Mihaela van der Schaar - researchr alias

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Viewing Publication 1 - 100 from 659

2025

No Equations Needed: Learning System Dynamics Without Relying on Closed-Form ODEsKrzysztof Kacprzyk, Mihaela van der Schaar. iclr 2025: [doi]

Active Task Disambiguation with LLMsKasia Kobalczyk, Nicolás Astorga, Tennison Liu, Mihaela van der Schaar. iclr 2025: [doi]

Towards Automated Knowledge Integration From Human-Interpretable RepresentationsKasia Kobalczyk, Mihaela van der Schaar. iclr 2025: [doi]

Risk-Sensitive Diffusion: Robustly Optimizing Diffusion Models with Noisy SamplesYangming Li, Max Ruiz Luyten, Mihaela van der Schaar. iclr 2025: [doi]

Decision Tree Induction Through LLMs via Semantically-Aware EvolutionTennison Liu, Nicolas Huynh, Mihaela van der Schaar. iclr 2025: [doi]

Going Beyond Static: Understanding Shifts with Time-Series AttributionJiashuo Liu, Nabeel Seedat, Peng Cui 0001, Mihaela van der Schaar. iclr 2025: [doi]

2024

Discovering Preference Optimization Algorithms with and for Large Language ModelsChris Lu 0001, Samuel Holt, Claudio Fanconi, Alex J. Chan, Jakob N. Foerster, Mihaela van der Schaar, Robert T. Lange. nips 2024: [doi]

Active Learning with LLMs for Partially Observed and Cost-Aware ScenariosNicolás Astorga, Tennison Liu, Nabeel Seedat, Mihaela van der Schaar. nips 2024: [doi]

Unveiling the Power of Sparse Neural Networks for Feature SelectionZahra Atashgahi, Tennison Liu, Mykola Pechenizkiy, Raymond N. J. Veldhuis, Decebal Constantin Mocanu, Mihaela van der Schaar. ecai 2024: 2669-2676 [doi]

Linear Deconfounded Score Method: Scoring DAGs With Dense Unobserved ConfoundingAlexis Bellot, Mihaela van der Schaar. tnn, 35(4):4948-4962, April 2024. [doi]

Causal Deep Learning: Encouraging Impact on Real-world Problems Through CausalityJeroen Berrevoets, Krzysztof Kacprzyk, Zhaozhi Qian, Mihaela van der Schaar. ftsig, 18(3):200-309, 2024. [doi]

Dense Reward for Free in Reinforcement Learning from Human FeedbackAlex James Chan, Hao Sun, Samuel Holt, Mihaela van der Schaar. icml 2024: [doi]

Time Series Diffusion in the Frequency DomainJonathan Crabbé, Nicolas Huynh, Jan Stanczuk, Mihaela van der Schaar. icml 2024: [doi]

A Neural Framework for Generalized Causal Sensitivity AnalysisDennis Frauen, Fergus Imrie, Alicia Curth, Valentyn Melnychuk, Stefan Feuerriegel, Mihaela van der Schaar. iclr 2024: [doi]

Generalization - a key challenge for responsible AI in patient-facing clinical applicationsLea Goetz, Nabeel Seedat, Robert Vandersluis, Mihaela van der Schaar. npjdm, 7(1), 2024. [doi]

U-PASS: An uncertainty-guided deep learning pipeline for automated sleep stagingElisabeth R. M. Heremans, Nabeel Seedat, Bertien Buyse, Dries Testelmans, Mihaela van der Schaar, Maarten De Vos. cbm, 171:108205, 2024. [doi]

Data-Driven Discovery of Dynamical Systems in Pharmacology using Large Language ModelsSamuel Holt, Zhaozhi Qian, Tennison Liu, James Weatherall, Mihaela van der Schaar. nips 2024: [doi]

L2MAC: Large Language Model Automatic Computer for Extensive Code GenerationSamuel Holt, Max Ruiz Luyten, Mihaela van der Schaar. iclr 2024: [doi]

