← Authors

Mihaela van der Schaar

174 papers · 2014–2025 · 9 conferences · across top CS/AI conferences

Achievements

🐝 Cross-Pollinator (15) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird πŸƒ Academic Marathon (11) 🌍 Conference Polyglot (9) 🌈 Renaissance Researcher (6) πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (32) 🏠 Conference Loyalist (61) 🀝 Dynamic Duo (25) πŸ‘‘ Triple Crown πŸ† Keyword Champion πŸ† Grand Slam 🌱 Topic Pioneer πŸ”¬ Deep Specialist (25) πŸš€ Conference Pioneer ⚑ Prolific Year (18) πŸ”₯ Unstoppable (12) ❓ The Questioner (6) πŸ’Ž Century Club (174) πŸ—ƒοΈ Keyword Collector (123) πŸ“ˆ Trend Setter

Conferences

NIPS (61) ICML (47) ICLR (37) AISTATS (22) MLHC (2) UAI (2) AAAI (1) ACL (1) JMLR (1)

Top co-authors

Zhaozhi Qian (25) Nabeel Seedat (19) Alihan HΓΌyΓΌk (17) Daniel Jarrett (16) Jonathan Crabbe (15) Alicia Curth (15) Tennison Liu (14) Ioana Bica (14) Fergus Imrie (14) Jeroen Berrevoets (13)

Research topics

Differential Privacy (1) Privacy (1)

Keywords

causal inference (24) representation learning (10) treatment effect (8) uncertainty quantification (7) heterogeneous treatment effect (7) survival analysis (6) large language model (6) treatment effect estimation (6) generative adversarial network (5) tabular datum (5) generative model (4) disease progression (4) covariate shift (4) ensemble learning (4) sequential decision (4) policy learning (4) conformal prediction (4) imitation learning (4) feature importance (4) organ transplantation (4)

