Valentyn Melnychuk
14 papers · 2022–2026 · 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
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π£ Hot Topic Early Bird π Conference Polyglot Β· Silver π Interdisciplinary Bridge Β· Silver πΊοΈ Taxonomy Completionist (19) π€ Dynamic Duo (14)
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Dynamic Duo
(14)
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Triple Crown
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Deep Specialist
(12)
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Prolific Year Β· Productive
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Unstoppable Β· Bronze
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Century Club Β· Bronze
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Renaissance Researcher
(5)
ποΈ
Keyword Collector
(59)
Venues
Top co-authors
Research topics
Artificial Intelligence
(13)
Core AI
(13)
Machine Learning
(10)
Optimization & Theory
(5)
Bayesian & Probabilistic
(4)
Healthcare & Medicine
(3)
Knowledge & Reasoning
(3)
Application Areas
(3)
Clinical
(3)
Reasoning
(3)
Deep Learning
(2)
Reinforcement Learning
(2)
Core Methods
(2)
Learning Paradigms
(2)
Learning Types
(2)
Keywords
causal inference
(11)
treatment effect
(4)
sensitivity analysis
(3)
partial identification
(3)
heterogeneous treatment effect
(2)
unobserved confounding
(2)
variational inference
(2)
potential outcome
(2)
normalizing flow
(2)
off-policy learning
(2)
time-varying confounder
(2)
generalization bound
(1)
uncertainty quantification
(1)
diffusion model
(1)
generative model
(1)
conditional distribution
(1)
density estimation
(1)
representation learning
(1)
treatment effect estimation
(1)
personalized medicine
(1)
Papers
Consistent End-to-End Estimation for Counterfactual Fairness
CLeaR 2026
Differentially private learners for heterogeneous treatment effects
ICLR 2025
A Neural Framework for Generalized Causal Sensitivity Analysis
ICLR 2024
Bayesian Neural Controlled Differential Equations for Treatment Effect Estimation
ICLR 2024
Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimation
ICLR 2024
DiffPO: A causal diffusion model for learning distributions of potential outcomes
NeurIPS 2024
Fair Off-Policy Learning from Observational Data
ICML 2024
Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learner
NeurIPS 2024
Estimating Average Causal Effects from Patient Trajectories
AAAI 2023
Normalizing Flows for Interventional Density Estimation
ICML 2023
Partial Counterfactual Identification of Continuous Outcomes with a Curvature Sensitivity Model
NeurIPS 2023
Reliable Off-Policy Learning for Dosage Combinations
NeurIPS 2023
Sharp Bounds for Generalized Causal Sensitivity Analysis
NeurIPS 2023
Causal Transformer for Estimating Counterfactual Outcomes
ICML 2022