Automatically Learning Hybrid Digital Twins of Dynamical SystemsSamuel Holt, Tennison Liu, Mihaela van der Schaar. nips 2024: [doi]

DAGnosis: Localized Identification of Data Inconsistencies using StructuresNicolas Huynh, Jeroen Berrevoets, Nabeel Seedat, Jonathan Crabbé, Zhaozhi Qian, Mihaela van der Schaar. aistats 2024: 1864-1872 [doi]

Adaptive Experiment Design with Synthetic ControlsAlihan Hüyük, Zhaozhi Qian, Mihaela van der Schaar. aistats 2024: 1180-1188 [doi]

Defining Expertise: Applications to Treatment Effect EstimationAlihan Hüyük, Qiyao Wei, Alicia Curth, Mihaela van der Schaar. iclr 2024: [doi]

Deep Learning Through A Telescoping Lens: A Simple Model Provides Empirical Insights On Grokking, Gradient Boosting & BeyondAlan Jeffares, Alicia Curth, Mihaela van der Schaar. nips 2024: [doi]

ODE Discovery for Longitudinal Heterogeneous Treatment Effects InferenceKrzysztof Kacprzyk, Samuel Holt, Jeroen Berrevoets, Zhaozhi Qian, Mihaela van der Schaar. iclr 2024: [doi]

Shape Arithmetic Expressions: Advancing Scientific Discovery Beyond Closed-Form EquationsKrzysztof Kacprzyk, Mihaela van der Schaar. aistats 2024: 3601-3609 [doi]

Towards Transparent Time Series ForecastingKrzysztof Kacprzyk, Tennison Liu, Mihaela van der Schaar. iclr 2024: [doi]

Discovering Features with Synergistic Interactions in Multiple ViewsChohee Kim, Mihaela van der Schaar, ChangHee Lee. icml 2024: [doi]

On Error Propagation of Diffusion ModelsYangming Li, Mihaela van der Schaar. iclr 2024: [doi]

Soft Mixture Denoising: Beyond the Expressive Bottleneck of Diffusion ModelsYangming Li, Boris van Breugel, Mihaela van der Schaar. iclr 2024: [doi]

Large Language Models to Enhance Bayesian OptimizationTennison Liu, Nicolás Astorga, Nabeel Seedat, Mihaela van der Schaar. iclr 2024: [doi]

A theoretical design of concept sets: improving the predictability of concept bottleneck modelsMax Ruiz Luyten, Mihaela van der Schaar. nips 2024: [doi]

Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal LearnerValentyn Melnychuk, Stefan Feuerriegel, Mihaela van der Schaar. nips 2024: [doi]

Relaxed Quantile Regression: Prediction Intervals for Asymmetric NoiseThomas Pouplin, Alan Jeffares, Nabeel Seedat, Mihaela van der Schaar. icml 2024: [doi]

Context-Aware Testing: A New Paradigm for Model Testing with Large Language ModelsPaulius Rauba, Nabeel Seedat, Max Ruiz Luyten, Mihaela van der Schaar. nips 2024: [doi]

Self-Healing Machine Learning: A Framework for Autonomous Adaptation in Real-World EnvironmentsPaulius Rauba, Nabeel Seedat, Krzysztof Kacprzyk, Mihaela van der Schaar. nips 2024: [doi]

Meta-Learners for Partially-Identified Treatment Effects Across Multiple EnvironmentsJonas Schweisthal, Dennis Frauen, Mihaela van der Schaar, Stefan Feuerriegel. icml 2024: [doi]

Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in low-data regimesNabeel Seedat, Nicolas Huynh, Boris van Breugel, Mihaela van der Schaar. icml 2024: [doi]

Dissecting Sample Hardness: A Fine-Grained Analysis of Hardness Characterization Methods for Data-Centric AINabeel Seedat, Fergus Imrie, Mihaela van der Schaar. iclr 2024: [doi]

