DeepTrax: Embedding Graphs of Financial Transactions. - Anish Khazane; Jonathan Rider; Max Serpe; Antonia Gogoglou; Keegan E. Hines; C. Bayan Bruss; Richard Serpe (ICMLA, 2019)

Towards Automated Machine Learning: Evaluation and Comparison of AutoML Approaches and Tools. - Anh Truong; Austin Walters; Jeremy Goodsitt; Keegan E. Hines; C. Bayan Bruss; Reza Farivar (ICTAI, 2019)

Graph Embeddings at Scale. - C. Bayan Bruss; Anish Khazane; Jonathan Rider; Richard Serpe; Saurabh Nagrecha; Keegan E. Hines (CoRR, 2019)

DeepTrax: Embedding Graphs of Financial Transactions. - C. Bayan Bruss; Anish Khazane; Jonathan Rider; Richard Serpe; Antonia Gogoglou; Keegan E. Hines (CoRR, 2019)

On the Interpretability and Evaluation of Graph Representation Learning. - Antonia Gogoglou; C. Bayan Bruss; Keegan E. Hines (CoRR, 2019)

Navigating the dynamics of financial embeddings over time. - Antonia Gogoglou; Brian Nguyen; Alan Salimov; Jonathan B. Rider; C. Bayan Bruss (ICAIF, 2020)

Quantifying Challenges in the Application of Graph Representation Learning. - Antonia Gogoglou; C. Bayan Bruss; Brian Nguyen; Reza Sarshogh; Keegan E. Hines (ICMLA, 2020)

Towards Ground Truth Explainability on Tabular Data. - Brian Barr; Ke Xu; Cláudio T. Silva; Enrico Bertini; Robert Reilly; C. Bayan Bruss; Jason D. Wittenbach (CoRR, 2020)

Machine Learning for Temporal Data in Finance: Challenges and Opportunities. - Jason D. Wittenbach; Brian d'Alessandro; C. Bayan Bruss (CoRR, 2020)

DLGNet-Task: An End-to-end Neural Network Framework for Modeling Multi-turn Multi-domain Task-Oriented Dialogue. - Oluwatobi O. Olabiyi; Prarthana Bhattarai; C. Bayan Bruss; Zachary Kulis (CoRR, 2020)

Latent-CF: A Simple Baseline for Reverse Counterfactual Explanations. - Rachana Balasubramanian; Samuel Sharpe; Brian Barr; Jason D. Wittenbach; C. Bayan Bruss (CoRR, 2020)

SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training. - Gowthami Somepalli; Micah Goldblum; Avi Schwarzschild; C. Bayan Bruss; Tom Goldstein (CoRR, 2021)

MetaBalance: High-Performance Neural Networks for Class-Imbalanced Data. - Arpit Bansal; Micah Goldblum; Valeriia Cherepanova; Avi Schwarzschild; C. Bayan Bruss; Tom Goldstein (CoRR, 2021)

Counterfactual Explanations via Latent Space Projection and Interpolation. - Brian Barr; Matthew R. Harrington; Samuel Sharpe; C. Bayan Bruss (CoRR, 2021)

Double-Hashing Algorithm for Frequency Estimation in Data Streams. - Nikita Seleznev; Senthil Kumar; C. Bayan Bruss (CoRR, 2022)

BASED-XAI: Breaking Ablation Studies Down for Explainable Artificial Intelligence. - Isha Hameed; Samuel Sharpe; Daniel Barcklow; Justin Au-Yeung; Sahil Verma; Jocelyn Huang; Brian Barr; C. Bayan Bruss (CoRR, 2022)

Adapting Self-Supervised Representations to Multi-Domain Setups. - Neha Mukund Kalibhat; Samuel Sharpe; Jeremy Goodsitt; C. Bayan Bruss; Soheil Feizi (BMVC, 2023)

From Detection to Action: a Human-in-the-loop Toolkit for Anomaly Reasoning and Management. - Xueying Ding; Nikita Seleznev; Senthil Kumar; C. Bayan Bruss; Leman Akoglu (ICAIF, 2023)

Transfer Learning with Deep Tabular Models. - Roman Levin; Valeriia Cherepanova; Avi Schwarzschild; Arpit Bansal; C. Bayan Bruss; Tom Goldstein; Andrew Gordon Wilson; Micah Goldblum (ICLR, 2023)

Identifying Interpretable Subspaces in Image Representations. - Neha Mukund Kalibhat; Shweta Bhardwaj; C. Bayan Bruss; Hamed Firooz; Maziar Sanjabi; Soheil Feizi (ICML, 2023)

GOAT: A Global Transformer on Large-scale Graphs. - Kezhi Kong; Jiuhai Chen; John Kirchenbauer; Renkun Ni; C. Bayan Bruss; Tom Goldstein (ICML, 2023)

A Performance-Driven Benchmark for Feature Selection in Tabular Deep Learning. - Valeriia Cherepanova; Roman Levin; Gowthami Somepalli; Jonas Geiping; C. Bayan Bruss; Andrew Gordon Wilson; Tom Goldstein; Micah Goldblum (NeurIPS, 2023)

Simplifying Neural Network Training Under Class Imbalance. - Ravid Shwartz-Ziv; Micah Goldblum; Yucen Lily Li; C. Bayan Bruss; Andrew Gordon Wilson (NeurIPS, 2023)

From Explanation to Action: An End-to-End Human-in-the-loop Framework for Anomaly Reasoning and Management. - Xueying Ding; Nikita Seleznev; Senthil Kumar; C. Bayan Bruss; Leman Akoglu (CoRR, 2023)

Searching for Efficient Linear Layers over a Continuous Space of Structured Matrices. - Andres Potapczynski; Shikai Qiu; Marc Finzi; Christopher Ferri; Charlie Chen; Micah Goldblum; C. Bayan Bruss; Christopher De Sa; Andrew Gordon Wilson (NeurIPS, 2024)

Just How Flexible are Neural Networks in Practice? - Ravid Shwartz-Ziv; Micah Goldblum; Arpit Bansal; C. Bayan Bruss; Yann LeCun; Andrew Gordon Wilson (CoRR, 2024)

Searching for Efficient Linear Layers over a Continuous Space of Structured Matrices. - Andres Potapczynski; Shikai Qiu; Marc Finzi; Christopher Ferri; Zixi Chen; Micah Goldblum; C. Bayan Bruss; Christopher De Sa; Andrew Gordon Wilson (CoRR, 2024)

A Simple Baseline for Predicting Events with Auto-Regressive Tabular Transformers. - Alex Stein; Samuel Sharpe; Doron Bergman; Senthil Kumar; C. Bayan Bruss; John Dickerson 001; Tom Goldstein; Micah Goldblum (CoRR, 2024)

Explain What You Mean: Intent Augmented Knowledge Graph Recommender Built With An LLM. - Wenqing Zheng; Noah Fatsi; Daniel Barcklow; Dmitri Kalaev; Steven Yao; Owen Reinert; C. Bayan Bruss; Daniele Rosa (CoRR, 2025)

Out-of-Distribution Detection Methods Answer the Wrong Questions. - Yucen Lily Li; Daohan Lu; Polina Kirichenko; Shikai Qiu; Tim G. J. Rudner; C. Bayan Bruss; Andrew Gordon Wilson (CoRR, 2025)