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2020 – today
- 2024
[i24]Ravid Shwartz-Ziv, Micah Goldblum, Arpit Bansal, C. Bayan Bruss, Yann LeCun, Andrew Gordon Wilson:
Just How Flexible are Neural Networks in Practice? CoRR abs/2406.11463 (2024)- [i23]Andres Potapczynski, Shikai Qiu, Marc Finzi, Christopher Ferri, Zixi Chen, Micah Goldblum, C. Bayan Bruss, Christopher De Sa, Andrew Gordon Wilson:
Searching for Efficient Linear Layers over a Continuous Space of Structured Matrices. CoRR abs/2410.02117 (2024) - [i22]Alex Stein, Samuel Sharpe, Doron Bergman, Senthil Kumar, C. Bayan Bruss, John Dickerson, Tom Goldstein, Micah Goldblum:
A Simple Baseline for Predicting Events with Auto-Regressive Tabular Transformers. CoRR abs/2410.10648 (2024) - 2023
- [c11]Neha Mukund Kalibhat, Samuel Sharpe, Jeremy Goodsitt, C. Bayan Bruss, Soheil Feizi:
Adapting Self-Supervised Representations to Multi-Domain Setups. BMVC 2023: 353-355 - [c10]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: 279-287 - [c9]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 - [c8]Neha Mukund Kalibhat, Shweta Bhardwaj, C. Bayan Bruss, Hamed Firooz, Maziar Sanjabi, Soheil Feizi:
Identifying Interpretable Subspaces in Image Representations. ICML 2023: 15623-15638 - [c7]Kezhi Kong, Jiuhai Chen, John Kirchenbauer, Renkun Ni, C. Bayan Bruss, Tom Goldstein:
GOAT: A Global Transformer on Large-scale Graphs. ICML 2023: 17375-17390 - [c6]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 - [c5]Ravid Shwartz-Ziv, Micah Goldblum, Yucen Lily Li, C. Bayan Bruss, Andrew Gordon Wilson:
Simplifying Neural Network Training Under Class Imbalance. NeurIPS 2023 - [i21]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 abs/2304.03368 (2023) - [i20]Neha Mukund Kalibhat, Shweta Bhardwaj, C. Bayan Bruss, Hamed Firooz, Maziar Sanjabi, Soheil Feizi:
Identifying Interpretable Subspaces in Image Representations. CoRR abs/2307.10504 (2023) - [i19]Neha Mukund Kalibhat, Samuel Sharpe, Jeremy Goodsitt, C. Bayan Bruss, Soheil Feizi:
Adapting Self-Supervised Representations to Multi-Domain Setups. CoRR abs/2309.03999 (2023) - [i18]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 abs/2311.05877 (2023) - [i17]Ravid Shwartz-Ziv, Micah Goldblum, Yucen Lily Li, C. Bayan Bruss, Andrew Gordon Wilson:
Simplifying Neural Network Training Under Class Imbalance. CoRR abs/2312.02517 (2023) - 2022
- [i16]Nikita Seleznev, Senthil Kumar, C. Bayan Bruss:
Double-Hashing Algorithm for Frequency Estimation in Data Streams. CoRR abs/2204.00650 (2022) - [i15]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 abs/2206.15306 (2022) - [i14]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 abs/2207.05566 (2022) - 2021
- [i13]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 abs/2106.01342 (2021) - [i12]Arpit Bansal, Micah Goldblum, Valeriia Cherepanova, Avi Schwarzschild, C. Bayan Bruss, Tom Goldstein:
MetaBalance: High-Performance Neural Networks for Class-Imbalanced Data. CoRR abs/2106.09643 (2021) - [i11]Brian Barr, Matthew R. Harrington, Samuel Sharpe, C. Bayan Bruss:
Counterfactual Explanations via Latent Space Projection and Interpolation. CoRR abs/2112.00890 (2021) - 2020
- [c4]Antonia Gogoglou, Brian Nguyen, Alan Salimov, Jonathan B. Rider, C. Bayan Bruss:
Navigating the dynamics of financial embeddings over time. ICAIF 2020: 21:1-21:8 - [c3]Antonia Gogoglou, C. Bayan Bruss, Brian Nguyen, Reza Sarshogh, Keegan E. Hines:
Quantifying Challenges in the Application of Graph Representation Learning. ICMLA 2020: 1519-1526 - [i10]Antonia Gogoglou, C. Bayan Bruss, Brian Nguyen, Reza Sarshogh, Keegan E. Hines:
Quantifying Challenges in the Application of Graph Representation Learning. CoRR abs/2006.10252 (2020) - [i9]Antonia Gogoglou, Brian Nguyen, Alan Salimov, Jonathan Rider, C. Bayan Bruss:
Navigating the Dynamics of Financial Embeddings over Time. CoRR abs/2007.00591 (2020) - [i8]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 abs/2007.10532 (2020) - [i7]Jason D. Wittenbach, Brian d'Alessandro, C. Bayan Bruss:
Machine Learning for Temporal Data in Finance: Challenges and Opportunities. CoRR abs/2009.05636 (2020) - [i6]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 abs/2010.01693 (2020) - [i5]Rachana Balasubramanian, Samuel Sharpe, Brian Barr, Jason D. Wittenbach, C. Bayan Bruss:
Latent-CF: A Simple Baseline for Reverse Counterfactual Explanations. CoRR abs/2012.09301 (2020)
2010 – 2019
- 2019
- [c2]Anish Khazane, Jonathan Rider, Max Serpe, Antonia Gogoglou, Keegan E. Hines, C. Bayan Bruss, Richard Serpe:
DeepTrax: Embedding Graphs of Financial Transactions. ICMLA 2019: 126-133 - [c1]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: 1471-1479 - [i4]C. Bayan Bruss, Anish Khazane, Jonathan Rider, Richard Serpe, Saurabh Nagrecha, Keegan E. Hines:
Graph Embeddings at Scale. CoRR abs/1907.01705 (2019) - [i3]C. Bayan Bruss, Anish Khazane, Jonathan Rider, Richard Serpe, Antonia Gogoglou, Keegan E. Hines:
DeepTrax: Embedding Graphs of Financial Transactions. CoRR abs/1907.07225 (2019) - [i2]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 abs/1908.05557 (2019) - [i1]Antonia Gogoglou, C. Bayan Bruss, Keegan E. Hines:
On the Interpretability and Evaluation of Graph Representation Learning. CoRR abs/1910.03081 (2019)
Coauthor Index
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