Search Results for author:
Found 12 papers, 10 papers with code
Knowledge Graph Entity Alignment with Graph Convolutional Networks: Lessons Learned
In this work, we focus on the problem of entity alignment in Knowledge Graphs (KG) and we report on our experiences when applying a Graph Convolutional Network (GCN) based model for this task.
Ranked #33 on
Entity Alignment
on DBP15k zh-en
Unsupervised Anomaly Detection for X-Ray Images
Therefore, we adopt state-of-the-art approaches for unsupervised learning to detect anomalies and show how the outputs of these methods can be explained.
Matching the Clinical Reality: Accurate OCT-Based Diagnosis From Few Labels
Furthermore, our experiments show that exponential moving average (EMA) of model parameters, which is a component of both algorithms, is not needed for our classification problem, as disabling it leaves the outcome unchanged.
Ranked #3 on
Retinal OCT Disease Classification
on OCT2017
Retinal OCT Disease Classification
Semi-Supervised Image Classification
+1
Estimating average causal effects from patient trajectories
In medical practice, treatments are selected based on the expected causal effects on patient outcomes.
Causal Transformer for Estimating Counterfactual Outcomes
In this paper, we develop a novel Causal Transformer for estimating counterfactual outcomes over time.
Normalizing Flows for Interventional Density Estimation
To the best of our knowledge, our Interventional Normalizing Flows are the first proper fully-parametric, deep learning method for density estimation of potential outcomes.
Fair Off-Policy Learning from Observational Data
Algorithmic decision-making in practice must be fair for legal, ethical, and societal reasons.
Partial Counterfactual Identification of Continuous Outcomes with a Curvature Sensitivity Model
We further show that existing point counterfactual identification methods are special cases of our Curvature Sensitivity Model when the bound of the curvature is set to zero.
Bayesian Neural Controlled Differential Equations for Treatment Effect Estimation
Treatment effect estimation in continuous time is crucial for personalized medicine.
Counterfactual Fairness for Predictions using Generative Adversarial Networks
It is often achieved through counterfactual fairness, which ensures that the prediction for an individual is the same as that in a counterfactual world under a different sensitive attribute.
Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimation
In this paper, we propose a new, representation-agnostic refutation framework for estimating bounds on the representation-induced confounding bias that comes from dimensionality reduction (or other constraints on the representations) in CATE estimation.
A Neural Framework for Generalized Causal Sensitivity Analysis
Unobserved confounding is common in many applications, making causal inference from observational data challenging.
