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  1. AU="Qian, Zhaozhi"
  2. AU="Dangis, Glenda"
  3. AU="Takei, Masami"
  4. AU="Riemann, Conrad"
  5. AU="Meyer, Michel"
  6. AU="Gert Schaart"
  7. AU="Caputo, Christina R"
  8. AU="Dupré, Caroline"
  9. AU="Eelco van Duinkerken"
  10. AU="Brumm, Henrik"
  11. AU=Kalligeros Markos
  12. AU="Oberholzer, Michael"
  13. AU="Phil B. Tsai"
  14. AU="Tallent, Melanie K"
  15. AU="Bajolle, Fanny"
  16. AU="El-Shafie, Sittana S"
  17. AU="Kammili, Nagamani"
  18. AU=Harholt Jesper

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Treffer 1 - 10 von insgesamt 28

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  1. Buch ; Online: Adaptive Experiment Design with Synthetic Controls

    Hüyük, Alihan / Qian, Zhaozhi / van der Schaar, Mihaela

    2024  

    Abstract: Clinical trials are typically run in order to understand the effects of a new treatment on a given population of patients. However, patients in large populations rarely respond the same way to the same treatment. This heterogeneity in patient responses ... ...

    Abstract Clinical trials are typically run in order to understand the effects of a new treatment on a given population of patients. However, patients in large populations rarely respond the same way to the same treatment. This heterogeneity in patient responses necessitates trials that investigate effects on multiple subpopulations - especially when a treatment has marginal or no benefit for the overall population but might have significant benefit for a particular subpopulation. Motivated by this need, we propose Syntax, an exploratory trial design that identifies subpopulations with positive treatment effect among many subpopulations. Syntax is sample efficient as it (i) recruits and allocates patients adaptively and (ii) estimates treatment effects by forming synthetic controls for each subpopulation that combines control samples from other subpopulations. We validate the performance of Syntax and provide insights into when it might have an advantage over conventional trial designs through experiments.

    Comment: Proceedings of the 27th International Conference on Artificial Intelligence and Statistics
    Schlagwörter Statistics - Machine Learning ; Computer Science - Machine Learning
    Thema/Rubrik (Code) 310
    Erscheinungsdatum 2024-01-30
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    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  2. Buch ; Online: Deep Generative Symbolic Regression

    Holt, Samuel / Qian, Zhaozhi / van der Schaar, Mihaela

    2023  

    Abstract: Symbolic regression (SR) aims to discover concise closed-form mathematical equations from data, a task fundamental to scientific discovery. However, the problem is highly challenging because closed-form equations lie in a complex combinatorial search ... ...

    Abstract Symbolic regression (SR) aims to discover concise closed-form mathematical equations from data, a task fundamental to scientific discovery. However, the problem is highly challenging because closed-form equations lie in a complex combinatorial search space. Existing methods, ranging from heuristic search to reinforcement learning, fail to scale with the number of input variables. We make the observation that closed-form equations often have structural characteristics and invariances (e.g., the commutative law) that could be further exploited to build more effective symbolic regression solutions. Motivated by this observation, our key contribution is to leverage pre-trained deep generative models to capture the intrinsic regularities of equations, thereby providing a solid foundation for subsequent optimization steps. We show that our novel formalism unifies several prominent approaches of symbolic regression and offers a new perspective to justify and improve on the previous ad hoc designs, such as the usage of cross-entropy loss during pre-training. Specifically, we propose an instantiation of our framework, Deep Generative Symbolic Regression (DGSR). In our experiments, we show that DGSR achieves a higher recovery rate of true equations in the setting of a larger number of input variables, and it is more computationally efficient at inference time than state-of-the-art RL symbolic regression solutions.

