C. Bayan Bruss

46 papers A* 6A 1B 1C 2Journal 33Unranked 3

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	PersonaLedger: Generating Realistic Financial Transactions with Persona Conditioned LLMs and Rule Grounded Feedback. CoRR Dehao Yuan, Tyler Farnan, Stefan Tesliuc, Doron L. Bergman, Yulun Wu, Xiaoyu Liu, Minghui Liu, James Montgomery, Nam H. Nguyen, C. Bayan Bruss, Furong Huang
2026	J	`jnl`	Revisiting RAG Retrievers: An Information Theoretic Benchmark. CoRR Wenqing Zheng, Dmitri Kalaev, Noah Fatsi, Daniel Barcklow, Owen Reinert, Igor Melnyk, Senthil Kumar, C. Bayan Bruss
2026	J	`jnl`	TimeSqueeze: Dynamic Patching for Efficient Time Series Forecasting. CoRR Sravan Kumar Ankireddy, Nikita Seleznev, Nam H. Nguyen, Yulun Wu, Senthil Kumar, Furong Huang, C. Bayan Bruss
2025	J	`jnl`	BEDTime: A Unified Benchmark for Automatically Describing Time Series. CoRR Medhasweta Sen, Zachary Gottesman, Jiaxing Qiu, C. Bayan Bruss, Nam Nguyen, Tom Hartvigsen
2025	J	`jnl`	Bridging the Divide: End-to-End Sequence-Graph Learning. CoRR Yuen Chen, Yulun Wu, Samuel Sharpe, Igor Melnyk, Nam H. Nguyen, Furong Huang, C. Bayan Bruss, Rizal Fathony
2025	J	`jnl`	DynaGuard: A Dynamic Guardrail Model With User-Defined Policies. CoRR Monte Hoover, Vatsal Baherwani, Neel Jain, Khalid Saifullah, Joseph Vincent, Chirag Jain, Melissa Kazemi Rad, C. Bayan Bruss, Ashwinee Panda, Tom Goldstein
2025	J	`jnl`	Explain What You Mean: Intent Augmented Knowledge Graph Recommender Built With An LLM. CoRR Wenqing Zheng, Noah Fatsi, Daniel Barcklow, Dmitri Kalaev, Steven Yao, Owen Reinert, C. Bayan Bruss, Daniele Rosa
2025	J	`jnl`	Integrating Sequential and Relational Modeling for User Events: Datasets and Prediction Tasks. CoRR Rizal Fathony, Igor Melnyk, Owen Reinert, Nam H. Nguyen, Daniele Rosa, C. Bayan Bruss
2025	J	`jnl`	Out-of-Distribution Detection Methods Answer the Wrong Questions. CoRR Yucen Lily Li, Daohan Lu, Polina Kirichenko, Shikai Qiu, Tim G. J. Rudner, C. Bayan Bruss, Andrew Gordon Wilson
2025	—	`conf`	Position: Supervised Classifiers Answer the Wrong Questions for OOD Detection. ICML (Position Papers) Yucen Lily Li, Daohan Lu, Polina Kirichenko, Shikai Qiu, Tim G. J. Rudner, C. Bayan Bruss, Andrew Gordon Wilson
2024	J	`jnl`	A Simple Baseline for Predicting Events with Auto-Regressive Tabular Transformers. CoRR Alex Stein, Samuel Sharpe, Doron Bergman, Senthil Kumar, C. Bayan Bruss, John Dickerson, Tom Goldstein, Micah Goldblum
2024	J	`jnl`	Just How Flexible are Neural Networks in Practice? CoRR Ravid Shwartz-Ziv, Micah Goldblum, Arpit Bansal, C. Bayan Bruss, Yann LeCun, Andrew Gordon Wilson
2024	A*	`conf`	Searching for Efficient Linear Layers over a Continuous Space of Structured Matrices. NeurIPS Andres Potapczynski, Shikai Qiu, Marc Finzi, Christopher Ferri, Charlie Chen, Micah Goldblum, C. Bayan Bruss, Christopher De Sa, Andrew Gordon Wilson
