@inproceedings{kim2022closing,

title = {Closing the Loop: Graph Networks to Unify Semantic Objects and Visual Features for Multi-object Scenes},

author = {Jonathan Kim and Martin Urschler and Pat Riddle and J\"{o}rg Wicker},

url = {https://ieeexplore.ieee.org/abstract/document/9981542},

doi = {10.1109/IROS47612.2022.9981542},

year  = {2022},

date = {2022-10-20},

urldate = {2022-10-20},

booktitle = {2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022)},

pages = {4352-4358},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{poonawala-lohani2022geographic,

title = {Geographic Ensembles of Observations using Randomised Ensembles of Autoregression Chains: Ensemble methods for spatio-temporal Time Series Forecasting of Influenza-like Illness},

author = {Nooriyan Poonawala-Lohani and Pat Riddle and Mehnaz Adnan and J\"{o}rg Wicker},

doi = {10.1145/3535508.3545562},

isbn = {9781450393867},

year  = {2022},

date = {2022-08-07},

urldate = {2022-08-07},

booktitle = {Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics},

pages = {1-7},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

abstract = {Influenza is a communicable respiratory illness that can cause serious public health hazards. Flu surveillance in New Zealand tracks case counts from various District health boards (DHBs) in the country to monitor the spread of influenza in different geographic locations. Many factors contribute to the spread of the influenza across a geographic region, and it can be challenging to forecast cases in one region without taking into account case numbers in another region. This paper proposes a novel ensemble method called Geographic Ensembles of Observations using Randomised Ensembles of Autoregression Chains (GEO-Reach). GEO-Reach is an ensemble technique that uses a two layer approach to utilise interdependence of historical case counts between geographic regions in New Zealand. This work extends a previously published method by the authors called Randomized Ensembles of Auto-regression chains (Reach). State-of-the-art forecasting models look at studying the spread of the virus. They focus on accurate forecasting of cases for a location using historical case counts for the same location and other data sources based on human behaviour such as movement of people across cities/geographic regions. This new approach is evaluated using Influenza like illness (ILI) case counts in 7 major regions in New Zealand from the years 2015-2019 and compares its performance with other standard methods such as Dante, ARIMA, Autoregression and Random Forests. The results demonstrate that the proposed method performed better than baseline methods when applied to this multi-variate time series forecasting problem.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{graffeuille2022semi,

title = {Semi-Supervised Conditional Density Estimation with Wasserstein Laplacian Regularisation},

author = {Olivier Graffeuille and Yun Sing Koh and J\"{o}rg Wicker and Moritz Lehmann},

url = {https://ojs.aaai.org/index.php/AAAI/article/view/20630},

doi = {10.1609/aaai.v36i6.20630 },

year  = {2022},

date = {2022-06-28},

urldate = {2022-02-22},

booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence,},

volume = {36},

number = {6},

pages = {6746-6754},

abstract = {Conditional Density Estimation (CDE) has wide-reaching applicability to various real-world problems, such as spatial density estimation and environmental modelling. CDE estimates the probability density of a random variable rather than a single value and can thus model uncertainty and inverse problems. This task is inherently more complex than regression, and many algorithms suffer from overfitting, particularly when modelled with few labelled data points. For applications where unlabelled data is abundant but labelled data is scarce, we propose Wasserstein Laplacian Regularisation, a semi-supervised learning framework that allows CDE algorithms to leverage these unlabelled data. The framework minimises an objective function which ensures that the learned model is smooth along the manifold of the underlying data, as measured by Wasserstein distance. When applying our framework to Mixture Density Networks, the resulting semi-supervised algorithm can achieve similar performance to a supervised model with up to three times as many labelled data points on baseline datasets. We additionally apply our technique to the problem of remote sensing for chlorophyll-a estimation in inland waters.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{dost2022divide,

title = {Divide and Imitate: Multi-Cluster Identification and Mitigation of Selection Bias},

author = {Katharina Dost and Hamish Duncanson and Ioannis Ziogas and Pat Riddle and J\"{o}rg Wicker},

doi = {10.1007/978-3-031-05936-0_12},

isbn = {978-3-031-05935-3},

year  = {2022},

date = {2022-05-16},

urldate = {2022-05-16},

booktitle = {26th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD2022)},

pages = {149\textendash160},

publisher = {Springer-Verlag},

address = {Berlin, Heidelberg},

abstract = {Machine Learning can help overcome human biases in decision making by focusing on purely logical conclusions based on the training data. If the training data is biased, however, that bias will be transferred to the model and remains undetected as the performance is validated on a test set drawn from the same biased distribution. Existing strategies for selection bias identification and mitigation generally rely on some sort of knowledge of the bias or the ground-truth. An exception is the Imitate algorithm that assumes no knowledge but comes with a strong limitation: It can only model datasets with one normally distributed cluster per class. In this paper, we introduce a novel algorithm, Mimic, which uses Imitate as a building block but relaxes this limitation. By allowing mixtures of multivariate Gaussians, our technique is able to model multi-cluster datasets and provide solutions for a substantially wider set of problems. Experiments confirm that Mimic not only identifies potential biases in multi-cluster datasets which can be corrected early on but also improves classifier performance.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{poonawala-lohani2022novel,

title = {A Novel Approach for Time Series Forecasting of Influenza-like Illness Using a Regression Chain Method},

author = {Nooriyan Poonawala-Lohani and Pat Riddle and Mehnaz Adnan and J\"{o}rg Wicker},

editor = {Russ Altman and Keith Dunker and Lawrence Hunter and Marylyn Ritchie and Tiffany Murray and Teri Klein},

url = {https://www.worldscientific.com/doi/abs/10.1142/9789811250477_0028

http://psb.stanford.edu/psb-online/proceedings/psb22/poorawala-lohani.pdf},

doi = {10.1142/9789811250477_0028},

year  = {2022},

date = {2022-01-03},

urldate = {2022-01-03},

booktitle = {Pacific Symposium on Biocomputing},

volume = {27},

pages = {301-312},

abstract = {Influenza  is  a  communicable  respiratory  illness  that  can cause serious public health hazards. Due to its huge threat to the community, accurate forecasting of Influenza-like-illness (ILI) can diminish the impact of an influenza season by enabling early public health interventions.  Current  forecasting  models  are  limited  in  their  performance, particularly  when  using  a  longer  forecasting  window.  To  support  better forecasts over a longer forecasting window, we propose to use additional features such as weather data. Commonly used methods to fore-cast ILI, including statistical methods such as ARIMA, limit prediction performance when using additional data sources that might have complex  non-linear  associations  with  ILI  incidence.  This  paper  proposes  a novel time series forecasting method, Randomized Ensembles of Auto-regression chains (Reach). Reach implements an ensemble of random chains for multi-step time series forecasting. This new approach is evaluated on ILI case counts in Auckland, New Zealand from the years 2015-2018 and compared to other standard methods. The results demonstrate that the proposed method performed better than baseline methods when applied to this multi-variate time series forecasting problem.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}