Brian Quanz

ORCID

Publication Activity (10 Years)

Years Active: 2008-2024
Publications (10 Years): 19

Top Venues

University of Kansas, USA (PHD)

Education History

University of Kansas, USA

PHD

Publications

I-Chi Chen, Harshdeep Singh, V. L. Anukruti, Brian Quanz, Kavitha Yogaraj
A Survey of Classical and Quantum Sequence Models. COMSNETS (2024)
I-Chi Chen, Harshdeep Singh, V. L. Anukruti, Brian Quanz, Kavitha Yogaraj
A Survey of Classical And Quantum Sequence Models. CoRR (2023)
Pavithra Harsha, Shivaram Subramanian, Ali Koc, Mahesh Ramakrishna, Brian Quanz, Dhruv Shah, Chandra Narayanaswami
An Optimistic-Robust Approach for Dynamic Positioning of Omnichannel Inventories. CoRR (2023)
Arindam Jati, Vijay Ekambaram, Shaonli Pal, Brian Quanz, Wesley M. Gifford, Pavithra Harsha, Stuart Siegel, Sumanta Mukherjee, Chandra Narayanaswami
Hierarchical Proxy Modeling for Improved HPO in Time Series Forecasting. KDD (2023)
Arindam Jati, Vijay Ekambaram, Shaonli Pal, Brian Quanz, Wesley M. Gifford, Pavithra Harsha, Stuart Siegel, Sumanta Mukherjee, Chandra Narayanaswami
Hierarchy-guided Model Selection for Time Series Forecasting. CoRR (2022)
Dhaval Salwala, Seshu Tirupathi, Brian Quanz, Wesley M. Gifford, Stuart Siegel, Vijay Ekambaram, Arindam Jati
Distributed Incremental Machine Learning for Big Time Series Data. Big Data (2022)
Celia Cintas, Payel Das, Brian Quanz, Girmaw Abebe Tadesse, Skyler Speakman, Pin-Yu Chen
Towards Creativity Characterization of Generative Models via Group-based Subset Scanning. CoRR (2022)
Celia Cintas, Payel Das, Brian Quanz, Girmaw Abebe Tadesse, Skyler Speakman, Pin-Yu Chen
Towards Creativity Characterization of Generative Models via Group-Based Subset Scanning. IJCAI (2022)
Yair Schiff, Brian Quanz, Payel Das, Pin-Yu Chen
Predicting Deep Neural Network Generalization with Perturbation Response Curves. CoRR (2021)
Brian Quanz, Ajay Deshpande, Dahai Xing, Xuan Liu
Learning to shortcut and shortlist order fulfillment deciding. CoRR (2021)
Yair Schiff, Brian Quanz, Payel Das, Pin-Yu Chen
Predicting Deep Neural Network Generalization with Perturbation Response Curves. NeurIPS (2021)
Celia Cintas, Payel Das, Brian Quanz, Skyler Speakman, Victor Akinwande, Pin-Yu Chen
Towards creativity characterization of generative models via group-based subset scanning. CoRR (2021)
Nam Nguyen, Brian Quanz
Temporal Latent Auto-Encoder: A Method for Probabilistic Multivariate Time Series Forecasting. AAAI (2021)
Nam Nguyen, Brian Quanz
Temporal Latent Auto-Encoder: A Method for Probabilistic Multivariate Time Series Forecasting. CoRR (2021)
Yair Schiff, Brian Quanz, Payel Das, Pin-Yu Chen
Gi and Pal Scores: Deep Neural Network Generalization Statistics. CoRR (2021)
Pavithra Harsha, Ashish Jagmohan, Jayant R. Kalagnanam, Brian Quanz, Divya Singhvi
Math Programming based Reinforcement Learning for Multi-Echelon Inventory Management. CoRR (2021)
Payel Das, Brian Quanz, Pin-Yu Chen, Jae-wook Ahn, Dhruv Shah
Toward a neuro-inspired creative decoder. IJCAI (2020)
Brian Quanz, Wei Sun, Ajay Deshpande, Dhruv Shah, Jae Eun Park
Machine learning based co-creative design framework. CoRR (2020)
Payel Das, Brian Quanz, Pin-Yu Chen, Jae-wook Ahn
Toward A Neuro-inspired Creative Decoder. CoRR (2019)
Brian Quanz, Jun Huan
CoNet: feature generation for multi-view semi-supervised learning with partially observed views. CIKM (2012)
Brian Quanz, Jun Huan
When Additional Views are Not Free: Active View Completion for Multi-view Semi-Supervised Learning. ICDM Workshops (2012)
Brian Quanz, Jun Huan, Meenakshi Mishra
Knowledge Transfer with Low-Quality Data: A Feature Extraction Issue. IEEE Trans. Knowl. Data Eng. 24 (10) (2012)
Brian Quanz, Jun Huan, Meenakshi Mishra
Knowledge transfer with low-quality data: A feature extraction issue. ICDE (2011)
Hongliang Fei, Brian Quanz, Jun Huan
Regularization and feature selection for networked features. CIKM (2010)
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, Angela Oguna
Anomaly Detection with Sensor Data for Distributed Security. ICCCN (2009)
Brian Quanz, Jun Huan
Aligned Graph Classification with Regularized Logistic Regression. SDM (2009)
Brian Quanz, Jun Huan
Large margin transductive transfer learning. CIKM (2009)
Hongliang Fei, Brian Quanz, Jun Huan
GLSVM: Integrating Structured Feature Selection and Large Margin Classification. ICDM Workshops (2009)
Brian Quanz, Meeyoung Park, Jun Huan
Biological pathways as features for microarray data classification. DTMBIO (2008)
Brian Quanz, Costas Tsatsoulis
Determining Object Safety Using a Multiagent, Collaborative System. SASO Workshops (2008)

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Publication Activity (10 Years)

Top Topics

Top Venues

Brian Quanz