Zobrazeno 1 - 10
of 156
pro vyhledávání: '"Sudjianto, Agus"'
Autor:
Shen, Yueyang, Sudjianto, Agus, R, Arun Prakash, Bhattacharyya, Anwesha, Rao, Maorong, Wang, Yaqun, Vaughan, Joel, Zhou, Nengfeng
We propose and study a minimalist approach towards synthetic tabular data generation. The model consists of a minimalistic unsupervised SparsePCA encoder (with contingent clustering step or log transformation to handle nonlinearity) and XGboost decod
Externí odkaz:
http://arxiv.org/abs/2411.10982
Tree ensemble models like random forests and gradient boosting machines are widely used in machine learning due to their excellent predictive performance. However, a high-performance ensemble consisting of a large number of decision trees lacks suffi
Externí odkaz:
http://arxiv.org/abs/2410.19098
Fair lending practices and model interpretability are crucial concerns in the financial industry, especially given the increasing use of complex machine learning models. In response to the Consumer Financial Protection Bureau's (CFPB) requirement to
Externí odkaz:
http://arxiv.org/abs/2410.19067
Autor:
Sudjianto, Agus, Zhang, Aijun
This paper presents a comprehensive overview of model validation practices and advancement in the banking industry based on the experience of managing Model Risk Management (MRM) since the inception of regulatory guidance SR11-7/OCC11-12 over a decad
Externí odkaz:
http://arxiv.org/abs/2410.13877
Recent work in behavioral testing for natural language processing (NLP) models, such as Checklist, is inspired by related paradigms in software engineering testing. They allow evaluation of general linguistic capabilities and domain understanding, he
Externí odkaz:
http://arxiv.org/abs/2408.00161
In the early days of machine learning (ML), the emphasis was on developing complex algorithms to achieve best predictive performance. To understand and explain the model results, one had to rely on post hoc explainability techniques, which are known
Externí odkaz:
http://arxiv.org/abs/2305.15670
PiML (read $\pi$-ML, /`pai`em`el/) is an integrated and open-access Python toolbox for interpretable machine learning model development and model diagnostics. It is designed with machine learning workflows in both low-code and high-code modes, includ
Externí odkaz:
http://arxiv.org/abs/2305.04214
Gradient-boosted decision trees (GBDT) are widely used and highly effective machine learning approach for tabular data modeling. However, their complex structure may lead to low robustness against small covariate perturbation in unseen data. In this
Externí odkaz:
http://arxiv.org/abs/2304.13761
When a financial institution declines an application for credit, an adverse action (AA) is said to occur. The applicant is then entitled to an explanation for the negative decision. This paper focuses on credit decisions based on a predictive model f
Externí odkaz:
http://arxiv.org/abs/2204.12365
Although neural networks (NNs) with ReLU activation functions have found success in a wide range of applications, their adoption in risk-sensitive settings has been limited by the concerns on robustness and interpretability. Previous works to examine
Externí odkaz:
http://arxiv.org/abs/2111.08922