Zobrazeno 1 - 10
of 111
pro vyhledávání: '"Cripps, Sally"'
Autor:
Cripps, Sally, Lopatnikova, Anna, Afshar, Hadi Mohasel, Gales, Ben, Marchant, Roman, Francis, Gilad, Moreira, Catarina, Fischer, Alex
This paper proposes Bayesian Adaptive Trials (BAT) as both an efficient method to conduct trials and a unifying framework for evaluation social policy interventions, addressing limitations inherent in traditional methods such as Randomized Controlled
Externí odkaz:
http://arxiv.org/abs/2406.02868
Open-World Compositional Zero-Shot Learning (OW-CZSL) aims to recognize new compositions of seen attributes and objects. In OW-CZSL, methods built on the conventional closed-world setting degrade severely due to the unconstrained OW test space. While
Externí odkaz:
http://arxiv.org/abs/2303.00404
Recent advances in recommender systems have proved the potential of Reinforcement Learning (RL) to handle the dynamic evolution processes between users and recommender systems. However, learning to train an optimal RL agent is generally impractical w
Externí odkaz:
http://arxiv.org/abs/2208.05142
Autor:
Chin, Vincent, Samia, Noelle I., Marchant, Roman, Rosen, Ori, Ioannidis, John P. A., Tanner, Martin A., Cripps, Sally
Forecasting models have been influential in shaping decision-making in the COVID-19 pandemic. However, there is concern that their predictions may have been misleading. Here, we dissect the predictions made by four models for the daily COVID-19 death
Externí odkaz:
http://arxiv.org/abs/2006.15997
This paper provides a formal evaluation of the predictive performance of a model (and its various updates) developed by the Institute for Health Metrics and Evaluation (IHME) for predicting daily deaths attributed to COVID19 for each state in the Uni
Externí odkaz:
http://arxiv.org/abs/2004.04734
We present a method for the joint analysis of a panel of possibly nonstationary time series. The approach is Bayesian and uses a covariate-dependent infinite mixture model to incorporate multiple time series, with mixture components parameterized by
Externí odkaz:
http://arxiv.org/abs/1908.06622
We propose a scalable framework for inference in an inhomogeneous Poisson process modeled by a continuous sigmoidal Cox process that assumes the corresponding intensity function is given by a Gaussian process (GP) prior transformed with a scaled logi
Externí odkaz:
http://arxiv.org/abs/1906.03161
Discrimination between non-stationarity and long-range dependency is a difficult and long-standing issue in modelling financial time series. This paper uses an adaptive spectral technique which jointly models the non-stationarity and dependency of fi
Externí odkaz:
http://arxiv.org/abs/1902.03350
Autor:
Scalzo, Richard, Kohn, David, Olierook, Hugo, Houseman, Gregory, Chandra, Rohitash, Girolami, Mark, Cripps, Sally
The rigorous quantification of uncertainty in geophysical inversions is a challenging problem. Inversions are often ill-posed and the likelihood surface may be multi-modal; properties of any single mode become inadequate uncertainty measures, and sam
Externí odkaz:
http://arxiv.org/abs/1812.00318
Bayesian neural learning feature a rigorous approach to estimation and uncertainty quantification via the posterior distribution of weights that represent knowledge of the neural network. This not only provides point estimates of optimal set of weigh
Externí odkaz:
http://arxiv.org/abs/1811.04343