Unsupervised Anomaly Detection for X-Ray Images
D. Davletshina, V. Melnychuk, V. Tran, H. Singla, M. Berrendorf, E. Faerman, M. Fromm, M. Schubert. ArXiv preprint, 2020
Posts by Collection
portfolio
publications
Knowledge Graph Entity Alignment with Graph Convolutional Networks: Lessons Learned
M. Berrendorf, E. Faerman, V. Melnychuk, V. Tresp, T. Seidl. Accepted at ECIR, 2020
Matching the Clinical Reality: Accurate OCT-Based Diagnosis From Few Labels
V. Melnychuk, E. Faerman, I. Manakov, T. Seidl. ArXiv preprint, 2020
Causal Transformer for Estimating Counterfactual Outcomes
V. Melnychuk, D. Frauen, S. Feuerriegel. Accepted at ICML, 2022
Estimating Average Causal Effects from Patient Trajectories
D. Frauen, T. Hatt, V. Melnychuk, S. Feuerriegel. Accepted at AAAI, 2023
Normalizing Flows for Interventional Density Estimation
V. Melnychuk, D. Frauen, S. Feuerriegel. Accepted at ICML, 2023
Counterfactual Fairness for Predictions using Generative Adversarial Networks
Y. Ma, D. Frauen, V. Melnychuk, S. Feuerriegel. ArXiv preprint, 2023
Partial Counterfactual Identification of Continuous Outcomes with a Curvature Sensitivity Model
V. Melnychuk, D. Frauen, S. Feuerriegel. Accepted at NeurIPS, 2023
Reliable Off-Policy Learning for Dosage Combinations
J. Schweisthal, D. Frauen, V. Melnychuk, S. Feuerriegel. Accepted at NeurIPS, 2023
Sharp Bounds for Generalized Causal Sensitivity Analysis
D. Frauen, V. Melnychuk, S. Feuerriegel. Accepted at NeurIPS, 2023
A Neural Framework for Generalized Causal Sensitivity Analysis
D. Frauen, F. Imrie, A. Curth, V. Melnychuk, S. Feuerriegel, M. van der Schaar. Accepted at ICLR, 2024
Bayesian Neural Controlled Differential Equations for Treatment Effect Estimation
K. Hess, V. Melnychuk, D. Frauen, S. Feuerriegel. Accepted at ICLR, 2024
Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimation
V. Melnychuk, D. Frauen, S. Feuerriegel. Accepted at ICLR, 2024
Causal Machine Learning for Predicting Treatment Outcomes
S. Feuerriegel, D. Frauen, V. Melnychuk, J. Schweisthal, K. Hess, A. Curth, S. Bauer, N. Kilbertus, I. S. Kohane & M. van der Schaar. Accepted at Nature Medicine, 2024
Fair Off-Policy Learning from Observational Data
D. Frauen, V. Melnychuk, S. Feuerriegel. Accepted at ICML, 2024
G-Transformer for Conditional Average Potential Outcome Estimation over Time
K. Hess, D. Frauen, V. Melnychuk, S. Feuerriegel. ArXiv preprint, 2024
Conformal Prediction for Causal Effects of Continuous Treatments
M. Schröder, D. Frauen, J. Schweisthal, K. Hess, V. Melnychuk, S. Feuerriegel. ArXiv preprint, 2024
DiffPO: A Causal Diffusion Model for Predicting Potential Outcomes of Treatments
Y. Ma, V. Melnychuk, J. Schweisthal, S. Feuerriegel. Accepted at NeurIPS, 2024
Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learner
V. Melnychuk, S. Feuerriegel, M. van der Schaar. Accepted at NeurIPS, 2024
Orthogonal Representation Learning for Estimating Causal Quantities
V. Melnychuk, D. Frauen, J. Schweisthal, S. Feuerriegel. ArXiv preprint, 2025
Efficient and Sharp Off-Policy Learning under Unobserved Confounding
K. Hess, D. Frauen, V. Melnychuk, S. Feuerriegel. ArXiv preprint, 2025
Differentially Private Learners for Heterogeneous Treatment Effects
M. Schröder, V. Melnychuk, S. Feuerriegel. Accepted at ICLR, 2025
talks
Causal Transformer for Estimating Counterfactual Outcomes, ICML 2022 paper
Published:
Title: Causal Transformer for Estimating Counterfactual Outcomes
| Slides
Causal Transformer for Estimating Counterfactual Outcomes & Normalizing Flows for Interventional Density Estimation, ICML 2022 & 2023 papers
Published:
Title: Causal Transformer for Estimating Counterfactual Outcomes
| Slides
Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimation, ICLR 2024 paper
Published:
Title: Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimation
| Slides
Causal ML for predicting treatment outcomes
Published:
Title: Causal ML for predicting treatment outcomes
| Slides
Causal ML for treatment effect estimation
Published:
Tutorial on the universe of the causal ML methods.
| Slides
Causal ML for predicting treatment outcomes
Published:
Title: Causal ML for predicting treatment outcomes
| Slides
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.