Brian Quanz

According to our database, Brian Quanz authored at least 29 papers between 2008 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

A Survey of Classical and Quantum Sequence Models.

I-Chi Chen, Harshdeep Singh, V. L. Anukruti, Brian Quanz, Kavitha Yogaraj

Proceedings of the 16th International Conference on COMmunication Systems & NETworkS, 2024

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

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

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

End-to-End Learning for Optimization via Constraint-Enforcing Approximators.

Rares Cristian, Pavithra Harsha, Georgia Perakis, Brian Leo Quanz, Ioannis Spantidakis

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

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

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

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

Distributed Incremental Machine Learning for Big Time Series Data.

Dhaval Salwala, Seshu Tirupathi, Brian Quanz, Wesley M. Gifford, Stuart Siegel, Vijay Ekambaram, Arindam Jati

Proceedings of the IEEE International Conference on Big Data, 2022

2021

Math Programming based Reinforcement Learning for Multi-Echelon Inventory Management.

Pavithra Harsha, Ashish Jagmohan, Jayant R. Kalagnanam, Brian Quanz, Divya Singhvi

CoRR, 2021

Learning to shortcut and shortlist order fulfillment deciding.

Brian Quanz, Ajay Deshpande, Dahai Xing, Xuan Liu

CoRR, 2021

Gi and Pal Scores: Deep Neural Network Generalization Statistics.

Yair Schiff, Brian Quanz, Payel Das, Pin-Yu Chen

CoRR, 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

Predicting Deep Neural Network Generalization with Perturbation Response Curves.

Yair Schiff, Brian Quanz, Payel Das, Pin-Yu Chen

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Temporal Latent Auto-Encoder: A Method for Probabilistic Multivariate Time Series Forecasting.

Nam Nguyen, Brian Quanz

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Practical application improvement to Quantum SVM: theory to practice.

Jae Eun Park, Brian Quanz, Steve Wood, Heather Higgins, Ray Harishankar

CoRR, 2020

Machine learning based co-creative design framework.

Brian Quanz, Wei Sun, Ajay Deshpande, Dhruv Shah, Jae Eun Park

CoRR, 2020

Toward a neuro-inspired creative decoder.

Payel Das, Brian Quanz, Pin-Yu Chen, Jae-wook Ahn, Dhruv Shah

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

2019

Toward A Neuro-inspired Creative Decoder.

Payel Das, Brian Quanz, Pin-Yu Chen, Jae-wook Ahn

CoRR, 2019

2018

A Local Algorithm for Product Return Prediction in E-Commerce.

Yada Zhu, Jianbo Li, Jingrui He, Brian Leo Quanz, Ajay A. Deshpande

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

2012

Learning with Low-Quality Data: Multi-View Semi-Supervised Learning with Missing Views.

Brian Quanz

PhD thesis, 2012

Knowledge Transfer with Low-Quality Data: A Feature Extraction Issue.

Brian Quanz, Jun Huan, Meenakshi Mishra

IEEE Trans. Knowl. Data Eng., 2012

When Additional Views are Not Free: Active View Completion for Multi-view Semi-Supervised Learning.

Brian Quanz, Jun Huan

Proceedings of the 12th IEEE International Conference on Data Mining Workshops, 2012

CoNet: feature generation for multi-view semi-supervised learning with partially observed views.

Brian Quanz, Jun Huan

Proceedings of the 21st ACM International Conference on Information and Knowledge Management, 2012

2010

Regularization and feature selection for networked features.

Hongliang Fei, Brian Quanz, Jun Huan

Proceedings of the 19th ACM Conference on Information and Knowledge Management, 2010

2009

Aligned Graph Classification with Regularized Logistic Regression.

Brian Quanz, Jun Huan

Proceedings of the SIAM International Conference on Data Mining, 2009

GLSVM: Integrating Structured Feature Selection and Large Margin Classification.

Hongliang Fei, Brian Quanz, Jun Huan

Proceedings of the ICDM Workshops 2009, 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, Angela Oguna

Proceedings of the 18th International Conference on Computer Communications and Networks, 2009

Large margin transductive transfer learning.

Brian Quanz, Jun Huan

Proceedings of the 18th ACM Conference on Information and Knowledge Management, 2009

2008

Determining Object Safety Using a Multiagent, Collaborative System.

Brian Quanz, Costas Tsatsoulis

Proceedings of the Second IEEE International Conference on Self-Adaptive and Self-Organizing Systems, 2008

Biological pathways as features for microarray data classification.

Brian Quanz, Meeyoung Park, Jun Huan

Proceedings of the Proceeding of the 2nd International Workshop on Data and Text Mining in Bioinformatics, 2008