C. Bayan Bruss

According to our database, C. Bayan Bruss authored at least 26 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

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

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

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

Simplifying Neural Network Training Under Class Imbalance.

Ravid Shwartz-Ziv, Micah Goldblum, Yucen Lily Li, C. Bayan Bruss, Andrew Gordon Wilson

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 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

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

GOAT: A Global Transformer on Large-scale Graphs.

Kezhi Kong, Jiuhai Chen, John Kirchenbauer, Renkun Ni, C. Bayan Bruss, Tom Goldstein

Proceedings of the International Conference on Machine Learning, 2023

Identifying Interpretable Subspaces in Image Representations.

Neha Mukund Kalibhat, Shweta Bhardwaj, C. Bayan Bruss, Hamed Firooz, Maziar Sanjabi, Soheil Feizi

Proceedings of the International Conference on Machine Learning, 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

Proceedings of the Eleventh International Conference on Learning Representations, 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

Proceedings of the 4th ACM International Conference on AI in Finance, 2023

Adapting Self-Supervised Representations to Multi-Domain Setups.

Neha Mukund Kalibhat, Samuel Sharpe, Jeremy Goodsitt, C. Bayan Bruss, Soheil Feizi

Proceedings of the 34th British Machine Vision Conference 2023, 2023

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

Double-Hashing Algorithm for Frequency Estimation in Data Streams.

Nikita Seleznev, Senthil Kumar, C. Bayan Bruss

CoRR, 2022

2021

Counterfactual Explanations via Latent Space Projection and Interpolation.

Brian Barr, Matthew R. Harrington, Samuel Sharpe, C. Bayan Bruss

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

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

2020

Latent-CF: A Simple Baseline for Reverse Counterfactual Explanations.

Rachana Balasubramanian, Samuel Sharpe, Brian Barr, Jason D. Wittenbach, 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

Machine Learning for Temporal Data in Finance: Challenges and Opportunities.

Jason D. Wittenbach, Brian d'Alessandro, C. Bayan Bruss

CoRR, 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

Quantifying Challenges in the Application of Graph Representation Learning.

Antonia Gogoglou, C. Bayan Bruss, Brian Nguyen, Reza Sarshogh, Keegan E. Hines

Proceedings of the 19th IEEE International Conference on Machine Learning and Applications, 2020

Navigating the dynamics of financial embeddings over time.

Antonia Gogoglou, Brian Nguyen, Alan Salimov, Jonathan B. Rider, C. Bayan Bruss

Proceedings of the ICAIF '20: The First ACM International Conference on AI in Finance, 2020

2019

On the Interpretability and Evaluation of Graph Representation Learning.

Antonia Gogoglou, C. Bayan Bruss, 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

Graph Embeddings at Scale.

C. Bayan Bruss, Anish Khazane, Jonathan Rider, Richard Serpe, Saurabh Nagrecha, Keegan E. Hines

CoRR, 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

Proceedings of the 31st IEEE International Conference on Tools with Artificial Intelligence, 2019

DeepTrax: Embedding Graphs of Financial Transactions.

Anish Khazane, Jonathan Rider, Max Serpe, Antonia Gogoglou, Keegan E. Hines, C. Bayan Bruss, Richard Serpe

Proceedings of the 18th IEEE International Conference On Machine Learning And Applications, 2019