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Mihaela van der Schaar
Mihaela van der Schaar-Mitrea
Person information
- affiliation: University of Cambridge, Department of Applied Mathematics and Theoretical Physics, UK
- affiliation: University of California, Los Angeles, Electrical Engineering Department, CA, USA
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2020 – today
- 2024
- [j264]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. Comput. Biol. Medicine 171: 108205 (2024) - [j263]Jeroen Berrevoets, Krzysztof Kacprzyk, Zhaozhi Qian, Mihaela van der Schaar:
Causal Deep Learning: Encouraging Impact on Real-world Problems Through Causality. Found. Trends Signal Process. 18(3): 200-309 (2024) - [j262]Lea Goetz, Nabeel Seedat
, Robert Vandersluis, Mihaela van der Schaar:
Generalization - a key challenge for responsible AI in patient-facing clinical applications. npj Digit. Medicine 7(1) (2024) - [j261]Nabeel Seedat
, Fergus Imrie
, Mihaela van der Schaar
:
Navigating Data-Centric Artificial Intelligence With DC-Check: Advances, Challenges, and Opportunities. IEEE Trans. Artif. Intell. 5(6): 2589-2603 (2024) - [j260]Alexis Bellot
, Mihaela van der Schaar:
Linear Deconfounded Score Method: Scoring DAGs With Dense Unobserved Confounding. IEEE Trans. Neural Networks Learn. Syst. 35(4): 4948-4962 (2024) - [c396]Alihan Hüyük, Zhaozhi Qian, Mihaela van der Schaar:
Adaptive Experiment Design with Synthetic Controls. AISTATS 2024: 1180-1188 - [c395]Nicolas Huynh, Jeroen Berrevoets, Nabeel Seedat, Jonathan Crabbé, Zhaozhi Qian, Mihaela van der Schaar:
DAGnosis: Localized Identification of Data Inconsistencies using Structures. AISTATS 2024: 1864-1872 - [c394]Krzysztof Kacprzyk, Mihaela van der Schaar:
Shape Arithmetic Expressions: Advancing Scientific Discovery Beyond Closed-Form Equations. AISTATS 2024: 3601-3609 - [c393]Zahra Atashgahi, Tennison Liu, Mykola Pechenizkiy, Raymond N. J. Veldhuis, Decebal Constantin Mocanu, Mihaela van der Schaar:
Unveiling the Power of Sparse Neural Networks for Feature Selection. ECAI 2024: 2669-2676 - [c392]Dennis Frauen, Fergus Imrie, Alicia Curth, Valentyn Melnychuk, Stefan Feuerriegel, Mihaela van der Schaar:
A Neural Framework for Generalized Causal Sensitivity Analysis. ICLR 2024 - [c391]Samuel Holt, Max Ruiz Luyten, Mihaela van der Schaar:
L2MAC: Large Language Model Automatic Computer for Extensive Code Generation. ICLR 2024 - [c390]Alihan Hüyük, Qiyao Wei, Alicia Curth, Mihaela van der Schaar:
Defining Expertise: Applications to Treatment Effect Estimation. ICLR 2024 - [c389]Krzysztof Kacprzyk, Samuel Holt, Jeroen Berrevoets
, Zhaozhi Qian, Mihaela van der Schaar:
ODE Discovery for Longitudinal Heterogeneous Treatment Effects Inference. ICLR 2024 - [c388]Krzysztof Kacprzyk, Tennison Liu, Mihaela van der Schaar:
Towards Transparent Time Series Forecasting. ICLR 2024 - [c387]Yangming Li, Boris van Breugel, Mihaela van der Schaar:
Soft Mixture Denoising: Beyond the Expressive Bottleneck of Diffusion Models. ICLR 2024 - [c386]Yangming Li, Mihaela van der Schaar:
On Error Propagation of Diffusion Models. ICLR 2024 - [c385]Tennison Liu, Nicolás Astorga, Nabeel Seedat, Mihaela van der Schaar:
Large Language Models to Enhance Bayesian Optimization. ICLR 2024 - [c384]Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar:
Dissecting Sample Hardness: A Fine-Grained Analysis of Hardness Characterization Methods for Data-Centric AI. ICLR 2024 - [c383]Hao Sun, Alihan Hüyük, Mihaela van der Schaar:
Query-Dependent Prompt Evaluation and Optimization with Offline Inverse RL. ICLR 2024 - [c382]Boris van Breugel, Mihaela van der Schaar:
Position: Why Tabular Foundation Models Should Be a Research Priority. ICML 2024 - [c381]Alex James Chan, Hao Sun, Samuel Holt, Mihaela van der Schaar:
Dense Reward for Free in Reinforcement Learning from Human Feedback. ICML 2024 - [c380]Jonathan Crabbé, Nicolas Huynh, Jan Stanczuk, Mihaela van der Schaar:
Time Series Diffusion in the Frequency Domain. ICML 2024 - [c379]Chohee Kim, Mihaela van der Schaar, Changhee Lee:
Discovering Features with Synergistic Interactions in Multiple Views. ICML 2024 - [c378]Thomas Pouplin, Alan Jeffares, Nabeel Seedat, Mihaela van der Schaar:
Relaxed Quantile Regression: Prediction Intervals for Asymmetric Noise. ICML 2024 - [c377]Jonas Schweisthal, Dennis Frauen, Mihaela van der Schaar, Stefan Feuerriegel:
Meta-Learners for Partially-Identified Treatment Effects Across Multiple Environments. ICML 2024 - [c376]Nabeel Seedat, Nicolas Huynh, Boris van Breugel, Mihaela van der Schaar:
Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in low-data regimes. ICML 2024 - [i294]Samuel Holt, Zhaozhi Qian, Mihaela van der Schaar:
Deep Generative Symbolic Regression. CoRR abs/2401.00282 (2024) - [i293]Alihan Hüyük, Zhaozhi Qian, Mihaela van der Schaar:
Adaptive Experiment Design with Synthetic Controls. CoRR abs/2401.17205 (2024) - [i292]Alex J. Chan, Hao Sun, Samuel Holt, Mihaela van der Schaar:
Dense Reward for Free in Reinforcement Learning from Human Feedback. CoRR abs/2402.00782 (2024) - [i291]Alicia Curth, Alan Jeffares, Mihaela van der Schaar:
Why do Random Forests Work? Understanding Tree Ensembles as Self-Regularizing Adaptive Smoothers. CoRR abs/2402.01502 (2024) - [i290]Yangming Li, Max Ruiz Luyten, Mihaela van der Schaar:
Risk-Sensitive Diffusion: Learning the Underlying Distribution from Noisy Samples. CoRR abs/2402.02081 (2024) - [i289]Tennison Liu, Nicolás Astorga, Nabeel Seedat, Mihaela van der Schaar:
Large Language Models to Enhance Bayesian Optimization. CoRR abs/2402.03921 (2024) - [i288]Jonathan Crabbé, Nicolas Huynh, Jan Stanczuk, Mihaela van der Schaar:
Time Series Diffusion in the Frequency Domain. CoRR abs/2402.05933 (2024) - [i287]Thomas Pouplin, Hao Sun, Samuel Holt, Mihaela van der Schaar:
Retrieval-Augmented Thought Process as Sequential Decision Making. CoRR abs/2402.07812 (2024) - [i286]Katarzyna Kobalczyk
, Mihaela van der Schaar:
Informed Meta-Learning. CoRR abs/2402.16105 (2024) - [i285]Nicolas Huynh, Jeroen Berrevoets, Nabeel Seedat, Jonathan Crabbé, Zhaozhi Qian, Mihaela van der Schaar:
DAGnosis: Localized Identification of Data Inconsistencies using Structures. CoRR abs/2402.17599 (2024) - [i284]Alihan Hüyük, Qiyao Wei, Alicia Curth, Mihaela van der Schaar:
Defining Expertise: Applications to Treatment Effect Estimation. CoRR abs/2403.00694 (2024) - [i283]Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar:
Dissecting Sample Hardness: A Fine-Grained Analysis of Hardness Characterization Methods for Data-Centric AI. CoRR abs/2403.04551 (2024) - [i282]Krzysztof Kacprzyk, Samuel Holt, Jeroen Berrevoets, Zhaozhi Qian, Mihaela van der Schaar:
ODE Discovery for Longitudinal Heterogeneous Treatment Effects Inference. CoRR abs/2403.10766 (2024) - [i281]Krzysztof Kacprzyk, Mihaela van der Schaar:
Shape Arithmetic Expressions: Advancing Scientific Discovery Beyond Closed-Form Equations. CoRR abs/2404.09788 (2024) - [i280]Boris van Breugel, Mihaela van der Schaar:
Why Tabular Foundation Models Should Be a Research Priority. CoRR abs/2405.01147 (2024) - [i279]Yangming Li, Mihaela van der Schaar:
A Study of Posterior Stability for Time-Series Latent Diffusion. CoRR abs/2405.14021 (2024) - [i278]Hao Sun, Mihaela van der Schaar:
Inverse-RLignment: Inverse Reinforcement Learning from Demonstrations for LLM Alignment. CoRR abs/2405.15624 (2024) - [i277]Jonas Schweisthal, Dennis Frauen, Mihaela van der Schaar, Stefan Feuerriegel:
Meta-Learners for Partially-Identified Treatment Effects Across Multiple Environments. CoRR abs/2406.02464 (2024) - [i276]Thomas Pouplin, Alan Jeffares, Nabeel Seedat, Mihaela van der Schaar:
Relaxed Quantile Regression: Prediction Intervals for Asymmetric Noise. CoRR abs/2406.03258 (2024) - [i275]Chris Lu, Samuel Holt, Claudio Fanconi, Alex J. Chan, Jakob N. Foerster, Mihaela van der Schaar, Robert Tjarko Lange:
Discovering Preference Optimization Algorithms with and for Large Language Models. CoRR abs/2406.08414 (2024) - [i274]Nabeel Seedat, Nicolas Huynh, Fergus Imrie, Mihaela van der Schaar:
You can't handle the (dirty) truth: Data-centric insights improve pseudo-labeling. CoRR abs/2406.13733 (2024) - [i273]Boris van Breugel, Jonathan Crabbé, Rob Davis, Mihaela van der Schaar:
LaTable: Towards Large Tabular Models. CoRR abs/2406.17673 (2024) - [i272]Fergus Imrie, Stefan Denner, Lucas S. Brunschwig, Klaus H. Maier-Hein, Mihaela van der Schaar:
Automated Ensemble Multimodal Machine Learning for Healthcare. CoRR abs/2407.18227 (2024) - [i271]Alexander Bastounis, Paolo Campodonico, Mihaela van der Schaar, Ben Adcock, Anders C. Hansen:
On the consistent reasoning paradox of intelligence and optimal trust in AI: The power of 'I don't know'. CoRR abs/2408.02357 (2024) - [i270]Zahra Atashgahi, Tennison Liu, Mykola Pechenizkiy, Raymond N. J. Veldhuis, Decebal Constantin Mocanu, Mihaela van der Schaar:
Unveiling the Power of Sparse Neural Networks for Feature Selection. CoRR abs/2408.04583 (2024) - [i269]Evgeny Saveliev, Tim Schubert, Thomas Pouplin, Vasilis Kosmoliaptsis, Mihaela van der Schaar:
CliMB: An AI-enabled Partner for Clinical Predictive Modeling. CoRR abs/2410.03736 (2024) - [i268]Stefan Feuerriegel, Dennis Frauen, Valentyn Melnychuk, Jonas Schweisthal, Konstantin Hess, Alicia Curth, Stefan Bauer, Niki Kilbertus, Isaac S. Kohane, Mihaela van der Schaar:
Causal machine learning for predicting treatment outcomes. CoRR abs/2410.08770 (2024) - [i267]Samuel Holt, Tennison Liu, Mihaela van der Schaar:
Automatically Learning Hybrid Digital Twins of Dynamical Systems. CoRR abs/2410.23691 (2024) - [i266]Paulius Rauba, Nabeel Seedat, Max Ruiz Luyten, Mihaela van der Schaar:
Context-Aware Testing: A New Paradigm for Model Testing with Large Language Models. CoRR abs/2410.24005 (2024) - [i265]Nabeel Seedat, Mihaela van der Schaar:
Matchmaker: Self-Improving Large Language Model Programs for Schema Matching. CoRR abs/2410.24105 (2024) - [i264]Paulius Rauba, Nabeel Seedat, Krzysztof Kacprzyk, Mihaela van der Schaar:
Self-Healing Machine Learning: A Framework for Autonomous Adaptation in Real-World Environments. CoRR abs/2411.00186 (2024) - [i263]Alan Jeffares, Alicia Curth, Mihaela van der Schaar:
Deep Learning Through A Telescoping Lens: A Simple Model Provides Empirical Insights On Grokking, Gradient Boosting & Beyond. CoRR abs/2411.00247 (2024) - [i262]Nicolás Astorga, Tennison Liu, Yuanzhang Xiao, Mihaela van der Schaar:
Autoformulation of Mathematical Optimization Models Using LLMs. CoRR abs/2411.01679 (2024) - [i261]Valentyn Melnychuk, Stefan Feuerriegel, Mihaela van der Schaar:
Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learner. CoRR abs/2411.03387 (2024) - [i260]Nabeel Seedat, Caterina Tozzi, Andrea Hita Ardiaca, Mihaela van der Schaar, James Weatherall, Adam Taylor:
