Biological pathways as features for microarray data classification. - Brian Quanz; Meeyoung Park; Jun Huan (DTMBIO, 2008)

Determining Object Safety Using a Multiagent, Collaborative System. - Brian Quanz; Costas Tsatsoulis (SASO Workshops, 2008)

Large margin transductive transfer learning. - Brian Quanz; Jun Huan (CIKM, 2009)

Anomaly Detection with Sensor Data for Distributed Security. - Brian Quanz; Hongliang Fei; Jun Huan; Joseph B. Evans; Victor Frost; Gary J. Minden; Daniel D. Deavours; Leon S. Searl; Daniel DePardo; Martin Kuehnhausen; Daniel Fokum; Matt Zeets; et al. (ICCCN, 2009)

GLSVM: Integrating Structured Feature Selection and Large Margin Classification. - Hongliang Fei; Brian Quanz; Jun Huan (ICDM Workshops, 2009)

Aligned Graph Classification with Regularized Logistic Regression. - Brian Quanz; Jun Huan (SDM, 2009)

Regularization and feature selection for networked features. - Hongliang Fei; Brian Quanz; Jun Huan (CIKM, 2010)

CoNet: feature generation for multi-view semi-supervised learning with partially observed views. - Brian Quanz; Jun Huan (CIKM, 2012)

When Additional Views are Not Free: Active View Completion for Multi-view Semi-Supervised Learning. - Brian Quanz; Jun Huan (ICDM Workshops, 2012)

Knowledge Transfer with Low-Quality Data: A Feature Extraction Issue. - Brian Quanz; Jun Huan; Meenakshi Mishra (IEEE Trans. Knowl. Data Eng., 2012)

Learning with Low-Quality Data: Multi-View Semi-Supervised Learning with Missing Views. - Brian Quanz (2012)

A Local Algorithm for Product Return Prediction in E-Commerce. - Yada Zhu; Jianbo Li; Jingrui He; Brian Leo Quanz; Ajay A. Deshpande (IJCAI, 2018)

Toward A Neuro-inspired Creative Decoder. - Payel Das; Brian Quanz; Pin-Yu Chen; Jae-wook Ahn (CoRR, 2019)

Toward a neuro-inspired creative decoder. - Payel Das; Brian Quanz; Pin-Yu Chen; Jae-wook Ahn; Dhruv Shah (IJCAI, 2020)

Machine learning based co-creative design framework. - Brian Quanz; Wei Sun 031; Ajay Deshpande; Dhruv Shah; Jae Eun Park (CoRR, 2020)

Practical application improvement to Quantum SVM: theory to practice. - Jae Eun Park; Brian Quanz; Steve Wood; Heather Higgins; Ray Harishankar (CoRR, 2020)

Temporal Latent Auto-Encoder: A Method for Probabilistic Multivariate Time Series Forecasting. - Nam Nguyen; Brian Quanz (AAAI, 2021)

Predicting Deep Neural Network Generalization with Perturbation Response Curves. - Yair Schiff; Brian Quanz; Payel Das; Pin-Yu Chen (NeurIPS, 2021)

Towards creativity characterization of generative models via group-based subset scanning. - Celia Cintas; Payel Das; Brian Quanz; Skyler Speakman; Victor Akinwande; Pin-Yu Chen (CoRR, 2021)

Gi and Pal Scores: Deep Neural Network Generalization Statistics. - Yair Schiff; Brian Quanz; Payel Das; Pin-Yu Chen (CoRR, 2021)

Learning to shortcut and shortlist order fulfillment deciding. - Brian Quanz; Ajay Deshpande; Dahai Xing; Xuan Liu (CoRR, 2021)

Math Programming based Reinforcement Learning for Multi-Echelon Inventory Management. - Pavithra Harsha; Ashish Jagmohan; Jayant R. Kalagnanam; Brian Quanz; Divya Singhvi (CoRR, 2021)

Distributed Incremental Machine Learning for Big Time Series Data. - Dhaval Salwala; Seshu Tirupathi; Brian Quanz; Wesley M. Gifford; Stuart Siegel; Vijay Ekambaram; Arindam Jati (IEEE Big Data, 2022)

Towards Creativity Characterization of Generative Models via Group-Based Subset Scanning. - Celia Cintas; Payel Das; Brian Quanz; Girmaw Abebe Tadesse; Skyler Speakman; Pin-Yu Chen (IJCAI, 2022)

Hierarchy-guided Model Selection for Time Series Forecasting. - Arindam Jati; Vijay Ekambaram; Shaonli Pal; Brian Quanz; Wesley M. Gifford; Pavithra Harsha; Stuart Siegel; Sumanta Mukherjee; Chandra Narayanaswami (CoRR, 2022)

End-to-End Learning for Optimization via Constraint-Enforcing Approximators. - Rares Cristian; Pavithra Harsha; Georgia Perakis; Brian Leo Quanz; Ioannis Spantidakis (AAAI, 2023)

Hierarchical Proxy Modeling for Improved HPO in Time Series Forecasting. - Arindam Jati; Vijay Ekambaram; Shaonli Pal; Brian Quanz; Wesley M. Gifford; Pavithra Harsha; Stuart Siegel; Sumanta Mukherjee; Chandra Narayanaswami (KDD, 2023)

An Optimistic-Robust Approach for Dynamic Positioning of Omnichannel Inventories. - Pavithra Harsha; Shivaram Subramanian; Ali Koc; Mahesh Ramakrishna; Brian Quanz; Dhruv Shah; Chandra Narayanaswami (CoRR, 2023)

A Survey of Classical and Quantum Sequence Models. - I-Chi Chen; Harshdeep Singh; V. L. Anukruti; Brian Quanz; Kavitha Yogaraj (COMSNETS, 2024)

Inter-Series Transformer: Attending to Products in Time Series Forecasting. - Rares Cristian; Pavithra Harsha; Clemente Ocejo; Georgia Perakis; Brian Quanz; Ioannis Spantidakis; Hamza Zerhouni (CoRR, 2024)

Deep Policy Iteration with Integer Programming for Inventory Management. - Pavithra Harsha; Ashish Jagmohan; Jayant Kalagnanam; Brian Quanz; Divya Singhvi (Manuf. Serv. Oper. Manag., 2025)

CoRe: Coherency Regularization for Hierarchical Time Series. - Rares Cristian; Pavithra Harhsa; Georgia Perakis; Brian Quanz (CoRR, 2025)

Efficient End-to-End Learning for Decision-Making: A Meta-Optimization Approach. - Rares Cristian; Pavithra Harsha; Georgia Perakis; Brian Quanz (CoRR, 2025)

Aligning Learning and Endogenous Decision-Making. - Rares Cristian; Pavithra Harsha; Georgia Perakis; Brian Quanz (CoRR, 2025)