$, while the quadratic-time Kernel Stein Discrepancy (KSD) based tests achieve the maximum exponential decay rate under a relaxed level constraint.
