Machine Learning – Publications

@article{DBLP:journals/corr/abs-2306-05415,

title = {Causal normalizing flows: from theory to practice},

author = {Adrián Javaloy and Pablo Sánchez-Mart'ın and Isabel Valera},

url = {https://doi.org/10.48550/arXiv.2306.05415},

doi = {10.48550/arXiv.2306.05415},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {CoRR},

volume = {abs/2306.05415},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/corr/abs-2302-06223,

title = {Variational Mixture of HyperGenerators for Learning Distributions 

 Over Functions},

author = {Batuhan Koyuncu and Pablo Sánchez-Mart'ın and Ignacio Peis and Pablo M. Olmos and Isabel Valera},

url = {https://doi.org/10.48550/arXiv.2302.06223},

doi = {10.48550/arXiv.2302.06223},

year  = {2023},

date = {2023-01-01},

journal = {CoRR},

volume = {abs/2302.06223},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/csur/KarimiBSV23,

title = {A Survey of Algorithmic Recourse: Contrastive Explanations and Consequential 

 Recommendations},

author = {Amir-Hossein Karimi and Gilles Barthe and Bernhard Schölkopf and Isabel Valera},

url = {https://doi.org/10.1145/3527848},

doi = {10.1145/3527848},

year  = {2023},

date = {2023-01-01},

journal = {ACM Comput. Surv.},

volume = {55},

number = {5},

pages = {95:1–95:29},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/corr/abs-2302-03465,

title = {Robustness Implies Fairness in Causal Algorithmic Recourse},

author = {Ahmad-Reza Ehyaei and Amir-Hossein Karimi and Bernhard Schölkopf and Setareh Maghsudi},

url = {https://doi.org/10.48550/arXiv.2302.03465},

doi = {10.48550/arXiv.2302.03465},

year  = {2023},

date = {2023-01-01},

journal = {CoRR},

volume = {abs/2302.03465},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/corr/abs-2303-00408,

title = {A Persian Benchmark for Joint Intent Detection and Slot Filling},

author = {Masoud Akbari and Amir-Hossein Karimi and Tayyebeh Saeedi and Zeinab Saeidi and Kiana Ghezelbash and Fatemeh Shamsezat and Mohammad Akbari and Ali Mohades},

url = {https://doi.org/10.48550/arXiv.2303.00408},

doi = {10.48550/arXiv.2303.00408},

year  = {2023},

date = {2023-01-01},

journal = {CoRR},

volume = {abs/2303.00408},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/nn/ReyesSanchezASERV23,

title = {Automatized offline and online exploration to achieve a target dynamics  in biohybrid neural circuits built with living and model neurons},

author = {Manuel Reyes-Sanchez and Rodrigo Amaducci and Pablo Sánchez-Mart'ın and Irene Elices and Francisco Borja Rodr'ıguez and Pablo Varona},

url = {https://doi.org/10.1016/j.neunet.2023.04.034},

doi = {10.1016/j.neunet.2023.04.034},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {Neural Networks},

volume = {164},

pages = {464–475},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/isci/SanchezMartinOP23,

title = {Enhancing diversity in GANs via non-uniform sampling},

author = {Pablo Sánchez-Mart'ın and Pablo M. Olmos and Fernando Pérez-Cruz},

url = {https://doi.org/10.1016/j.ins.2023.04.007},

doi = {10.1016/j.ins.2023.04.007},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {Inf. Sci.},

volume = {637},

pages = {118928},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{,

title = {Chemotherapy Response Prediction with Diffuser Elapser Network},

author = {Batuhan Koyuncu and Ahmet Melek and Defne Yilmaz and Mert Tuzer and Mehmet Burcin Unlu},