Navigating Data-Centric Artificial Intelligence With DC-Check: Advances, Challenges, and OpportunitiesNabeel Seedat, Fergus Imrie, Mihaela van der Schaar. tai, 5(6):2589-2603, June 2024. [doi]

Query-Dependent Prompt Evaluation and Optimization with Offline Inverse RLHao Sun, Alihan Hüyük, Mihaela van der Schaar. iclr 2024: [doi]

Position: Why Tabular Foundation Models Should Be a Research PriorityBoris van Breugel, Mihaela van der Schaar. icml 2024: [doi]

2023

Conservative Policy Construction Using Variational Autoencoders for Logged Data With Missing ValuesMahed Abroshan, Kai Hou Yip, Cem Tekin, Mihaela van der Schaar. tnn, 34(9):6368-6378, September 2023. [doi]

Data-Driven Online Recommender Systems With Costly Information AcquisitionOnur Atan, Saeed Ghoorchian, Setareh Maghsudi, Mihaela van der Schaar. tsc, 16(1):235-245, January - February 2023. [doi]

Differentiable and Transportable Structure LearningJeroen Berrevoets, Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar. icml 2023: 2206-2233 [doi]

AllSim: Simulating and Benchmarking Resource Allocation Policies in Multi-User SystemsJeroen Berrevoets, Daniel Jarrett, Alex J. Chan, Mihaela van der Schaar. nips 2023: [doi]

To Impute or not to Impute? Missing Data in Treatment Effect EstimationJeroen Berrevoets, Fergus Imrie, Trent Kyono, James Jordon, Mihaela van der Schaar. aistats 2023: 3568-3590 [doi]

Evaluating the Robustness of Interpretability Methods through Explanation Invariance and EquivarianceJonathan Crabbé, Mihaela van der Schaar. nips 2023: [doi]

In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect EstimationAlicia Curth, Mihaela van der Schaar. icml 2023: 6623-6642 [doi]

A U-turn on Double Descent: Rethinking Parameter Counting in Statistical LearningAlicia Curth, Alan Jeffares, Mihaela van der Schaar. nips 2023: [doi]

Understanding the Impact of Competing Events on Heterogeneous Treatment Effect Estimation from Time-to-Event DataAlicia Curth, Mihaela van der Schaar. aistats 2023: 7961-7980 [doi]

Adaptive Identification of Populations with Treatment Benefit in Clinical Trials: Machine Learning Challenges and SolutionsAlicia Curth, Alihan Hüyük, Mihaela van der Schaar. icml 2023: 6603-6622 [doi]

Reimagining Synthetic Tabular Data Generation through Data-Centric AI: A Comprehensive BenchmarkLasse Hansen, Nabeel Seedat, Mihaela van der Schaar, Andrija Petrovic. nips 2023: [doi]

Deep Generative Symbolic RegressionSamuel Holt, Zhaozhi Qian, Mihaela van der Schaar. iclr 2023: [doi]

Active Observing in Continuous-time ControlSamuel Holt, Alihan Hüyük, Mihaela van der Schaar. nips 2023: [doi]

Neural Laplace Control for Continuous-time Delayed SystemsSamuel Holt, Alihan Hüyük, Zhaozhi Qian, Hao Sun, Mihaela van der Schaar. aistats 2023: 1747-1778 [doi]

When to Make and Break Commitments?Alihan Hüyük, Zhaozhi Qian, Mihaela van der Schaar. iclr 2023: [doi]

Multiple stakeholders drive diverse interpretability requirements for machine learning in healthcareFergus Imrie, Robert Davis, Mihaela van der Schaar. natmi, 5(8):824-829, August 2023. [doi]

TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and SpecializationAlan Jeffares, Tennison Liu, Jonathan Crabbé, Fergus Imrie, Mihaela van der Schaar. iclr 2023: [doi]

Joint Training of Deep Ensembles Fails Due to Learner CollusionAlan Jeffares, Tennison Liu, Jonathan Crabbé, Mihaela van der Schaar. nips 2023: [doi]