    Comment: In the proceedings of the Eleventh International Conference on Learning Representations (ICLR 2023). https://iclr.cc/virtual/2023/poster/11782
    Schlagwörter Computer Science - Machine Learning ; I.2.6 ; I.2.5
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2023-12-30
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    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  3. Buch ; Online: Synthcity

    Qian, Zhaozhi / Cebere, Bogdan-Constantin / van der Schaar, Mihaela

    facilitating innovative use cases of synthetic data in different data modalities

    2023  

    Abstract: Synthcity is an open-source software package for innovative use cases of synthetic data in ML fairness, privacy and augmentation across diverse tabular data modalities, including static data, regular and irregular time series, data with censoring, multi- ... ...

    Abstract Synthcity is an open-source software package for innovative use cases of synthetic data in ML fairness, privacy and augmentation across diverse tabular data modalities, including static data, regular and irregular time series, data with censoring, multi-source data, composite data, and more. Synthcity provides the practitioners with a single access point to cutting edge research and tools in synthetic data. It also offers the community a playground for rapid experimentation and prototyping, a one-stop-shop for SOTA benchmarks, and an opportunity for extending research impact. The library can be accessed on GitHub (https://github.com/vanderschaarlab/synthcity) and pip (https://pypi.org/project/synthcity/). We warmly invite the community to join the development effort by providing feedback, reporting bugs, and contributing code.
    Schlagwörter Computer Science - Machine Learning ; Computer Science - Artificial Intelligence
    Thema/Rubrik (Code) 306
    Erscheinungsdatum 2023-01-18
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  4. Buch ; Online: Causal Deep Learning

    Berrevoets, Jeroen / Kacprzyk, Krzysztof / Qian, Zhaozhi / van der Schaar, Mihaela

    2023  

    Abstract: Causality has the potential to truly transform the way we solve a large number of real-world problems. Yet, so far, its potential remains largely unlocked since most work so far requires strict assumptions which do not hold true in practice. To address ... ...

    Abstract Causality has the potential to truly transform the way we solve a large number of real-world problems. Yet, so far, its potential remains largely unlocked since most work so far requires strict assumptions which do not hold true in practice. To address this challenge and make progress in solving real-world problems, we propose a new way of thinking about causality - we call this causal deep learning. The framework which we propose for causal deep learning spans three dimensions: (1) a structural dimension, which allows incomplete causal knowledge rather than assuming either full or no causal knowledge; (2) a parametric dimension, which encompasses parametric forms which are typically ignored; and finally, (3) a temporal dimension, which explicitly allows for situations which capture exposure times or temporal structure. Together, these dimensions allow us to make progress on a variety of real-world problems by leveraging (sometimes incomplete) causal knowledge and/or combining diverse causal deep learning methods. This new framework also enables researchers to compare systematically across existing works as well as identify promising research areas which can lead to real-world impact.

    Comment: arXiv admin note: substantial text overlap with arXiv:2212.00911
    Schlagwörter Computer Science - Machine Learning ; Computer Science - Artificial Intelligence
    Thema/Rubrik (Code) 501
    Erscheinungsdatum 2023-03-03
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    Dokumenttyp Buch ; Online
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  5. Buch ; Online: TRIAGE

    Seedat, Nabeel / Crabbé, Jonathan / Qian, Zhaozhi / van der Schaar, Mihaela

    Characterizing and auditing training data for improved regression

    2023  

    Abstract: Data quality is crucial for robust machine learning algorithms, with the recent interest in data-centric AI emphasizing the importance of training data characterization. However, current data characterization methods are largely focused on classification ...

    Abstract Data quality is crucial for robust machine learning algorithms, with the recent interest in data-centric AI emphasizing the importance of training data characterization. However, current data characterization methods are largely focused on classification settings, with regression settings largely understudied. To address this, we introduce TRIAGE, a novel data characterization framework tailored to regression tasks and compatible with a broad class of regressors. TRIAGE utilizes conformal predictive distributions to provide a model-agnostic scoring method, the TRIAGE score. We operationalize the score to analyze individual samples' training dynamics and characterize samples as under-, over-, or well-estimated by the model. We show that TRIAGE's characterization is consistent and highlight its utility to improve performance via data sculpting/filtering, in multiple regression settings. Additionally, beyond sample level, we show TRIAGE enables new approaches to dataset selection and feature acquisition. Overall, TRIAGE highlights the value unlocked by data characterization in real-world regression applications