url = {https://www.nature.com/articles/s41598-022-05460-z.pdf},

doi = {https://doi.org/10.1038/s41598-022-05460-z},

year  = {2022},

date = {2022-01-31},

urldate = {2022-01-31},

journal = {Scientific Reports},

volume = {12},

issue = {1},

pages = {1-13},

abstract = {In solid tumors, elevated fluid pressure and inadequate blood perfusion resulting from unbalanced angiogenesis are the prominent reasons for the ineffective drug delivery inside tumors. To normalize the heterogeneous and tortuous tumor vessel structure, antiangiogenic treatment is an effective approach. Additionally, the combined therapy of antiangiogenic agents and chemotherapy drugs has shown promising effects on enhanced drug delivery. However, the need to find the appropriate scheduling and dosages of the combination therapy is one of the main problems in anticancer therapy. Our study aims to generate a realistic response to the treatment schedule, making it possible for future works to use these patient-specific responses to decide on the optimal starting time and dosages of cytotoxic drug treatment. Our dataset is based on our previous in-silico model with a framework for the tumor microenvironment, consisting of a tumor layer, vasculature network, interstitial fluid pressure, and drug diffusion maps. In this regard, the chemotherapy response prediction problem is discussed in the study, putting forth a proof of concept for deep learning models to capture the tumor growth and drug response behaviors simultaneously. The proposed model utilizes multiple convolutional neural network submodels to predict future tumor microenvironment maps considering the effects of ongoing treatment. Since the model has the task of predicting future tumor microenvironment maps, we use two image quality evaluation metrics, which are structural similarity and peak signal-to-noise ratio, to evaluate model performance. We track tumor cell density values of ground truth and predicted tumor microenvironments. The  model  predicts  tumor  microenvironment  maps  seven  days  ahead  with the average structural similarity score of 0.973 and the average peak signal ratio of 35.41 in the test set. It also predicts tumor cell density at the end day of 7 with the mean absolute percentage error of $2.292pm1.820$.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/corr/abs-2206-04496,

title = {Mitigating Modality Collapse in Multimodal VAEs via Impartial Optimization},

author = {Adrián Javaloy and Maryam Meghdadi and Isabel Valera},

url = {https://doi.org/10.48550/arXiv.2206.04496},

doi = {10.48550/arXiv.2206.04496},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {CoRR},

volume = {abs/2206.04496},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/bib/KarimiKASS22,

title = {Prospects and challenges of cancer systems medicine: from genes to 

 disease networks},

author = {Mohammad Reza Karimi and Amir-Hossein Karimi and Shamsozoha Abolmaali and Mehdi Sadeghi and Ulf Schmitz},

url = {https://doi.org/10.1093/bib/bbab343},

doi = {10.1093/bib/bbab343},

year  = {2022},

date = {2022-01-01},

journal = {Briefings Bioinform.},

volume = {23},

number = {1},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/cmpb/KarimiMMGVM22,

title = {Feasibility of 18-MV grid therapy from radiation protection aspects: 

 unwanted dose and fatal cancer risk caused by photoneutrons and scattered 

 photons},

author = {Amir-Hossein Karimi and Seyed Farshad Mirian and Farshid Mahmoudi and Ghazale Geraily and Héctor René Vega-Carrillo and Majid Mohiuddin},

url = {https://doi.org/10.1016/j.cmpb.2021.106524},

doi = {10.1016/j.cmpb.2021.106524},

year  = {2022},

date = {2022-01-01},

journal = {Comput. Methods Programs Biomed.},

volume = {213},

pages = {106524},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/corr/abs-2212-06925,

title = {On the Relationship Between Explanation and Prediction: A Causal 

 View},

author = {Amir-Hossein Karimi and Krikamol Muandet and Simon Kornblith and Bernhard Schölkopf and Been Kim},

url = {https://doi.org/10.48550/arXiv.2212.06925},

doi = {10.48550/arXiv.2212.06925},

year  = {2022},

date = {2022-01-01},

journal = {CoRR},

volume = {abs/2212.06925},

keywords = {},

pubstate = {published},

tppubtype = {article}

}