Zobrazeno 1 - 10
of 425
pro vyhledávání: '"Davis, Jesse"'
Argument structure learning~(ASL) entails predicting relations between arguments. Because it can structure a document to facilitate its understanding, it has been widely applied in many fields~(medical, commercial, and scientific domains). Despite it
Externí odkaz:
http://arxiv.org/abs/2405.01216
Tree ensembles are one of the most widely used model classes. However, these models are susceptible to adversarial examples, i.e., slightly perturbed examples that elicit a misprediction. There has been significant research on designing approaches to
Externí odkaz:
http://arxiv.org/abs/2402.08586
With the increasing deployment of machine learning models in many socially-sensitive tasks, there is a growing demand for reliable and trustworthy predictions. One way to accomplish these requirements is to allow a model to abstain from making a pred
Externí odkaz:
http://arxiv.org/abs/2401.12708
Medical Question Answering~(medical QA) systems play an essential role in assisting healthcare workers in finding answers to their questions. However, it is not sufficient to merely provide answers by medical QA systems because users might want expla
Externí odkaz:
http://arxiv.org/abs/2310.01299
Autor:
Perini, Lorenzo, Davis, Jesse
Anomaly detection aims at detecting unexpected behaviours in the data. Because anomaly detection is usually an unsupervised task, traditional anomaly detectors learn a decision boundary by employing heuristics based on intuitions, which are hard to v
Externí odkaz:
http://arxiv.org/abs/2305.13189
Anomaly detection attempts at finding examples that deviate from the expected behaviour. Usually, anomaly detection is tackled from an unsupervised perspective because anomalous labels are rare and difficult to acquire. However, the lack of labels ma
Externí odkaz:
http://arxiv.org/abs/2301.02909
In this study, we predict next-day movements of stock end-of-day implied volatility using random forests. Through an ablation study, we examine the usefulness of different sources of predictors and expose the value of attention and sentiment features
Externí odkaz:
http://arxiv.org/abs/2301.00248
Tree ensembles are powerful models that are widely used. However, they are susceptible to adversarial examples, which are examples that purposely constructed to elicit a misprediction from the model. This can degrade performance and erode a user's tr
Externí odkaz:
http://arxiv.org/abs/2206.13083