D-CIPHER: Discovery of Closed-form Partial Differential EquationsKrzysztof Kacprzyk, Zhaozhi Qian, Mihaela van der Schaar. nips 2023: [doi]

Selecting Treatment Effects Models for Domain Adaptation Using Causal KnowledgeTrent Kyono, Ioana Bica, Zhaozhi Qian, Mihaela van der Schaar. health, 4(2), April 2023. [doi]

Learning Representations without Compositional AssumptionsTennison Liu, Jeroen Berrevoets, Zhaozhi Qian, Mihaela van der Schaar. icml 2023: 21388-21403 [doi]

GOGGLE: Generative Modelling for Tabular Data by Learning Relational StructureTennison Liu, Zhaozhi Qian, Jeroen Berrevoets, Mihaela van der Schaar. iclr 2023: [doi]

SurvivalGAN: Generating Time-to-Event Data for Survival AnalysisAlexander Norcliffe, Bogdan Cebere, Fergus Imrie, Pietro Liè, Mihaela van der Schaar. aistats 2023: 10279-10304 [doi]

Synthcity: a benchmark framework for diverse use cases of tabular synthetic dataZhaozhi Qian, Robert Davis, Mihaela van der Schaar. nips 2023: [doi]

T-Phenotype: Discovering Phenotypes of Predictive Temporal Patterns in Disease ProgressionYuchao Qin, Mihaela van der Schaar, ChangHee Lee. aistats 2023: 3466-3492 [doi]

Risk-Averse Active Sensing for Timely Outcome Prediction under Cost PressureYuchao Qin, Mihaela van der Schaar, ChangHee Lee. nips 2023: [doi]

TRIAGE: Characterizing and auditing training data for improved regressionNabeel Seedat, Jonathan Crabbé, Zhaozhi Qian, Mihaela van der Schaar. nips 2023: [doi]

Improving Adaptive Conformal Prediction Using Self-Supervised LearningNabeel Seedat, Alan Jeffares, Fergus Imrie, Mihaela van der Schaar. aistats 2023: 10160-10177 [doi]

Accountability in Offline Reinforcement Learning: Explaining Decisions with a Corpus of ExamplesHao Sun, Alihan Hüyük, Daniel Jarrett, Mihaela van der Schaar. nips 2023: [doi]

What is Flagged in Uncertainty Quantification? Latent Density Models for Uncertainty CategorizationHao Sun, Boris van Breugel, Jonathan Crabbé, Nabeel Seedat, Mihaela van der Schaar. nips 2023: [doi]

Can You Rely on Your Model Evaluation? Improving Model Evaluation with Synthetic Test DataBoris van Breugel, Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar. nips 2023: [doi]

Synthetic Data, Real Errors: How (Not) to Publish and Use Synthetic DataBoris van Breugel, Zhaozhi Qian, Mihaela van der Schaar. icml 2023: 34793-34808 [doi]

Membership Inference Attacks against Synthetic Data through Overfitting DetectionBoris van Breugel, Hao Sun, Zhaozhi Qian, Mihaela van der Schaar. aistats 2023: 3493-3514 [doi]

Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over TimeToon Vanderschueren, Alicia Curth, Wouter Verbeke, Mihaela van der Schaar. icml 2023: 34855-34874 [doi]

2022

How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative ModelsAhmed Alaa, Boris van Breugel, Evgeny S. Saveliev, Mihaela van der Schaar. icml 2022: 290-306 [doi]

Neural graphical modelling in continuous-time: consistency guarantees and algorithmsAlexis Bellot, Kim Branson, Mihaela van der Schaar. iclr 2022: [doi]

Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects EstimationIoana Bica, Mihaela van der Schaar. nips 2022: [doi]

Synthetic Model Combination: An Instance-wise Approach to Unsupervised Ensemble LearningAlex J. Chan, Mihaela van der Schaar. nips 2022: [doi]