Papers

The Synergy of LLMs & RL Unlocks Offline Learning of Generalizable Language-Conditioned Policies with Low-fidelity Data ICML 2025
Stochastic Encodings for Active Feature Acquisition ICML 2025
Unified Screening for Multiple Diseases ICML 2025
Preference Learning for AI Alignment: a Causal Perspective ICML 2025
Skip the Equations: Learning Behavior of Personalized Dynamical Systems Directly From Data ICML 2025
Continuously Updating Digital Twins using Large Language Models ICML 2025
Autoformulation of Mathematical Optimization Models Using LLMs ICML 2025
G-Sim: Generative Simulations with Large Language Models and Gradient-Free Calibration ICML 2025
Inverse Reinforcement Learning Meets Large Language Model Alignment ACL 2025
Decision Tree Induction Through LLMs via Semantically-Aware Evolution ICLR 2025
No Equations Needed: Learning System Dynamics Without Relying on Closed-Form ODEs ICLR 2025
Towards Automated Knowledge Integration From Human-Interpretable Representations ICLR 2025
Active Task Disambiguation with LLMs ICLR 2025
Going Beyond Static: Understanding Shifts with Time-Series Attribution ICLR 2025
Risk-Sensitive Diffusion: Robustly Optimizing Diffusion Models with Noisy Samples ICLR 2025
Position: Truly Self-Improving Agents Require Intrinsic Metacognitive Learning ICML 2025
Position: All Current Generative Fidelity and Diversity Metrics are Flawed ICML 2025
Towards Regulatory-Confirmed Adaptive Clinical Trials: Machine Learning Opportunities and Solutions AISTATS 2025
Beyond Size-Based Metrics: Measuring Task-Specific Complexity in Symbolic Regression AISTATS 2025
Active Feature Acquisition for Personalised Treatment Assignment AISTATS 2025
Visualizing token importance for black-box language models AISTATS 2025
Differentiable Causal Structure Learning with Identifiability by NOTIME AISTATS 2025
Position: Not All Explanations for Deep Learning Phenomena Are Equally Valuable ICML 2025
AutoCATE: End-to-End, Automated Treatment Effect Estimation ICML 2025
Bootstrapping Self-Improvement of Language Model Programs for Zero-Shot Schema Matching ICML 2025
Strategic Planning: A Top-Down Approach to Option Generation ICML 2025
Statistical Hypothesis Testing for Auditing Robustness in Language Models ICML 2025
L2MAC: Large Language Model Automatic Computer for Extensive Code Generation ICLR 2024
Dissecting Sample Hardness: A Fine-Grained Analysis of Hardness Characterization Methods for Data-Centric AI ICLR 2024
Towards Transparent Time Series Forecasting ICLR 2024
Defining Expertise: Applications to Treatment Effect Estimation ICLR 2024
ODE Discovery for Longitudinal Heterogeneous Treatment Effects Inference ICLR 2024
On Error Propagation of Diffusion Models ICLR 2024
Soft Mixture Denoising: Beyond the Expressive Bottleneck of Diffusion Models ICLR 2024
A Neural Framework for Generalized Causal Sensitivity Analysis ICLR 2024
Query-Dependent Prompt Evaluation and Optimization with Offline Inverse RL ICLR 2024
Active Learning with LLMs for Partially Observed and Cost-Aware Scenarios NIPS 2024
Self-Healing Machine Learning: A Framework for Autonomous Adaptation in Real-World Environments NIPS 2024
Automatically Learning Hybrid Digital Twins of Dynamical Systems NIPS 2024
Discovering Preference Optimization Algorithms with and for Large Language Models NIPS 2024
Data-Driven Discovery of Dynamical Systems in Pharmacology using Large Language Models NIPS 2024
A theoretical design of concept sets: improving the predictability of concept bottleneck models NIPS 2024
Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learner NIPS 2024
Context-Aware Testing: A New Paradigm for Model Testing with Large Language Models NIPS 2024
Deep Learning Through A Telescoping Lens: A Simple Model Provides Empirical Insights On Grokking, Gradient Boosting & Beyond NIPS 2024
Discovering Features with Synergistic Interactions in Multiple Views ICML 2024
Time Series Diffusion in the Frequency Domain ICML 2024
Position: Why Tabular Foundation Models Should Be a Research Priority ICML 2024
Adaptive Experiment Design with Synthetic Controls AISTATS 2024
DAGnosis: Localized Identification of Data Inconsistencies using Structures AISTATS 2024
Shape Arithmetic Expressions: Advancing Scientific Discovery Beyond Closed-Form Equations AISTATS 2024
Dense Reward for Free in Reinforcement Learning from Human Feedback ICML 2024
Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in low-data regimes ICML 2024
Meta-Learners for Partially-Identified Treatment Effects Across Multiple Environments ICML 2024
Relaxed Quantile Regression: Prediction Intervals for Asymmetric Noise ICML 2024
Large Language Models to Enhance Bayesian Optimization ICLR 2024
Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over Time ICML 2023
