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:ID: bd98ede2-d6ec-4e93-8d10-4800fe528ff7
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#+title: thesis/citations
#+filetags: :project: :knowledge: :citations:

:PROPERTIES:
:ID: thesis-citations
:END:

#+title: thesis/citations
#+filetags: :project: :knowledge: :citations:

Sources used to justify implementation choices in the thesis. Linked from [[id:6294e2be-6189-4473-b363-a1dd9a75ff9b][thesis]].

* Bank Marketing Dataset — Model Architecture

** Justification
The standard neural network architecture for the UCI Bank Marketing dataset in the literature is a 2-hidden-layer MLP with 128 units per layer and ReLU activations. This is used in our =experiment-bank-marketing.py= as:
=Linear(input, 128) → ReLU → Dropout(0.3) → Linear(128, 128) → ReLU → Dropout(0.3) → Linear(128, 1)=

** Sources

- [[https://github.com/ktamburi/Bank-Marketing-UCI-Dataset-Neural-Network][ktamburi/Bank-Marketing-UCI-Dataset-Neural-Network (GitHub)]]
  Most-cited student/practitioner reference for NN on this dataset. Uses exactly 128→128 architecture with ReLU, final softmax (we use sigmoid for binary). Justifies our hidden layer size choice.

- [[https://arxiv.org/pdf/2407.00956][A Closer Look at Deep Learning Methods on Tabular Datasets (arXiv 2407.00956)]]
  Benchmarks MLP and TabNet on tabular datasets including bank marketing. Confirms that simple MLPs with 2-3 layers are the standard DL baseline for this domain.

- [[https://doi.org/10.3390/e28030354][Risk-Sensitive ML for Financial Decision Modeling Under Imbalanced Data (Entropy 2025)]]
  Directly addresses class imbalance on the bank marketing dataset. Supports our use of pos_weight and AUC as the primary metric given IR ~8:1.

- [[https://medium.com/@data-overload/bank-marketing-data-imbalanced-dataset-42140d6d4b95][Bank Marketing Data — Imbalanced Dataset (Medium)]]
  Confirms the dataset has ~11.3% positive rate (IR ~8:1), motivating use of imbalance-aware losses (GCE, MAE, focal, asymmetric, ROLL).