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07/11/2023
Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation
The practical utility of causality in decision-making is widespread and ...
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07/05/2023
Causal Discovery with Language Models as Imperfect Experts
Understanding the causal relationships that underlie a system is a funda...
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06/07/2023
Invariant Causal Set Covering Machines
Rule-based models, such as decision trees, appeal to practitioners due t...
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04/19/2023
Regions of Reliability in the Evaluation of Multivariate Probabilistic Forecasts
Multivariate probabilistic time series forecasts are commonly evaluated ...
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08/11/2022
RandomSCM: interpretable ensembles of sparse classifiers tailored for omics data
Background: Understanding the relationship between the Omics and the phe...
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02/07/2022
TACTiS: Transformer-Attentional Copulas for Time Series
The estimation of time-varying quantities is a fundamental component of ...
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07/22/2021
Typing assumptions improve identification in causal discovery
Causal discovery from observational data is a challenging task to which ...
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06/24/2021
byteSteady: Fast Classification Using Byte-Level n-Gram Embeddings
This article introduces byteSteady – a fast model for classification usi...
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10/22/2020
In Search of Robust Measures of Generalization
One of the principal scientific challenges in deep learning is explainin...
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09/14/2020
Synbols: Probing Learning Algorithms with Synthetic Datasets
Progress in the field of machine learning has been fueled by the introdu...
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07/03/2020
Differentiable Causal Discovery from Interventional Data
Discovering causal relationships in data is a challenging task that invo...
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03/09/2020
Embedding Propagation: Smoother Manifold for Few-Shot Classification
Few-shot classification is challenging because the data distribution of ...
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01/10/2018
Deep Learning for Electromyographic Hand Gesture Signal Classification by Leveraging Transfer Learning
In recent years, the use of deep learning algorithms has become increasi...
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10/11/2017
Maximum Margin Interval Trees
Learning a regression function using censored or interval-valued output ...
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05/22/2015
Greedy Biomarker Discovery in the Genome with Applications to Antimicrobial Resistance
The Set Covering Machine (SCM) is a greedy learning algorithm that produ...
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12/02/2014
Learning interpretable models of phenotypes from whole genome sequences with the Set Covering Machine
The increased affordability of whole genome sequencing has motivated its...
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07/31/2012