    Comment: Presented at NeurIPS 2023
    Schlagwörter Computer Science - Machine Learning
    Thema/Rubrik (Code) 310
    Erscheinungsdatum 2023-10-29
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  6. Buch ; Online: Learning Representations without Compositional Assumptions

    Liu, Tennison / Berrevoets, Jeroen / Qian, Zhaozhi / van der Schaar, Mihaela

    2023  

    Abstract: This paper addresses unsupervised representation learning on tabular data containing multiple views generated by distinct sources of measurement. Traditional methods, which tackle this problem using the multi-view framework, are constrained by predefined ...

    Abstract This paper addresses unsupervised representation learning on tabular data containing multiple views generated by distinct sources of measurement. Traditional methods, which tackle this problem using the multi-view framework, are constrained by predefined assumptions that assume feature sets share the same information and representations should learn globally shared factors. However, this assumption is not always valid for real-world tabular datasets with complex dependencies between feature sets, resulting in localized information that is harder to learn. To overcome this limitation, we propose a data-driven approach that learns feature set dependencies by representing feature sets as graph nodes and their relationships as learnable edges. Furthermore, we introduce LEGATO, a novel hierarchical graph autoencoder that learns a smaller, latent graph to aggregate information from multiple views dynamically. This approach results in latent graph components that specialize in capturing localized information from different regions of the input, leading to superior downstream performance.
    Schlagwörter Computer Science - Machine Learning
    Thema/Rubrik (Code) 004
    Erscheinungsdatum 2023-05-31
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  7. Buch ; Online: Synthetic data, real errors

    van Breugel, Boris / Qian, Zhaozhi / van der Schaar, Mihaela

    how (not) to publish and use synthetic data

    2023  

    Abstract: Generating synthetic data through generative models is gaining interest in the ML community and beyond, promising a future where datasets can be tailored to individual needs. Unfortunately, synthetic data is usually not perfect, resulting in potential ... ...

    Abstract Generating synthetic data through generative models is gaining interest in the ML community and beyond, promising a future where datasets can be tailored to individual needs. Unfortunately, synthetic data is usually not perfect, resulting in potential errors in downstream tasks. In this work we explore how the generative process affects the downstream ML task. We show that the naive synthetic data approach -- using synthetic data as if it is real -- leads to downstream models and analyses that do not generalize well to real data. As a first step towards better ML in the synthetic data regime, we introduce Deep Generative Ensemble (DGE) -- a framework inspired by Deep Ensembles that aims to implicitly approximate the posterior distribution over the generative process model parameters. DGE improves downstream model training, evaluation, and uncertainty quantification, vastly outperforming the naive approach on average. The largest improvements are achieved for minority classes and low-density regions of the original data, for which the generative uncertainty is largest.

    Comment: Proceedings of the 40th International Conference on Machine Learning (ICML 2023)
    Schlagwörter Computer Science - Machine Learning
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2023-05-16
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  8. Buch ; Online: Neural Laplace

    Holt, Samuel / Qian, Zhaozhi / van der Schaar, Mihaela

    Learning diverse classes of differential equations in the Laplace domain

    2022  

    Abstract: Neural Ordinary Differential Equations model dynamical systems with ODEs learned by neural networks. However, ODEs are fundamentally inadequate to model systems with long-range dependencies or discontinuities, which are common in engineering and ... ...

    Abstract Neural Ordinary Differential Equations model dynamical systems with ODEs learned by neural networks. However, ODEs are fundamentally inadequate to model systems with long-range dependencies or discontinuities, which are common in engineering and biological systems. Broader classes of differential equations (DE) have been proposed as remedies, including delay differential equations and integro-differential equations. Furthermore, Neural ODE suffers from numerical instability when modelling stiff ODEs and ODEs with piecewise forcing functions. In this work, we propose Neural Laplace, a unified framework for learning diverse classes of DEs including all the aforementioned ones. Instead of modelling the dynamics in the time domain, we model it in the Laplace domain, where the history-dependencies and discontinuities in time can be represented as summations of complex exponentials. To make learning more efficient, we use the geometrical stereographic map of a Riemann sphere to induce more smoothness in the Laplace domain. In the experiments, Neural Laplace shows superior performance in modelling and extrapolating the trajectories of diverse classes of DEs, including the ones with complex history dependency and abrupt changes.