2024	J	`jnl`	Searching for Efficient Linear Layers over a Continuous Space of Structured Matrices. CoRR Andres Potapczynski, Shikai Qiu, Marc Finzi, Christopher Ferri, Zixi Chen, Micah Goldblum, C. Bayan Bruss, Christopher De Sa, Andrew Gordon Wilson
2023	A*	`conf`	A Performance-Driven Benchmark for Feature Selection in Tabular Deep Learning. NeurIPS Valeriia Cherepanova, Roman Levin, Gowthami Somepalli, Jonas Geiping, C. Bayan Bruss, Andrew Gordon Wilson, Tom Goldstein, Micah Goldblum
2023	J	`jnl`	A Performance-Driven Benchmark for Feature Selection in Tabular Deep Learning. CoRR Valeriia Cherepanova, Roman Levin, Gowthami Somepalli, Jonas Geiping, C. Bayan Bruss, Andrew Gordon Wilson, Tom Goldstein, Micah Goldblum
2023	A	`conf`	Adapting Self-Supervised Representations to Multi-Domain Setups. BMVC Neha Mukund Kalibhat, Samuel Sharpe, Jeremy Goodsitt, C. Bayan Bruss, Soheil Feizi
2023	J	`jnl`	Adapting Self-Supervised Representations to Multi-Domain Setups. CoRR Neha Mukund Kalibhat, Samuel Sharpe, Jeremy Goodsitt, C. Bayan Bruss, Soheil Feizi
2023	—	`conf`	From Detection to Action: a Human-in-the-loop Toolkit for Anomaly Reasoning and Management. ICAIF Xueying Ding, Nikita Seleznev, Senthil Kumar, C. Bayan Bruss, Leman Akoglu
2023	J	`jnl`	From Explanation to Action: An End-to-End Human-in-the-loop Framework for Anomaly Reasoning and Management. CoRR Xueying Ding, Nikita Seleznev, Senthil Kumar, C. Bayan Bruss, Leman Akoglu
2023	A*	`conf`	GOAT: A Global Transformer on Large-scale Graphs. ICML Kezhi Kong, Jiuhai Chen, John Kirchenbauer, Renkun Ni, C. Bayan Bruss, Tom Goldstein
2023	A*	`conf`	Identifying Interpretable Subspaces in Image Representations. ICML Neha Mukund Kalibhat, Shweta Bhardwaj, C. Bayan Bruss, Hamed Firooz, Maziar Sanjabi, Soheil Feizi
2023	J	`jnl`	Identifying Interpretable Subspaces in Image Representations. CoRR Neha Mukund Kalibhat, Shweta Bhardwaj, C. Bayan Bruss, Hamed Firooz, Maziar Sanjabi, Soheil Feizi
2023	A*	`conf`	Simplifying Neural Network Training Under Class Imbalance. NeurIPS Ravid Shwartz-Ziv, Micah Goldblum, Yucen Lily Li, C. Bayan Bruss, Andrew Gordon Wilson
2023	J	`jnl`	Simplifying Neural Network Training Under Class Imbalance. CoRR Ravid Shwartz-Ziv, Micah Goldblum, Yucen Lily Li, C. Bayan Bruss, Andrew Gordon Wilson
2023	A*	`conf`	Transfer Learning with Deep Tabular Models. ICLR Roman Levin, Valeriia Cherepanova, Avi Schwarzschild, Arpit Bansal, C. Bayan Bruss, Tom Goldstein, Andrew Gordon Wilson, Micah Goldblum
2022	J	`jnl`	BASED-XAI: Breaking Ablation Studies Down for Explainable Artificial Intelligence. CoRR Isha Hameed, Samuel Sharpe, Daniel Barcklow, Justin Au-Yeung, Sahil Verma, Jocelyn Huang, Brian Barr, C. Bayan Bruss
2022	J	`jnl`	Double-Hashing Algorithm for Frequency Estimation in Data Streams. CoRR Nikita Seleznev, Senthil Kumar, C. Bayan Bruss
2022	J	`jnl`	Transfer Learning with Deep Tabular Models. CoRR Roman Levin, Valeriia Cherepanova, Avi Schwarzschild, Arpit Bansal, C. Bayan Bruss, Tom Goldstein, Andrew Gordon Wilson, Micah Goldblum
