C. Bayan Bruss

Publication Activity (10 Years)

Years Active: 2019-2024
Publications (10 Years): 33

Top Topics

Class Imbalance

Graph Representation

Feature Selection

Top Venues

Publications

Ravid Shwartz-Ziv, Micah Goldblum, Arpit Bansal, C. Bayan Bruss, Yann LeCun, Andrew Gordon Wilson
Just How Flexible are Neural Networks in Practice? CoRR (2024)
Xueying Ding, Nikita Seleznev, Senthil Kumar, C. Bayan Bruss, Leman Akoglu
From Explanation to Action: An End-to-End Human-in-the-loop Framework for Anomaly Reasoning and Management. CoRR (2023)
Roman Levin, Valeriia Cherepanova, Avi Schwarzschild, Arpit Bansal, C. Bayan Bruss, Tom Goldstein, Andrew Gordon Wilson, Micah Goldblum
Transfer Learning with Deep Tabular Models. ICLR (2023)
Kezhi Kong, Jiuhai Chen, John Kirchenbauer, Renkun Ni, C. Bayan Bruss, Tom Goldstein
GOAT: A Global Transformer on Large-scale Graphs. ICML (2023)
Valeriia Cherepanova, Roman Levin, Gowthami Somepalli, Jonas Geiping, C. Bayan Bruss, Andrew Gordon Wilson, Tom Goldstein, Micah Goldblum
A Performance-Driven Benchmark for Feature Selection in Tabular Deep Learning. CoRR (2023)
Ravid Shwartz-Ziv, Micah Goldblum, Yucen Lily Li, C. Bayan Bruss, Andrew Gordon Wilson
Simplifying Neural Network Training Under Class Imbalance. NeurIPS (2023)
Neha Mukund Kalibhat, Shweta Bhardwaj, C. Bayan Bruss, Hamed Firooz, Maziar Sanjabi, Soheil Feizi
Identifying Interpretable Subspaces in Image Representations. CoRR (2023)
Xueying Ding, Nikita Seleznev, Senthil Kumar, C. Bayan Bruss, Leman Akoglu
From Detection to Action: a Human-in-the-loop Toolkit for Anomaly Reasoning and Management. ICAIF (2023)
Neha Mukund Kalibhat, Shweta Bhardwaj, C. Bayan Bruss, Hamed Firooz, Maziar Sanjabi, Soheil Feizi
Identifying Interpretable Subspaces in Image Representations. ICML (2023)
Neha Mukund Kalibhat, Samuel Sharpe, Jeremy Goodsitt, C. Bayan Bruss, Soheil Feizi
Adapting Self-Supervised Representations to Multi-Domain Setups. CoRR (2023)
Neha Mukund Kalibhat, Samuel Sharpe, Jeremy Goodsitt, C. Bayan Bruss, Soheil Feizi
Adapting Self-Supervised Representations to Multi-Domain Setups. BMVC (2023)
Valeriia Cherepanova, Roman Levin, Gowthami Somepalli, Jonas Geiping, C. Bayan Bruss, Andrew Gordon Wilson, Tom Goldstein, Micah Goldblum
A Performance-Driven Benchmark for Feature Selection in Tabular Deep Learning. NeurIPS (2023)
Ravid Shwartz-Ziv, Micah Goldblum, Yucen Lily Li, C. Bayan Bruss, Andrew Gordon Wilson
Simplifying Neural Network Training Under Class Imbalance. CoRR (2023)
Nikita Seleznev, Senthil Kumar, C. Bayan Bruss
Double-Hashing Algorithm for Frequency Estimation in Data Streams. CoRR (2022)
Isha Hameed, Samuel Sharpe, Daniel Barcklow, Justin Au-Yeung, Sahil Verma, Jocelyn Huang, Brian Barr, C. Bayan Bruss
BASED-XAI: Breaking Ablation Studies Down for Explainable Artificial Intelligence. CoRR (2022)
Roman Levin, Valeriia Cherepanova, Avi Schwarzschild, Arpit Bansal, C. Bayan Bruss, Tom Goldstein, Andrew Gordon Wilson, Micah Goldblum