Differentiable and Transportable Structure Learning ICML 2023
Improving Adaptive Conformal Prediction Using Self-Supervised Learning AISTATS 2023
SurvivalGAN: Generating Time-to-Event Data for Survival Analysis AISTATS 2023
AllSim: Simulating and Benchmarking Resource Allocation Policies in Multi-User Systems NIPS 2023
Can You Rely on Your Model Evaluation? Improving Model Evaluation with Synthetic Test Data NIPS 2023
Accountability in Offline Reinforcement Learning: Explaining Decisions with a Corpus of Examples NIPS 2023
Synthcity: a benchmark framework for diverse use cases of tabular synthetic data NIPS 2023
What is Flagged in Uncertainty Quantification? Latent Density Models for Uncertainty Categorization NIPS 2023
Risk-Averse Active Sensing for Timely Outcome Prediction under Cost Pressure NIPS 2023
Joint Training of Deep Ensembles Fails Due to Learner Collusion NIPS 2023
D-CIPHER: Discovery of Closed-form Partial Differential Equations NIPS 2023
Reimagining Synthetic Tabular Data Generation through Data-Centric AI: A Comprehensive Benchmark NIPS 2023
Active Observing in Continuous-time Control NIPS 2023
A U-turn on Double Descent: Rethinking Parameter Counting in Statistical Learning NIPS 2023
Evaluating the Robustness of Interpretability Methods through Explanation Invariance and Equivariance NIPS 2023
TRIAGE: Characterizing and auditing training data for improved regression NIPS 2023
Synthetic Data, Real Errors: How (Not) to Publish and Use Synthetic Data ICML 2023
Learning Representations without Compositional Assumptions ICML 2023
In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect Estimation ICML 2023
Adaptive Identification of Populations with Treatment Benefit in Clinical Trials: Machine Learning Challenges and Solutions ICML 2023
Deep Generative Symbolic Regression ICLR 2023
When to Make and Break Commitments? ICLR 2023
TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization ICLR 2023
Neural Laplace Control for Continuous-time Delayed Systems AISTATS 2023
T-Phenotype: Discovering Phenotypes of Predictive Temporal Patterns in Disease Progression AISTATS 2023
Membership Inference Attacks against Synthetic Data through Overfitting Detection AISTATS 2023
To Impute or not to Impute? Missing Data in Treatment Effect Estimation AISTATS 2023
Understanding the Impact of Competing Events on Heterogeneous Treatment Effect Estimation from Time-to-Event Data AISTATS 2023
GOGGLE: Generative Modelling for Tabular Data by Learning Relational Structure ICLR 2023
Synthetic Model Combination: An Instance-wise Approach to Unsupervised Ensemble Learning NIPS 2022
Data-SUITE: Data-centric identification of in-distribution incongruous examples ICML 2022
Inferring Lexicographically-Ordered Rewards from Preferences AAAI 2022
Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular data NIPS 2022
Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations ICML 2022
Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies ICLR 2022
Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability NIPS 2022
Concept Activation Regions: A Generalized Framework For Concept-Based Explanations NIPS 2022
Online Decision Mediation NIPS 2022
Neural graphical modelling in continuous-time: consistency guarantees and algorithms ICLR 2022
Identifiable Energy-based Representations: An Application to Estimating Heterogeneous Causal Effects AISTATS 2022
POETREE: Interpretable Policy Learning with Adaptive Decision Trees ICLR 2022
Self-Supervision Enhanced Feature Selection with Correlated Gates ICLR 2022
D-CODE: Discovering Closed-form ODEs from Observed Trajectories ICLR 2022
How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models ICML 2022
Label-Free Explainability for Unsupervised Models ICML 2022
Neural Laplace: Learning diverse classes of differential equations in the Laplace domain ICML 2022
Inverse Contextual Bandits: Learning How Behavior Evolves over Time ICML 2022
HyperImpute: Generalized Iterative Imputation with Automatic Model Selection ICML 2022
Composite Feature Selection Using Deep Ensembles NIPS 2022
Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects Estimation NIPS 2022
A kernel two-sample test with selection bias UAI 2021
SyncTwin: Treatment Effect Estimation with Longitudinal Outcomes NIPS 2021
Invariant Causal Imitation Learning for Generalizable Policies NIPS 2021
Conformal Time-series Forecasting NIPS 2021
Integrating Expert ODEs into Neural ODEs: Pharmacology and Disease Progression NIPS 2021
Explaining Latent Representations with a Corpus of Examples NIPS 2021
On Inductive Biases for Heterogeneous Treatment Effect Estimation NIPS 2021
DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks NIPS 2021
Closing the loop in medical decision support by understanding clinical decision-making: A case study on organ transplantation NIPS 2021