    Comment: Proceedings of the 39th International Conference on Machine Learning, Baltimore, Maryland, USA, PMLR 162, 2022. Copyright 2022 by the author(s)
    Schlagwörter Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Statistics - Machine Learning ; I.2.6 ; I.2.5 ; E.1
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2022-06-09
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    Dokumenttyp Buch ; Online
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  9. Buch ; Online: D-CIPHER

    Kacprzyk, Krzysztof / Qian, Zhaozhi / van der Schaar, Mihaela

    Discovery of Closed-form Partial Differential Equations

    2022  

    Abstract: Closed-form differential equations, including partial differential equations and higher-order ordinary differential equations, are one of the most important tools used by scientists to model and better understand natural phenomena. Discovering these ... ...

    Abstract Closed-form differential equations, including partial differential equations and higher-order ordinary differential equations, are one of the most important tools used by scientists to model and better understand natural phenomena. Discovering these equations directly from data is challenging because it requires modeling relationships between various derivatives that are not observed in the data (equation-data mismatch) and it involves searching across a huge space of possible equations. Current approaches make strong assumptions about the form of the equation and thus fail to discover many well-known systems. Moreover, many of them resolve the equation-data mismatch by estimating the derivatives, which makes them inadequate for noisy and infrequently sampled systems. To this end, we propose D-CIPHER, which is robust to measurement artifacts and can uncover a new and very general class of differential equations. We further design a novel optimization procedure, CoLLie, to help D-CIPHER search through this class efficiently. Finally, we demonstrate empirically that it can discover many well-known equations that are beyond the capabilities of current methods.

    Comment: To appear in the Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS 2023)
    Schlagwörter Computer Science - Machine Learning
    Erscheinungsdatum 2022-06-21
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  10. Buch ; Online: Membership Inference Attacks against Synthetic Data through Overfitting Detection

    van Breugel, Boris / Sun, Hao / Qian, Zhaozhi / van der Schaar, Mihaela

    2023  

    Abstract: Data is the foundation of most science. Unfortunately, sharing data can be obstructed by the risk of violating data privacy, impeding research in fields like healthcare. Synthetic data is a potential solution. It aims to generate data that has the same ... ...

    Abstract Data is the foundation of most science. Unfortunately, sharing data can be obstructed by the risk of violating data privacy, impeding research in fields like healthcare. Synthetic data is a potential solution. It aims to generate data that has the same distribution as the original data, but that does not disclose information about individuals. Membership Inference Attacks (MIAs) are a common privacy attack, in which the attacker attempts to determine whether a particular real sample was used for training of the model. Previous works that propose MIAs against generative models either display low performance -- giving the false impression that data is highly private -- or need to assume access to internal generative model parameters -- a relatively low-risk scenario, as the data publisher often only releases synthetic data, not the model. In this work we argue for a realistic MIA setting that assumes the attacker has some knowledge of the underlying data distribution. We propose DOMIAS, a density-based MIA model that aims to infer membership by targeting local overfitting of the generative model. Experimentally we show that DOMIAS is significantly more successful at MIA than previous work, especially at attacking uncommon samples. The latter is disconcerting since these samples may correspond to underrepresented groups. We also demonstrate how DOMIAS' MIA performance score provides an interpretable metric for privacy, giving data publishers a new tool for achieving the desired privacy-utility trade-off in their synthetic data.
    Schlagwörter Computer Science - Machine Learning ; Computer Science - Cryptography and Security
    Thema/Rubrik (Code) 310
    Erscheinungsdatum 2023-02-24
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