Patrick Schwab
12 papers · 2018–2026 · 6 venues · across top CS/AI venues
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Achievements
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🗺️ Taxonomy Completionist (27) 🌍 Conference Polyglot · Silver 🏃 Academic Marathon · Sprint 🌉 Interdisciplinary Bridge · Silver 🐣 Hot Topic Early Bird
🧬
Topic Evolution
🏆
Grand Slam
🏆
Keyword Champion
🗃️
Keyword Collector
(65)
💎
Century Club · Bronze
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Conference Pioneer
Top co-authors
Research topics
Machine Learning
(9)
Artificial Intelligence
(7)
Healthcare & Medicine
(6)
Core AI
(6)
Deep Learning
(5)
Learning Types · Machine Learning
(5)
Application Areas
(4)
Clinical
(4)
Core Methods
(4)
Knowledge & Reasoning
(3)
Learning Types · Deep Learning
(3)
Reasoning
(3)
Architectures
(2)
Learning Paradigms
(2)
Optimization & Theory
(2)
Keywords
causal inference
(4)
drug discovery
(2)
federated learning
(2)
feature importance
(2)
multi-task learning
(2)
attention mechanism
(2)
model aggregation
(1)
causal structure learning
(1)
feature learning
(1)
uncertainty quantification
(1)
reinforcement learning
(1)
binary classification
(1)
time series classification
(1)
deep learning
(1)
granger causality
(1)
benchmark suite
(1)
message passing
(1)
parameter sharing
(1)
experimental design
(1)
representation learning
(1)
Papers
Neuro-Symbolic Federated Learning over Heterogeneous Data-Views: A Structured Approach to Distributive EHR Modelling
AAAI 2026
Deriving Causal Order from Single-Variable Interventions: Guarantees & Algorithm
ICLR 2025
Information Transfer Across Clinical Tasks via Adaptive Parameter Optimisation
AISTATS 2025
Optimising Clinical Federated Learning through Mode Connectivity-based Model Aggregation
AISTATS 2025
The CausalBench challenge: A machine learning contest for gene network inference from single-cell perturbation data
CLeaR 2025
DiscoBAX: Discovery of optimal intervention sets in genomic experiment design
ICML 2023
GeneDisco: A Benchmark for Experimental Design in Drug Discovery
ICLR 2022
Learning Counterfactual Representations for Estimating Individual Dose-Response Curves
AAAI 2020
CXPlain: Causal Explanations for Model Interpretation under Uncertainty
NeurIPS 2019
Granger-Causal Attentive Mixtures of Experts: Learning Important Features with Neural Networks
AAAI 2019
PhoneMD: Learning to Diagnose Parkinson’s Disease from Smartphone Data
AAAI 2019
Not to Cry Wolf: Distantly Supervised Multitask Learning in Critical Care
ICML 2018