1 code implementation • 12 Jul 2022 • Isha Hameed, Samuel Sharpe, Daniel Barcklow, Justin Au-Yeung, Sahil Verma, Jocelyn Huang, Brian Barr, C. Bayan Bruss
By perturbing the input variables in rank order of importance, the goal is to assess the sensitivity of the model's performance.
1 code implementation • 30 Jun 2022 • Roman Levin, Valeriia Cherepanova, Avi Schwarzschild, Arpit Bansal, C. Bayan Bruss, Tom Goldstein, Andrew Gordon Wilson, Micah Goldblum
In this work, we demonstrate that upstream data gives tabular neural networks a decisive advantage over widely used GBDT models.
no code implementations • 2 Dec 2021 • Brian Barr, Matthew R. Harrington, Samuel Sharpe, C. Bayan Bruss
Counterfactual explanations represent the minimal change to a data sample that alters its predicted classification, typically from an unfavorable initial class to a desired target class.
no code implementations • 29 Sep 2021 • Neha Mukund Kalibhat, Yogesh Balaji, C. Bayan Bruss, Soheil Feizi
In fact, training these methods on a combination of several domains often degrades the quality of learned representations compared to the models trained on a single domain.
no code implementations • 17 Jun 2021 • Arpit Bansal, Micah Goldblum, Valeriia Cherepanova, Avi Schwarzschild, C. Bayan Bruss, Tom Goldstein
Class-imbalanced data, in which some classes contain far more samples than others, is ubiquitous in real-world applications.
7 code implementations • 2 Jun 2021 • Gowthami Somepalli, Micah Goldblum, Avi Schwarzschild, C. Bayan Bruss, Tom Goldstein
We devise a hybrid deep learning approach to solving tabular data problems.
no code implementations • 16 Dec 2020 • Rachana Balasubramanian, Samuel Sharpe, Brian Barr, Jason Wittenbach, C. Bayan Bruss
In the environment of fair lending laws and the General Data Protection Regulation (GDPR), the ability to explain a model's prediction is of paramount importance.
no code implementations • 4 Oct 2020 • Oluwatobi O. Olabiyi, Prarthana Bhattarai, C. Bayan Bruss, Zachary Kulis
Task oriented dialogue (TOD) requires the complex interleaving of a number of individually controllable components with strong guarantees for explainability and verifiability.
no code implementations • 11 Sep 2020 • Jason Wittenbach, Brian d'Alessandro, C. Bayan Bruss
Temporal data are ubiquitous in the financial services (FS) industry -- traditional data like economic indicators, operational data such as bank account transactions, and modern data sources like website clickstreams -- all of these occur as a time-indexed sequence.
1 code implementation • 20 Jul 2020 • Brian Barr, Ke Xu, Claudio Silva, Enrico Bertini, Robert Reilly, C. Bayan Bruss, Jason D. Wittenbach
In data science, there is a long history of using synthetic data for method development, feature selection and feature engineering.
no code implementations • 1 Jul 2020 • Antonia Gogoglou, Brian Nguyen, Alan Salimov, Jonathan Rider, C. Bayan Bruss
Financial transactions constitute connections between entities and through these connections a large scale heterogeneous weighted graph is formulated.
no code implementations • 18 Jun 2020 • Antonia Gogoglou, C. Bayan Bruss, Brian Nguyen, Reza Sarshogh, Keegan E. Hines
Graph Representation Learning (GRL) has experienced significant progress as a means to extract structural information in a meaningful way for subsequent learning tasks.
no code implementations • 7 Oct 2019 • Antonia Gogoglou, C. Bayan Bruss, Keegan E. Hines
With the rising interest in graph representation learning, a variety of approaches have been proposed to effectively capture a graph's properties.
no code implementations • 15 Aug 2019 • Anh Truong, Austin Walters, Jeremy Goodsitt, Keegan Hines, C. Bayan Bruss, Reza Farivar
There has been considerable growth and interest in industrial applications of machine learning (ML) in recent years.
no code implementations • 16 Jul 2019 • C. Bayan Bruss, Anish Khazane, Jonathan Rider, Richard Serpe, Antonia Gogoglou, Keegan E. Hines
In this paper, we present a novel application of representation learning to bipartite graphs of credit card transactions in order to learn embeddings of account and merchant entities.
no code implementations • 3 Jul 2019 • C. Bayan Bruss, Anish Khazane, Jonathan Rider, Richard Serpe, Saurabh Nagrecha, Keegan E. Hines
Graph embedding is a popular algorithmic approach for creating vector representations for individual vertices in networks.