2021	J	`jnl`	Counterfactual Explanations via Latent Space Projection and Interpolation. CoRR Brian Barr, Matthew R. Harrington, Samuel Sharpe, C. Bayan Bruss
2021	J	`jnl`	MetaBalance: High-Performance Neural Networks for Class-Imbalanced Data. CoRR Arpit Bansal, Micah Goldblum, Valeriia Cherepanova, Avi Schwarzschild, C. Bayan Bruss, Tom Goldstein
2021	J	`jnl`	SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training. CoRR Gowthami Somepalli, Micah Goldblum, Avi Schwarzschild, C. Bayan Bruss, Tom Goldstein
2020	J	`jnl`	DLGNet-Task: An End-to-end Neural Network Framework for Modeling Multi-turn Multi-domain Task-Oriented Dialogue. CoRR Oluwatobi O. Olabiyi, Prarthana Bhattarai, C. Bayan Bruss, Zachary Kulis
2020	J	`jnl`	Latent-CF: A Simple Baseline for Reverse Counterfactual Explanations. CoRR Rachana Balasubramanian, Samuel Sharpe, Brian Barr, Jason D. Wittenbach, C. Bayan Bruss
2020	J	`jnl`	Machine Learning for Temporal Data in Finance: Challenges and Opportunities. CoRR Jason D. Wittenbach, Brian D'Alessandro, C. Bayan Bruss
2020	J	`jnl`	Navigating the Dynamics of Financial Embeddings over Time. CoRR Antonia Gogoglou, Brian Nguyen, Alan Salimov, Jonathan Rider, C. Bayan Bruss
2020	—	`conf`	Navigating the dynamics of financial embeddings over time. ICAIF Antonia Gogoglou, Brian Nguyen, Alan Salimov, Jonathan B. Rider, C. Bayan Bruss
2020	C	`conf`	Quantifying Challenges in the Application of Graph Representation Learning. ICMLA Antonia Gogoglou, C. Bayan Bruss, Brian Nguyen, Reza Sarshogh, Keegan E. Hines
2020	J	`jnl`	Quantifying Challenges in the Application of Graph Representation Learning. CoRR Antonia Gogoglou, C. Bayan Bruss, Brian Nguyen, Reza Sarshogh, Keegan E. Hines
2020	J	`jnl`	Towards Ground Truth Explainability on Tabular Data. CoRR Brian Barr, Ke Xu, Cláudio T. Silva, Enrico Bertini, Robert Reilly, C. Bayan Bruss, Jason D. Wittenbach
2019	C	`conf`	DeepTrax: Embedding Graphs of Financial Transactions. ICMLA Anish Khazane, Jonathan Rider, Max Serpe, Antonia Gogoglou, Keegan E. Hines, C. Bayan Bruss, Richard Serpe
2019	J	`jnl`	DeepTrax: Embedding Graphs of Financial Transactions. CoRR C. Bayan Bruss, Anish Khazane, Jonathan Rider, Richard Serpe, Antonia Gogoglou, Keegan E. Hines
2019	J	`jnl`	Graph Embeddings at Scale. CoRR C. Bayan Bruss, Anish Khazane, Jonathan Rider, Richard Serpe, Saurabh Nagrecha, Keegan E. Hines
2019	J	`jnl`	On the Interpretability and Evaluation of Graph Representation Learning. CoRR Antonia Gogoglou, C. Bayan Bruss, Keegan E. Hines
2019	B	`conf`	Towards Automated Machine Learning: Evaluation and Comparison of AutoML Approaches and Tools. ICTAI Anh Truong, Austin Walters, Jeremy Goodsitt, Keegan E. Hines, C. Bayan Bruss, Reza Farivar
2019	J	`jnl`	Towards Automated Machine Learning: Evaluation and Comparison of AutoML Approaches and Tools. CoRR Anh Truong, Austin Walters, Jeremy Goodsitt, Keegan E. Hines, C. Bayan Bruss, Reza Farivar