Unlocking Historical Clinical Trial Data with ALIGN: A Compositional Large Language Model System for Medical Coding. CoRR abs/2411.13163 (2024) - [i259]Paulius Rauba, Qiyao Wei, Mihaela van der Schaar:
Quantifying perturbation impacts for large language models. CoRR abs/2412.00868 (2024) - [i258]Thomas Pouplin, Katarzyna Kobalczyk, Hao Sun, Mihaela van der Schaar:
LLMs for Generalizable Language-Conditioned Policy Learning under Minimal Data Requirements. CoRR abs/2412.06877 (2024) - [i257]Katarzyna Kobalczyk, Claudio Fanconi, Hao Sun, Mihaela van der Schaar:
Few-shot Steerable Alignment: Adapting Rewards and LLM Policies with Neural Processes. CoRR abs/2412.13998 (2024) - 2023
- [j259]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) - [j258]Fergus Imrie
, Robert Davis, Mihaela van der Schaar:
Multiple stakeholders drive diverse interpretability requirements for machine learning in healthcare. Nat. Mac. Intell. 5(8): 824-829 (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) - [c375]Samuel Holt, Alihan Hüyük, Zhaozhi Qian, Hao Sun, Mihaela van der Schaar:
Neural Laplace Control for Continuous-time Delayed Systems. AISTATS 2023: 1747-1778 - [c374]Yuchao Qin, Mihaela van der Schaar, Changhee Lee:
T-Phenotype: Discovering Phenotypes of Predictive Temporal Patterns in Disease Progression. AISTATS 2023: 3466-3492 - [c373]Boris van Breugel, Hao Sun, Zhaozhi Qian, Mihaela van der Schaar:
Membership Inference Attacks against Synthetic Data through Overfitting Detection. AISTATS 2023: 3493-3514 - [c372]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 - [c371]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 - [c370]Nabeel Seedat, Alan Jeffares, Fergus Imrie, Mihaela van der Schaar:
Improving Adaptive Conformal Prediction Using Self-Supervised Learning. AISTATS 2023: 10160-10177 - [c369]Alexander Norcliffe, Bogdan Cebere, Fergus Imrie, Pietro Liò, Mihaela van der Schaar:
SurvivalGAN: Generating Time-to-Event Data for Survival Analysis. AISTATS 2023: 10279-10304 - [c368]Samuel Holt, Zhaozhi Qian, Mihaela van der Schaar:
Deep Generative Symbolic Regression. ICLR 2023 - [c367]Alihan Hüyük, Zhaozhi Qian, Mihaela van der Schaar:
When to Make and Break Commitments? ICLR 2023 - [c366]Alan Jeffares, Tennison Liu, Jonathan Crabbé, Fergus Imrie, Mihaela van der Schaar:
TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization. ICLR 2023 - [c365]Tennison Liu, Zhaozhi Qian, Jeroen Berrevoets
, Mihaela van der Schaar:
GOGGLE: Generative Modelling for Tabular Data by Learning Relational Structure. ICLR 2023 - [c364]Jeroen Berrevoets
, Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar:
Differentiable and Transportable Structure Learning. ICML 2023: 2206-2233 - [c363]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 - [c362]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 - [c361]Tennison Liu, Jeroen Berrevoets
, Zhaozhi Qian, Mihaela van der Schaar:
Learning Representations without Compositional Assumptions. ICML 2023: 21388-21403 - [c360]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 - [c359]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 - [c358]Jeroen Berrevoets, Daniel Jarrett, Alex J. Chan, Mihaela van der Schaar:
AllSim: Simulating and Benchmarking Resource Allocation Policies in Multi-User Systems. NeurIPS 2023 - [c357]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. NeurIPS 2023 - [c356]Jonathan Crabbé, Mihaela van der Schaar:
Evaluating the Robustness of Interpretability Methods through Explanation Invariance and Equivariance. NeurIPS 2023 - [c355]Alicia Curth, Alan Jeffares, Mihaela van der Schaar:
A U-turn on Double Descent: Rethinking Parameter Counting in Statistical Learning. NeurIPS 2023 - [c354]Lasse Hansen, Nabeel Seedat, Mihaela van der Schaar, Andrija Petrovic:
Reimagining Synthetic Tabular Data Generation through Data-Centric AI: A Comprehensive Benchmark. NeurIPS 2023 - [c353]Samuel Holt, Alihan Hüyük, Mihaela van der Schaar:
Active Observing in Continuous-time Control. NeurIPS 2023 - [c352]Alan Jeffares, Tennison Liu, Jonathan Crabbé, Mihaela van der Schaar:
Joint Training of Deep Ensembles Fails Due to Learner Collusion. NeurIPS 2023 - [c351]Krzysztof Kacprzyk, Zhaozhi Qian, Mihaela van der Schaar:
D-CIPHER: Discovery of Closed-form Partial Differential Equations. NeurIPS 2023 - [c350]Zhaozhi Qian, Robert Davis, Mihaela van der Schaar:
Synthcity: a benchmark framework for diverse use cases of tabular synthetic data. NeurIPS 2023 - [c349]Yuchao Qin, Mihaela van der Schaar, Changhee Lee:
Risk-Averse Active Sensing for Timely Outcome Prediction under Cost Pressure. NeurIPS 2023 - [c348]Nabeel Seedat, Jonathan Crabbé, Zhaozhi Qian, Mihaela van der Schaar:
TRIAGE: Characterizing and auditing training data for improved regression. NeurIPS 2023 - [c347]Hao Sun, Boris van Breugel, Jonathan Crabbé, Nabeel Seedat, Mihaela van der Schaar:
What is Flagged in Uncertainty Quantification? Latent Density Models for Uncertainty Categorization. NeurIPS 2023 - [c346]Hao Sun, Alihan Hüyük, Daniel Jarrett, Mihaela van der Schaar:
Accountability in Offline Reinforcement Learning: Explaining Decisions with a Corpus of Examples. NeurIPS 2023 - [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] - [i256]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) - [i255]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) - [i254]Evgeny S. Saveliev, Mihaela van der Schaar:
TemporAI: Facilitating Machine Learning Innovation in Time Domain Tasks for Medicine. CoRR abs/2301.12260 (2023) - [i253]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) - [i252]Nabeel Seedat, Alan Jeffares, Fergus Imrie, Mihaela van der Schaar:
Improving Adaptive Conformal Prediction Using Self-Supervised Learning. CoRR abs/2302.12238 (2023) - [i251]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) - [i250]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) - [i249]Yuchao Qin, Mihaela van der Schaar, Changhee Lee
:
T-Phenotype: Discovering Phenotypes of Predictive Temporal Patterns in Disease Progression. CoRR abs/2302.12619 (2023) - [i248]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) - [i247]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) - [i246]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) - [i245]Jeroen Berrevoets, Krzysztof Kacprzyk, Zhaozhi Qian, Mihaela van der Schaar:
Causal Deep Learning. CoRR abs/2303.02186 (2023) - [i244]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) - [i243]Eleonora Giunchiglia, Fergus Imrie, Mihaela van der Schaar, Thomas Lukasiewicz:
Machine Learning with Requirements: a Manifesto. CoRR abs/2304.03674 (2023) - [i242]Boris van Breugel, Mihaela van der Schaar:
Beyond Privacy: Navigating the Opportunities and Challenges of Synthetic Data. CoRR abs/2304.03722 (2023) - [i241]Jonathan Crabbé, Mihaela van der Schaar:
Evaluating the Robustness of Interpretability Methods through Explanation Invariance and Equivariance. CoRR abs/2304.06715 (2023) - [i240]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) - [i239]Tennison Liu, Jeroen Berrevoets, Zhaozhi Qian, Mihaela van der Schaar:
Learning Representations without Compositional Assumptions. CoRR abs/2305.19726 (2023) - [i238]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) - [i237]