Estimating Multi-cause Treatment Effects via Single-cause Perturbation NIPS 2021
MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms NIPS 2021
SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data NIPS 2021
Time-series Generation by Contrastive Imitation NIPS 2021
A Variational Information Bottleneck Approach to Multi-Omics Data Integration AISTATS 2021
Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms AISTATS 2021
Learning Matching Representations for Individualized Organ Transplantation Allocation AISTATS 2021
SDF-Bayes: Cautious Optimism in Safe Dose-Finding Clinical Trials with Drug Combinations and Heterogeneous Patient Groups AISTATS 2021
Clairvoyance: A Pipeline Toolkit for Medical Time Series ICLR 2021
Generative Time-series Modeling with Fourier Flows ICLR 2021
Explaining by Imitating: Understanding Decisions by Interpretable Policy Learning ICLR 2021
Scalable Bayesian Inverse Reinforcement Learning ICLR 2021
Learning "What-if" Explanations for Sequential Decision-Making ICLR 2021
Policy Analysis using Synthetic Controls in Continuous-Time ICML 2021
Learning Queueing Policies for Organ Transplantation Allocation using Interpretable Counterfactual Survival Analysis ICML 2021
Explaining Time Series Predictions with Dynamic Masks ICML 2021
Inverse Decision Modeling: Learning Interpretable Representations of Behavior ICML 2021
Application of kernel hypothesis testing on set-valued data UAI 2021
Temporal Phenotyping using Deep Predictive Clustering of Disease Progression ICML 2020
Inverse Active Sensing: Modeling and Understanding Timely Decision-Making ICML 2020
Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift ICML 2020
Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders ICML 2020
Frequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence Functions ICML 2020
Discriminative Jackknife: Quantifying Uncertainty in Deep Learning via Higher-Order Influence Functions ICML 2020
Estimating counterfactual treatment outcomes over time through adversarially balanced representations ICLR 2020
Target-Embedding Autoencoders for Supervised Representation Learning ICLR 2020
OrganITE: Optimal transplant donor organ offering using an individual treatment effect NIPS 2020
Learning outside the Black-Box: The pursuit of interpretable models NIPS 2020
Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks NIPS 2020
VIME: Extending the Success of Self- and Semi-supervised Learning to Tabular Domain NIPS 2020
When and How to Lift the Lockdown? Global COVID-19 Scenario Analysis and Policy Assessment using Compartmental Gaussian Processes NIPS 2020
Strictly Batch Imitation Learning by Energy-based Distribution Matching NIPS 2020
Robust Recursive Partitioning for Heterogeneous Treatment Effects with Uncertainty Quantification NIPS 2020
Gradient Regularized V-Learning for Dynamic Treatment Regimes NIPS 2020
CASTLE: Regularization via Auxiliary Causal Graph Discovery NIPS 2020
Learning for Dose Allocation in Adaptive Clinical Trials with Safety Constraints ICML 2020
KnockoffGAN: Generating Knockoffs for Feature Selection using Generative Adversarial Networks ICLR 2019
Differentially Private Bagging: Improved utility and cheaper privacy than subsample-and-aggregate NIPS 2019
Validating Causal Inference Models via Influence Functions ICML 2019
Demystifying Black-box Models with Symbolic Metamodels NIPS 2019
Attentive State-Space Modeling of Disease Progression NIPS 2019
INVASE: Instance-wise Variable Selection using Neural Networks ICLR 2019
PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees ICLR 2019
Boosting Transfer Learning with Survival Data from Heterogeneous Domains AISTATS 2019
Conditional Independence Testing using Generative Adversarial Networks NIPS 2019
Time-series Generative Adversarial Networks NIPS 2019
Multitask Boosting for Survival Analysis with Competing Risks NIPS 2018
Deep Sensing: Active Sensing using Multi-directional Recurrent Neural Networks ICLR 2018
GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets ICLR 2018
A Hidden Absorbing Semi-Markov Model for Informatively Censored Temporal Data: Learning and Inference JMLR 2018
Boosted Trees for Risk Prognosis MLHC 2018
Disease-Atlas: Navigating Disease Trajectories using Deep Learning MLHC 2018
Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes NIPS 2017
DPSCREEN: Dynamic Personalized Screening NIPS 2017
A Non-parametric Learning Method for Confidently Estimating Patient's Clinical State and Dynamics NIPS 2016
Balancing Suspense and Surprise: Timely Decision Making with Endogenous Information Acquisition NIPS 2016
Global Multi-armed Bandits with HΓΆlder Continuity AISTATS 2015
Discovering, Learning and Exploiting Relevance NIPS 2014