Transfer Learning with Deep Tabular Models. CoRR (2022)
Arpit Bansal, Micah Goldblum, Valeriia Cherepanova, Avi Schwarzschild, C. Bayan Bruss, Tom Goldstein
MetaBalance: High-Performance Neural Networks for Class-Imbalanced Data. CoRR (2021)
Gowthami Somepalli, Micah Goldblum, Avi Schwarzschild, C. Bayan Bruss, Tom Goldstein
SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training. CoRR (2021)
Brian Barr, Matthew R. Harrington, Samuel Sharpe, C. Bayan Bruss
Counterfactual Explanations via Latent Space Projection and Interpolation. CoRR (2021)
Rachana Balasubramanian, Samuel Sharpe, Brian Barr, Jason D. Wittenbach, C. Bayan Bruss
Latent-CF: A Simple Baseline for Reverse Counterfactual Explanations. CoRR (2020)
Antonia Gogoglou, Brian Nguyen, Alan Salimov, Jonathan B. Rider, C. Bayan Bruss
Navigating the dynamics of financial embeddings over time. ICAIF (2020)
Antonia Gogoglou, C. Bayan Bruss, Brian Nguyen, Reza Sarshogh, Keegan E. Hines
Quantifying Challenges in the Application of Graph Representation Learning. ICMLA (2020)
Brian Barr, Ke Xu, Cláudio T. Silva, Enrico Bertini, Robert Reilly, C. Bayan Bruss, Jason D. Wittenbach
Towards Ground Truth Explainability on Tabular Data. CoRR (2020)
Oluwatobi O. Olabiyi, Prarthana Bhattarai, C. Bayan Bruss, Zachary Kulis
DLGNet-Task: An End-to-end Neural Network Framework for Modeling Multi-turn Multi-domain Task-Oriented Dialogue. CoRR (2020)
Antonia Gogoglou, Brian Nguyen, Alan Salimov, Jonathan Rider, C. Bayan Bruss
Navigating the Dynamics of Financial Embeddings over Time. CoRR (2020)
Jason D. Wittenbach, Brian d'Alessandro, C. Bayan Bruss
Machine Learning for Temporal Data in Finance: Challenges and Opportunities. CoRR (2020)
Antonia Gogoglou, C. Bayan Bruss, Brian Nguyen, Reza Sarshogh, Keegan E. Hines
Quantifying Challenges in the Application of Graph Representation Learning. CoRR (2020)
Anh Truong, Austin Walters, Jeremy Goodsitt, Keegan E. Hines, C. Bayan Bruss, Reza Farivar
Towards Automated Machine Learning: Evaluation and Comparison of AutoML Approaches and Tools. CoRR (2019)
C. Bayan Bruss, Anish Khazane, Jonathan Rider, Richard Serpe, Saurabh Nagrecha, Keegan E. Hines
Graph Embeddings at Scale. CoRR (2019)
Anish Khazane, Jonathan Rider, Max Serpe, Antonia Gogoglou, Keegan E. Hines, C. Bayan Bruss, Richard Serpe
DeepTrax: Embedding Graphs of Financial Transactions. ICMLA (2019)
C. Bayan Bruss, Anish Khazane, Jonathan Rider, Richard Serpe, Antonia Gogoglou, Keegan E. Hines
DeepTrax: Embedding Graphs of Financial Transactions. CoRR (2019)
Anh Truong, Austin Walters, Jeremy Goodsitt, Keegan E. Hines, C. Bayan Bruss, Reza Farivar
Towards Automated Machine Learning: Evaluation and Comparison of AutoML Approaches and Tools. ICTAI (2019)
Antonia Gogoglou, C. Bayan Bruss, Keegan E. Hines
On the Interpretability and Evaluation of Graph Representation Learning. CoRR (2019)