Stop the war!Остановите войну!
for scientists:
default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDMihaela van der Schaar
Mihaela van der Schaar-Mitrea
Person information
- affiliation: University of Cambridge, epartment of Applied Mathematics and Theoretical Physics, UK
- affiliation: University of California, Los Angeles, Electrical Engineering Department
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2023
[j258]Trent Kyono
, Ioana Bica, Zhaozhi Qian, Mihaela van der Schaar:
Selecting Treatment Effects Models for Domain Adaptation Using Causal Knowledge. ACM Trans. Comput. Heal. 4(2): 15:1-15:29 (2023)- [j257]Mahed Abroshan, Kai Hou Yip, Cem Tekin, Mihaela van der Schaar:
Conservative Policy Construction Using Variational Autoencoders for Logged Data With Missing Values. IEEE Trans. Neural Networks Learn. Syst. 34(9): 6368-6378 (2023) - [j256]Onur Atan, Saeed Ghoorchian, Setareh Maghsudi, Mihaela van der Schaar:
Data-Driven Online Recommender Systems With Costly Information Acquisition. IEEE Trans. Serv. Comput. 16(1): 235-245 (2023) - [c362]Samuel Holt, Alihan Hüyük, Zhaozhi Qian, Hao Sun, Mihaela van der Schaar:
Neural Laplace Control for Continuous-time Delayed Systems. AISTATS 2023: 1747-1778 - [c361]Yuchao Qin, Mihaela van der Schaar, Changhee Lee:
T-Phenotype: Discovering Phenotypes of Predictive Temporal Patterns in Disease Progression. AISTATS 2023: 3466-3492 - [c360]Boris van Breugel, Hao Sun, Zhaozhi Qian, Mihaela van der Schaar:
Membership Inference Attacks against Synthetic Data through Overfitting Detection. AISTATS 2023: 3493-3514 - [c359]Jeroen Berrevoets, Fergus Imrie, Trent Kyono, James Jordon, Mihaela van der Schaar:
To Impute or not to Impute? Missing Data in Treatment Effect Estimation. AISTATS 2023: 3568-3590 - [c358]Alicia Curth, Mihaela van der Schaar:
Understanding the Impact of Competing Events on Heterogeneous Treatment Effect Estimation from Time-to-Event Data. AISTATS 2023: 7961-7980 - [c357]Nabeel Seedat, Alan Jeffares, Fergus Imrie, Mihaela van der Schaar:
Improving Adaptive Conformal Prediction Using Self-Supervised Learning. AISTATS 2023: 10160-10177 - [c356]Alexander Norcliffe, Bogdan Cebere, Fergus Imrie, Pietro Liè, Mihaela van der Schaar:
SurvivalGAN: Generating Time-to-Event Data for Survival Analysis. AISTATS 2023: 10279-10304 - [c355]Samuel Holt, Zhaozhi Qian, Mihaela van der Schaar:
Deep Generative Symbolic Regression. ICLR 2023 - [c354]Alihan Hüyük, Zhaozhi Qian, Mihaela van der Schaar:
When to Make and Break Commitments? ICLR 2023 - [c353]Alan Jeffares, Tennison Liu, Jonathan Crabbé, Fergus Imrie, Mihaela van der Schaar:
TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization. ICLR 2023 - [c352]Tennison Liu, Zhaozhi Qian, Jeroen Berrevoets, Mihaela van der Schaar:
GOGGLE: Generative Modelling for Tabular Data by Learning Relational Structure. ICLR 2023 - [c351]Jeroen Berrevoets, Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar:
Differentiable and Transportable Structure Learning. ICML 2023: 2206-2233 - [c350]Alicia Curth, Alihan Hüyük, Mihaela van der Schaar:
Adaptive Identification of Populations with Treatment Benefit in Clinical Trials: Machine Learning Challenges and Solutions. ICML 2023: 6603-6622 - [c349]Alicia Curth, Mihaela van der Schaar:
In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect Estimation. ICML 2023: 6623-6642 - [c348]Tennison Liu, Jeroen Berrevoets, Zhaozhi Qian, Mihaela van der Schaar:
Learning Representations without Compositional Assumptions. ICML 2023: 21388-21403 - [c347]Boris van Breugel, Zhaozhi Qian, Mihaela van der Schaar:
Synthetic Data, Real Errors: How (Not) to Publish and Use Synthetic Data. ICML 2023: 34793-34808 - [c346]Toon Vanderschueren, Alicia Curth, Wouter Verbeke, Mihaela van der Schaar:
Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over Time. ICML 2023: 34855-34874 - [e3]Mihaela van der Schaar, Cheng Zhang, Dominik Janzing:
Conference on Causal Learning and Reasoning, CLeaR 2023, 11-14 April 2023, Amazon Development Center, Tübingen, Germany, April 11-14, 2023. Proceedings of Machine Learning Research 213, PMLR 2023 [contents] - [i253]Zhaozhi Qian, Bogdan-Constantin Cebere, Mihaela van der Schaar:
Synthcity: facilitating innovative use cases of synthetic data in different data modalities. CoRR abs/2301.07573 (2023) - [i252]Alan Jeffares, Tennison Liu, Jonathan Crabbé, Mihaela van der Schaar:
Joint Training of Deep Ensembles Fails Due to Learner Collusion. CoRR abs/2301.11323 (2023) - [i251]Evgeny S. Saveliev, Mihaela van der Schaar:
TemporAI: Facilitating Machine Learning Innovation in Time Domain Tasks for Medicine. CoRR abs/2301.12260 (2023) - [i250]Alicia Curth, Mihaela van der Schaar:
In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect Estimation. CoRR abs/2302.02923 (2023) - [i249]Nabeel Seedat, Alan Jeffares, Fergus Imrie, Mihaela van der Schaar:
Improving Adaptive Conformal Prediction Using Self-Supervised Learning. CoRR abs/2302.12238 (2023) - [i248]Boris van Breugel, Hao Sun, Zhaozhi Qian, Mihaela van der Schaar:
Membership Inference Attacks against Synthetic Data through Overfitting Detection. CoRR abs/2302.12580 (2023) - [i247]Samuel Holt, Alihan Hüyük, Zhaozhi Qian, Hao Sun, Mihaela van der Schaar:
Neural Laplace Control for Continuous-time Delayed Systems. CoRR abs/2302.12604 (2023) - [i246]Yuchao Qin, Mihaela van der Schaar, Changhee Lee:
T-Phenotype: Discovering Phenotypes of Predictive Temporal Patterns in Disease Progression. CoRR abs/2302.12619 (2023) - [i245]Alicia Curth, Mihaela van der Schaar:
Understanding the Impact of Competing Events on Heterogeneous Treatment Effect Estimation from Time-to-Event Data. CoRR abs/2302.12718 (2023) - [i244]Alexander Norcliffe, Bogdan Cebere, Fergus Imrie, Pietro Liò, Mihaela van der Schaar:
SurvivalGAN: Generating Time-to-Event Data for Survival Analysis. CoRR abs/2302.12749 (2023) - [i243]Mahed Abroshan, Oscar Giles, Sam F. Greenbury, Jack Roberts, Mihaela van der Schaar, Jannetta S. Steyn, Alan Wilson, May Yong:
Learning machines for health and beyond. CoRR abs/2303.01513 (2023) - [i242]Jeroen Berrevoets, Krzysztof Kacprzyk, Zhaozhi Qian, Mihaela van der Schaar:
Causal Deep Learning. CoRR abs/2303.02186 (2023) - [i241]Alan Jeffares, Tennison Liu, Jonathan Crabbé, Fergus Imrie, Mihaela van der Schaar:
TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization. CoRR abs/2303.05506 (2023) - [i240]Eleonora Giunchiglia, Fergus Imrie, Mihaela van der Schaar, Thomas Lukasiewicz:
Machine Learning with Requirements: a Manifesto. CoRR abs/2304.03674 (2023) - [i239]Boris van Breugel, Mihaela van der Schaar:
Beyond Privacy: Navigating the Opportunities and Challenges of Synthetic Data. CoRR abs/2304.03722 (2023) - [i238]Jonathan Crabbé, Mihaela van der Schaar:
Evaluating the Robustness of Interpretability Methods through Explanation Invariance and Equivariance. CoRR abs/2304.06715 (2023) - [i237]Boris van Breugel, Zhaozhi Qian, Mihaela van der Schaar:
Synthetic data, real errors: how (not) to publish and use synthetic data. CoRR abs/2305.09235 (2023) - [i236]Tennison Liu, Jeroen Berrevoets, Zhaozhi Qian, Mihaela van der Schaar:
Learning Representations without Compositional Assumptions. CoRR abs/2305.19726 (2023) - [i235]Toon Vanderschueren, Alicia Curth, Wouter Verbeke, Mihaela van der Schaar:
Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over Time. CoRR abs/2306.04255 (2023) - [i234]Elisabeth R. M. Heremans, Nabeel Seedat, Bertien Buyse, Dries Testelmans, Mihaela van der Schaar, Maarten De Vos:
U-PASS: an Uncertainty-guided deep learning Pipeline for Automated Sleep Staging. CoRR abs/2306.04663 (2023) - [i233]Aleksa Bisercic, Mladen Nikolic, Mihaela van der Schaar, Boris Delibasic, Pietro Liò, Andrija Petrovic:
Interpretable Medical Diagnostics with Structured Data Extraction by Large Language Models. CoRR abs/2306.05052 (2023) - [i232]Yangming Li, Zhaozhi Qian, Mihaela van der Schaar:
Do Diffusion Models Suffer Error Propagation? Theoretical Analysis and Consistency Regularization. CoRR abs/2308.05021 (2023) - [i231]Hao Sun, Alihan Hüyük, Mihaela van der Schaar:
Query-Dependent Prompt Evaluation and Optimization with Offline Inverse RL. CoRR abs/2309.06553 (2023) - [i230]Yangming Li, Boris van Breugel, Mihaela van der Schaar:
Soft Mixture Denoising: Beyond the Expressive Bottleneck of Diffusion Models. CoRR abs/2309.14068 (2023) - [i229]Samuel Holt, Max Ruiz Luyten, Mihaela van der Schaar:
L2MAC: Large Language Model Automatic Computer for Unbounded Code Generation. CoRR abs/2310.02003 (2023) - [i228]Fergus Imrie, Paulius Rauba, Mihaela van der Schaar:
Redefining Digital Health Interfaces with Large Language Models. CoRR abs/2310.03560 (2023) - [i227]Hao Sun, Alihan Hüyük, Daniel Jarrett, Mihaela van der Schaar:
Accountability in Offline Reinforcement Learning: Explaining Decisions with a Corpus of Examples. CoRR abs/2310.07747 (2023) - [i226]Boris van Breugel, Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar:
Can You Rely on Your Model Evaluation? Improving Model Evaluation with Synthetic Test Data. CoRR abs/2310.16524 (2023) - [i225]Lasse Hansen, Nabeel Seedat, Mihaela van der Schaar, Andrija Petrovic:
Reimagining Synthetic Tabular Data Generation through Data-Centric AI: A Comprehensive Benchmark. CoRR abs/2310.16981 (2023) - [i224]Daniel Jarrett, Alihan Hüyük, Mihaela van der Schaar:
Inverse Decision Modeling: Learning Interpretable Representations of Behavior. CoRR abs/2310.18591 (2023) - [i223]Daniel Jarrett, Alihan Hüyük, Mihaela van der Schaar:
Online Decision Mediation. CoRR abs/2310.18601 (2023) - [i222]Daniel Jarrett, Jinsung Yoon, Ioana Bica, Zhaozhi Qian, Ari Ercole, Mihaela van der Schaar:
Clairvoyance: A Pipeline Toolkit for Medical Time Series. CoRR abs/2310.18688 (2023) - [i221]Nabeel Seedat, Jonathan Crabbé, Zhaozhi Qian, Mihaela van der Schaar:
TRIAGE: Characterizing and auditing training data for improved regression. CoRR abs/2310.18970 (2023) - [i220]Alicia Curth, Alan Jeffares, Mihaela van der Schaar:
A U-turn on Double Descent: Rethinking Parameter Counting in Statistical Learning. CoRR abs/2310.18988 (2023) - [i219]Alihan Hüyük, Daniel Jarrett, Mihaela van der Schaar:
Explaining by Imitating: Understanding Decisions by Interpretable Policy Learning. CoRR abs/2310.19831 (2023) - [i218]Daniel Jarrett, Ioana Bica, Mihaela van der Schaar:
Time-series Generation by Contrastive Imitation. CoRR abs/2311.01388 (2023) - [i217]Ioana Bica, Daniel Jarrett, Mihaela van der Schaar:
Invariant Causal Imitation Learning for Generalizable Policies. CoRR abs/2311.01489 (2023) - [i216]Alex J. Chan, Alihan Hüyük, Mihaela van der Schaar:
Optimising Human-AI Collaboration by Learning Convincing Explanations. CoRR abs/2311.07426 (2023) - [i215]Max Zhu, Katarzyna Kobalczyk, Andrija Petrovic, Mladen Nikolic, Mihaela van der Schaar, Boris Delibasic, Petro Liò:
Tabular Few-Shot Generalization Across Heterogeneous Feature Spaces. CoRR abs/2311.10051 (2023) - [i214]Luis Oala, Manil Maskey, Lilith Bat-Leah, Alicia Parrish, Nezihe Merve Gürel, Tzu-Sheng Kuo, Yang Liu, Rotem Dror, Danilo Brajovic, Xiaozhe Yao, Max Bartolo, William Gaviria Rojas, Ryan Hileman, Rainier Aliment, Michael W. Mahoney, Meg Risdal, Matthew Lease, Wojciech Samek, Debojyoti Dutta, Curtis G. Northcutt, Cody Coleman, Braden Hancock, Bernard Koch, Girmaw Abebe Tadesse, Bojan Karlas, Ahmed Alaa, Adji Bousso Dieng, Natasha F. Noy, Vijay Janapa Reddi, James Zou, Praveen K. Paritosh, Mihaela van der Schaar, Kurt D. Bollacker, Lora Aroyo, Ce Zhang, Joaquin Vanschoren, Isabelle Guyon, Peter Mattson:
DMLR: Data-centric Machine Learning Research - Past, Present and Future. CoRR abs/2311.13028 (2023) - [i213]Hao Sun, Alex J. Chan, Nabeel Seedat, Alihan Hüyük, Mihaela van der Schaar:
When is Off-Policy Evaluation Useful? A Data-Centric Perspective. CoRR abs/2311.14110 (2023) - 2022
- [j255]Cong Shen, Zhaozhi Qian, Alihan Hüyük, Mihaela van der Schaar:
MARS: Assisting Human with Information Processing Tasks Using Machine Learning. ACM Trans. Comput. Heal. 3(2): 21:1-21:19 (2022) - [j254]Iacopo Vagliano, Sylvia Brinkman, Ameen Abu-Hanna, M. Sesmu Arbous, Dave A. Dongelmans, Paul W. G. Elbers, Dylan W. De Lange, Mihaela van der Schaar, Nicolette F. de Keizer, Martijn C. Schut:
Can we reliably automate clinical prognostic modelling? A retrospective cohort study for ICU triage prediction of in-hospital mortality of COVID-19 patients in the Netherlands. Int. J. Medical Informatics 160: 104688 (2022) - [j253]Changhee Lee, Alexander Light, Evgeny S. Saveliev, Mihaela van der Schaar, Vincent J. Gnanapragasam:
Developing machine learning algorithms for dynamic estimation of progression during active surveillance for prostate cancer. npj Digit. Medicine 5 (2022) - [j252]Cem Tekin, Sepehr Elahi, Mihaela van der Schaar:
Feedback Adaptive Learning for Medical and Educational Application Recommendation. IEEE Trans. Serv. Comput. 15(4): 2144-2157 (2022) - [c345]Alihan Hüyük, William R. Zame, Mihaela van der Schaar:
Inferring Lexicographically-Ordered Rewards from Preferences. AAAI 2022: 5737-5745 - [c344]Yao Zhang, Jeroen Berrevoets, Mihaela van der Schaar:
Identifiable Energy-based Representations: An Application to Estimating Heterogeneous Causal Effects. AISTATS 2022: 4158-4177 - [c343]Alexis Bellot, Kim Branson, Mihaela van der Schaar:
Neural graphical modelling in continuous-time: consistency guarantees and algorithms. ICLR 2022 - [c342]Alex J. Chan, Alicia Curth, Mihaela van der Schaar:
Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies. ICLR 2022 - [c341]Changhee Lee, Fergus Imrie, Mihaela van der Schaar:
Self-Supervision Enhanced Feature Selection with Correlated Gates. ICLR 2022 - [c340]Alizée Pace, Alex J. Chan, Mihaela van der Schaar:
POETREE: Interpretable Policy Learning with Adaptive Decision Trees. ICLR 2022 - [c339]Zhaozhi Qian, Krzysztof Kacprzyk, Mihaela van der Schaar:
D-CODE: Discovering Closed-form ODEs from Observed Trajectories. ICLR 2022 - [c338]Ahmed M. Alaa, Boris van Breugel, Evgeny S. Saveliev, Mihaela van der Schaar:
How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models. ICML 2022: 290-306 - [c337]Jonathan Crabbé, Mihaela van der Schaar:
Label-Free Explainability for Unsupervised Models. ICML 2022: 4391-4420 - [c336]Samuel Holt, Zhaozhi Qian, Mihaela van der Schaar:
Neural Laplace: Learning diverse classes of differential equations in the Laplace domain. ICML 2022: 8811-8832 - [c335]Alihan Hüyük, Daniel Jarrett, Mihaela van der Schaar:
Inverse Contextual Bandits: Learning How Behavior Evolves over Time. ICML 2022: 9506-9524 - [c334]Daniel Jarrett, Bogdan Cebere, Tennison Liu, Alicia Curth, Mihaela van der Schaar:
HyperImpute: Generalized Iterative Imputation with Automatic Model Selection. ICML 2022: 9916-9937 - [c333]Nabeel Seedat, Jonathan Crabbé, Mihaela van der Schaar:
Data-SUITE: Data-centric identification of in-distribution incongruous examples. ICML 2022: 19467-19496 - [c332]Nabeel Seedat, Fergus Imrie, Alexis Bellot, Zhaozhi Qian, Mihaela van der Schaar:
Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations. ICML 2022: 19497-19521 - [c331]Mihaela van der Schaar:
Machine Learning for Medicine and Healthcare. ICPRAM 2022: 5 - [c330]Ioana Bica, Mihaela van der Schaar:
Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects Estimation. NeurIPS 2022 - [c329]Alex J. Chan, Mihaela van der Schaar:
Synthetic Model Combination: An Instance-wise Approach to Unsupervised Ensemble Learning. NeurIPS 2022 - [c328]Jonathan Crabbé, Alicia Curth, Ioana Bica, Mihaela van der Schaar:
Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability. NeurIPS 2022 - [c327]Jonathan Crabbé, Mihaela van der Schaar:
Concept Activation Regions: A Generalized Framework For Concept-Based Explanations. NeurIPS 2022 - [c326]Fergus Imrie, Alexander Norcliffe, Pietro Lió, Mihaela van der Schaar:
Composite Feature Selection Using Deep Ensembles. NeurIPS 2022 - [c325]Daniel Jarrett, Alihan Hüyük, Mihaela van der Schaar:
Online Decision Mediation. NeurIPS 2022 - [c324]Nabeel Seedat, Jonathan Crabbé, Ioana Bica, Mihaela van der Schaar:
Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular data. NeurIPS 2022 - [e2]Isabelle Guyon, Marius Lindauer, Mihaela van der Schaar, Frank Hutter, Roman Garnett:
International Conference on Automated Machine Learning, AutoML 2022, 25-27 July 2022, Johns Hopkins University, Baltimore, MD, USA. Proceedings of Machine Learning Research 188, PMLR 2022 [contents] - [i212]Jeroen Berrevoets, Fergus Imrie, Trent Kyono, James Jordon, Mihaela van der Schaar:
To Impute or not to Impute? - Missing Data in Treatment Effect Estimation. CoRR abs/2202.02096 (2022) - [i211]Nabeel Seedat, Jonathan Crabbé, Mihaela van der Schaar:
Data-SUITE: Data-centric identification of in-distribution incongruous examples. CoRR abs/2202.08836 (2022) - [i210]Alihan Hüyük, William R. Zame, Mihaela van der Schaar:
Inferring Lexicographically-Ordered Rewards from Preferences. CoRR abs/2202.10153 (2022) - [i209]Tobias Hatt, Jeroen Berrevoets, Alicia Curth, Stefan Feuerriegel, Mihaela van der Schaar:
Combining Observational and Randomized Data for Estimating Heterogeneous Treatment Effects. CoRR abs/2202.12891 (2022) - [i208]Jonathan Crabbé, Mihaela van der Schaar:
Label-Free Explainability for Unsupervised Models. CoRR abs/2203.01928 (2022) - [i207]Alex J. Chan, Alicia Curth, Mihaela van der Schaar:
Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies. CoRR abs/2203.07338 (2022) - [i206]Alizée Pace, Alex J. Chan, Mihaela van der Schaar:
POETREE: Interpretable Policy Learning with Adaptive Decision Trees. CoRR abs/2203.08057 (2022) - [i205]Samuel Holt, Zhaozhi Qian, Mihaela van der Schaar:
Neural Laplace: Learning diverse classes of differential equations in the Laplace domain. CoRR abs/2206.04843 (2022) - [i204]Jeroen Berrevoets, Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar:
Differentiable and Transportable Structure Learning. CoRR abs/2206.06354 (2022) - [i203]Daniel Jarrett, Bogdan Cebere, Tennison Liu, Alicia Curth, Mihaela van der Schaar:
HyperImpute: Generalized Iterative Imputation with Automatic Model Selection. CoRR abs/2206.07769 (2022) - [i202]Nabeel Seedat, Fergus Imrie, Alexis Bellot, Zhaozhi Qian, Mihaela van der Schaar:
Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations. CoRR abs/2206.08311 (2022) - [i201]Jonathan Crabbé, Alicia Curth, Ioana Bica, Mihaela van der Schaar:
Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability. CoRR abs/2206.08363 (2022) - [i200]Krzysztof Kacprzyk, Zhaozhi Qian, Mihaela van der Schaar:
D-CIPHER: Discovery of Closed-form PDEs. CoRR abs/2206.10586 (2022) - [i199]Hao Sun, Boris van Breugel, Jonathan Crabbé, Nabeel Seedat, Mihaela van der Schaar:
DAUX: a Density-based Approach for Uncertainty eXplanations. CoRR abs/2207.05161 (2022) - [i198]Yanke Li, Tobias Hatt, Ioana Bica, Mihaela van der Schaar:
DAPDAG: Domain Adaptation via Perturbed DAG Reconstruction. CoRR abs/2208.01373 (2022) - [i197]Alicia Curth, Alihan Hüyük, Mihaela van der Schaar:
Adaptively Identifying Patient Populations With Treatment Benefit in Clinical Trials. CoRR abs/2208.05844 (2022) - [i196]Jonathan Crabbé, Mihaela van der Schaar:
Concept Activation Regions: A Generalized Framework For Concept-Based Explanations. CoRR abs/2209.11222 (2022) - [i195]Alex J. Chan, Mihaela van der Schaar:
Synthetic Model Combination: An Instance-wise Approach to Unsupervised Ensemble Learning. CoRR abs/2210.05320 (2022) - [i194]Ioana Bica, Mihaela van der Schaar:
Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects Estimation. CoRR abs/2210.06183 (2022) - [i193]Fergus Imrie, Bogdan Cebere, Eoin F. McKinney, Mihaela van der Schaar:
AutoPrognosis 2.0: Democratizing Diagnostic and Prognostic Modeling in Healthcare with Automated Machine Learning. CoRR abs/2210.12090 (2022) - [i192]Nabeel Seedat, Jonathan Crabbé, Ioana Bica, Mihaela van der Schaar:
Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular data. CoRR abs/2210.13043 (2022) - [i191]Fergus Imrie, Alexander Norcliffe, Pietro Liò, Mihaela van der Schaar:
Composite Feature Selection using Deep Ensembles. CoRR abs/2211.00631 (2022) - [i190]Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar:
DC-Check: A Data-Centric AI checklist to guide the development of reliable machine learning systems. CoRR abs/2211.05764 (2022) - [i189]Tennison Liu, Alex J. Chan, Boris van Breugel, Mihaela van der Schaar:
Practical Approaches for Fair Learning with Multitype and Multivariate Sensitive Attributes. CoRR abs/2211.06138 (2022) - [i188]Jeroen Berrevoets, Krzysztof Kacprzyk, Zhaozhi Qian, Mihaela van der Schaar:
Navigating causal deep learning. CoRR abs/2212.00911 (2022) - 2021
- [j251]Trent Kyono, Fiona J. Gilbert, Mihaela van der Schaar:
Triage of 2D Mammographic Images Using Multi-view Multi-task Convolutional Neural Networks. ACM Trans. Comput. Heal. 2(3): 26:1-26:24 (2021) - [j250]Mihaela van der Schaar, Ahmed M. Alaa, R. Andres Floto, Alexander Gimson, Stefan Scholtes, Angela M. Wood, Eoin F. McKinney, Daniel Jarrett, Pietro Lió, Ari Ercole:
How artificial intelligence and machine learning can help healthcare systems respond to COVID-19.