Inverse Online Learning: Understanding Non-Stationary and Reactionary PoliciesAlex J. Chan, Alicia Curth, Mihaela van der Schaar. iclr 2022: [doi]

Concept Activation Regions: A Generalized Framework For Concept-Based ExplanationsJonathan Crabbé, Mihaela van der Schaar. nips 2022: [doi]

Benchmarking Heterogeneous Treatment Effect Models through the Lens of InterpretabilityJonathan Crabbé, Alicia Curth, Ioana Bica, Mihaela van der Schaar. nips 2022: [doi]

Label-Free Explainability for Unsupervised ModelsJonathan Crabbé, Mihaela van der Schaar. icml 2022: 4391-4420 [doi]

Neural Laplace: Learning diverse classes of differential equations in the Laplace domainSamuel Holt, Zhaozhi Qian, Mihaela van der Schaar. icml 2022: 8811-8832 [doi]

Inverse Contextual Bandits: Learning How Behavior Evolves over TimeAlihan Hüyük, Daniel Jarrett, Mihaela van der Schaar. icml 2022: 9506-9524 [doi]

Inferring Lexicographically-Ordered Rewards from PreferencesAlihan Hüyük, William R. Zame, Mihaela van der Schaar. AAAI 2022: 5737-5745 [doi]

Composite Feature Selection Using Deep EnsemblesFergus Imrie, Alexander Norcliffe, Pietro Lió, Mihaela van der Schaar. nips 2022: [doi]

HyperImpute: Generalized Iterative Imputation with Automatic Model SelectionDaniel Jarrett, Bogdan Cebere, Tennison Liu, Alicia Curth, Mihaela van der Schaar. icml 2022: 9916-9937 [doi]

Online Decision MediationDaniel Jarrett, Alihan Hüyük, Mihaela van der Schaar. nips 2022: [doi]

Self-Supervision Enhanced Feature Selection with Correlated GatesChangHee Lee, Fergus Imrie, Mihaela van der Schaar. iclr 2022: [doi]

Developing machine learning algorithms for dynamic estimation of progression during active surveillance for prostate cancerChangHee Lee, Alexander Light, Evgeny S. Saveliev, Mihaela van der Schaar, Vincent J. Gnanapragasam. npjdm, 5, 2022. [doi]

POETREE: Interpretable Policy Learning with Adaptive Decision TreesAlizée Pace, Alex Chan, Mihaela van der Schaar. iclr 2022: [doi]

D-CODE: Discovering Closed-form ODEs from Observed TrajectoriesZhaozhi Qian, Krzysztof Kacprzyk, Mihaela van der Schaar. iclr 2022: [doi]

Data-SUITE: Data-centric identification of in-distribution incongruous examplesNabeel Seedat, Jonathan Crabbé, Mihaela van der Schaar. icml 2022: 19467-19496 [doi]

Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular dataNabeel Seedat, Jonathan Crabbé, Ioana Bica, Mihaela van der Schaar. nips 2022: [doi]

Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential EquationsNabeel Seedat, Fergus Imrie, Alexis Bellot, Zhaozhi Qian, Mihaela van der Schaar. icml 2022: 19497-19521 [doi]

MARS: Assisting Human with Information Processing Tasks Using Machine LearningCong Shen, Zhaozhi Qian, Alihan Hüyük, Mihaela van der Schaar. health, 3(2), 2022. [doi]

Feedback Adaptive Learning for Medical and Educational Application RecommendationCem Tekin, Sepehr Elahi, Mihaela van der Schaar. tsc, 15(4):2144-2157, 2022. [doi]

Machine Learning for Medicine and HealthcareMihaela van der Schaar. icpram 2022: 5

Identifiable Energy-based Representations: An Application to Estimating Heterogeneous Causal EffectsYao Zhang, Jeroen Berrevoets, Mihaela van der Schaar. aistats 2022: 4158-4177 [doi]

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