Global ETD Search
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Showing 1 to 20 of 23 for “"Conformal Prediction"”.
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Data Augmentation and Conformal Prediction
Conformal prediction is a popular line of research in uncertainty quantification. Conformal predictors output sets of predictions accompanied by a guarantee that the set contains the true label. Conformal prediction is particularly promising because it makes no distributional assumptions and …
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Conformal prediction with temporal updates in cross-sectional time series
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-05-01
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A Diagnostic and Prescriptive Conformal Prediction Framework: Applied to Sleep Disorders
… given their clinical history. Via the use of a conformal algorithm with a classifier as its base model, we are able to utilize a patients history of diagnoses, pharmacy dispensing, and other features to produce a set of possible final sleep disorder diagnoses and/or treatments with a definitive …
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Robust Inference via Optimal Transport Ambiguity Sets
… errors via Wasserstein ambiguity sets. Split conformal prediction, hereafter referred to as conformal prediction, offers a powerful framework for quantifying predictive uncertainty by constructing prediction intervals with finite-sample, distribution-free guarantees. Despite its widespread …
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Locating People of Interest in Social Networks
… networks. We then demonstrate how to utilize conformal prediction framework [103] to obtain guaranteed error bounds in POI prediction. Experimental results show that the Conformal Prediction framework can provide up to a 30% improvement in node classification algorithm accuracy while …
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Conformal Methods for Efficient and Reliable Deep Learning
… deep learning systems that are broadly based on conformal prediction, a model-agnostic and distribution-free uncertainty estimation framework. We develop both theory and practice for leveraging uncertainty estimation to build adaptive models that are cheaper to run, have desirable performance …
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Forecasting and trading optimisation in the day-ahead and balancing market
… model as the most effective for accurate price prediction, closely followed by Extreme Gradient Boosting and Random Forest. Deep Learning models, while excelling in the DAM forecast, struggle to accurately forecast prices in the BM. Expanding on these insights, the thesis explores the …
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Towards an Informative Recommender System
… fine-tuning framework integrates conformal prediction to quantify uncertainty in recommendations. To bridge collaborative and semantic signals, we develop a method that improves performance in both warm-start and cold-start scenarios by leveraging textual information. We further …
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Generative Modeling with Guarantees
… while leaving others open for the model’s prediction. By facilitating interactive editing and rewriting, this framework provides users with precise control over the generated text. Next, we introduce conformal prediction methods for generating predictions under soft constraints, ensuring …
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Detección de Anomalías de Precio en Comercio Electrónico
… Además, se implementó un nuevo método llamado Conformal Prediction sobre el modelo Random Forest, el cual permite calibrar el balance entre el nivel de seguridad y las tareas manuales asociadas a la revisión de anomalías. La tesis concluye con recomendaciones para la implementación práctica de …
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Accurate Uncertainty Quantification and Explainable Artificial Intelligence in Machine Learning Models for Toxicological Risk Assessment
… Two key issues remain: uncertainty of the predictions and transparency of the model. The second chapter discusses mechanistically driven structural alerts for mitochondrial toxicity. Structural alerts are constructed using a maximum common substructure algorithm developed by Wedlake et al. …
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Robust and Efficient Deep Learning for Misinformation Prevention
… measures and develop novel extensions to the conformal prediction framework. Our methods can dynamically allocate the required computational resources for each input to satisfy an arbitrary user-specified tolerance level. We demonstrate on multiple datasets that our well-calibrated decision …
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UNDERSTANDING CONDITIONAL MODES OF ACTIONS IN CHEMICAL-INDUCED TOXICITY USING RULE MODELS
… against a benchmark Random Forest model in a conformal prediction framework. Irrespective to the data type used in the training, the models were prone to bias over compounds promiscuity, by which high promiscuous compounds were more likely to be predicted as toxic. Overall, the studies …
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Optimization Techniques for Trustworthy 3D Object Understanding
… noise on 2D keypoint measurements (e.g., from conformal prediction), we derive an estimator for the most likely object pose which uses a semidefinite relaxation to initialize a local solver. We pair this with an efficient uncertainty estimation routine which relies on a generalization of the …
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Natural Language Foundation Models in Medical Artificial Intelligence
… of text self-supervision. Next, we explore how conformal prediction can be used to control zero-shot classification performance and preempt compatible inputs for these CLIP-style models. In chapter 4, I describe the development of Articulate Medical Intelligence Explorer (AMIE), a conversational …
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Topics in conditional causal inference
… method uses a distribution-free technique called conformal prediction to quantify the uncertainties in CATE estimates, then leverage the uncertainties to construct robust subgroups. It leads to more well-identified subgroups and fewer false discoveries due to random noise in the data. All the …
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Statistical uncertainty quantification for machine learning models and training acceleration for graph neural networks
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-05-01
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Predictive Modelling of the Primary and Secondary Pharmacology of Compounds in Drug Discovery
… section of this thesis is concerned with the prediction of ligand selectivity profiles using proteochemometric (PCM) modelling, a technique which uses both compound similarity and protein target similarity as input into machine learning models for the prediction of ligand-target interactions. …
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Tackling performativity in discrete-time dynamical systems: An iterative refinement approach
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-03-01 without embargo terms
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