Brian Quanz - RankMe

38 papers A* 6A 4B 2Misc 1Journal 20Unranked 4

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Aligning Learning and Endogenous Decision-Making. CoRR Rares Cristian, Pavithra Harsha, Georgia Perakis, Brian Quanz
2025	J	`jnl`	CoRe: Coherency Regularization for Hierarchical Time Series. CoRR Rares Cristian, Pavithra Harhsa, Georgia Perakis, Brian Quanz
2025	J	`jnl`	Deep Policy Iteration with Integer Programming for Inventory Management. Manuf. Serv. Oper. Manag. Pavithra Harsha, Ashish Jagmohan, Jayant Kalagnanam, Brian Quanz, Divya Singhvi
2025	J	`jnl`	Efficient End-to-End Learning for Decision-Making: A Meta-Optimization Approach. CoRR Rares Cristian, Pavithra Harsha, Georgia Perakis, Brian Quanz
2025	J	`jnl`	Quantum feature encoding optimization. CoRR Tommaso Fioravanti, Brian Quanz, Gabriele Agliardi, Edgar Andres Ruiz Guzman, Ginés Carrascal, Jae Eun Park
2024	Misc	`conf`	A Survey of Classical and Quantum Sequence Models. COMSNETS I-Chi Chen, Harshdeep Singh, V. L. Anukruti, Brian Quanz, Kavitha Yogaraj
2024	J	`jnl`	Inter-Series Transformer: Attending to Products in Time Series Forecasting. CoRR Rares Cristian, Pavithra Harsha, Clemente Ocejo, Georgia Perakis, Brian Quanz, Ioannis Spantidakis, Hamza Zerhouni
2023	J	`jnl`	A Survey of Classical And Quantum Sequence Models. CoRR I-Chi Chen, Harshdeep Singh, V. L. Anukruti, Brian Quanz, Kavitha Yogaraj
2023	J	`jnl`	An Optimistic-Robust Approach for Dynamic Positioning of Omnichannel Inventories. CoRR Pavithra Harsha, Shivaram Subramanian, Ali Koc, Mahesh Ramakrishna, Brian Quanz, Dhruv Shah, Chandra Narayanaswami
2023	A*	`conf`	Hierarchical Proxy Modeling for Improved HPO in Time Series Forecasting. KDD Arindam Jati, Vijay Ekambaram, Shaonli Pal, Brian Quanz, Wesley M. Gifford, Pavithra Harsha, Stuart Siegel, Sumanta Mukherjee, Chandra Narayanaswami
2022	B	`conf`	Distributed Incremental Machine Learning for Big Time Series Data. IEEE Big Data Dhaval Salwala, Seshu Tirupathi, Brian Quanz, Wesley M. Gifford, Stuart Siegel, Vijay Ekambaram, Arindam Jati
2022	J	`jnl`	Hierarchy-guided Model Selection for Time Series Forecasting. CoRR Arindam Jati, Vijay Ekambaram, Shaonli Pal, Brian Quanz, Wesley M. Gifford, Pavithra Harsha, Stuart Siegel, Sumanta Mukherjee, Chandra Narayanaswami
2022	A*	`conf`	Towards Creativity Characterization of Generative Models via Group-Based Subset Scanning. IJCAI Celia Cintas, Payel Das, Brian Quanz, Girmaw Abebe Tadesse, Skyler Speakman, Pin-Yu Chen
2022	J	`jnl`	Towards Creativity Characterization of Generative Models via Group-based Subset Scanning. CoRR Celia Cintas, Payel Das, Brian Quanz, Girmaw Abebe Tadesse, Skyler Speakman, Pin-Yu Chen
2021	J	`jnl`	Gi and Pal Scores: Deep Neural Network Generalization Statistics. CoRR Yair Schiff, Brian Quanz, Payel Das, Pin-Yu Chen
2021	J	`jnl`	Learning to shortcut and shortlist order fulfillment deciding. CoRR Brian Quanz, Ajay Deshpande, Dahai Xing, Xuan Liu
2021	J	`jnl`	Math Programming based Reinforcement Learning for Multi-Echelon Inventory Management. CoRR Pavithra Harsha, Ashish Jagmohan, Jayant R. Kalagnanam, Brian Quanz, Divya Singhvi
2021	A*	`conf`	Predicting Deep Neural Network Generalization with Perturbation Response Curves. NeurIPS Yair Schiff, Brian Quanz, Payel Das, Pin-Yu Chen
2021	J	`jnl`	Predicting Deep Neural Network Generalization with Perturbation Response Curves. CoRR Yair Schiff, Brian Quanz, Payel Das, Pin-Yu Chen
2021	A*	`conf`	Temporal Latent Auto-Encoder: A Method for Probabilistic Multivariate Time Series Forecasting. AAAI Nam Nguyen, Brian Quanz
2021	J	`jnl`	Temporal Latent Auto-Encoder: A Method for Probabilistic Multivariate Time Series Forecasting. CoRR Nam Nguyen, Brian Quanz
2021	J	`jnl`	Towards creativity characterization of generative models via group-based subset scanning. CoRR Celia Cintas, Payel Das, Brian Quanz, Skyler Speakman, Victor Akinwande, Pin-Yu Chen
2020	J	`jnl`	Machine learning based co-creative design framework. CoRR Brian Quanz, Wei Sun, Ajay Deshpande, Dhruv Shah, Jae Eun Park
2020	J	`jnl`	Practical application improvement to Quantum SVM: theory to practice. CoRR Jae Eun Park, Brian Quanz, Steve Wood, Heather Higgins, Ray Harishankar
2020	A*	`conf`	Toward a neuro-inspired creative decoder. IJCAI Payel Das, Brian Quanz, Pin-Yu Chen, Jae-wook Ahn, Dhruv Shah
2019	J	`jnl`	Toward A Neuro-inspired Creative Decoder. CoRR Payel Das, Brian Quanz, Pin-Yu Chen, Jae-wook Ahn
2012	A	`conf`	CoNet: feature generation for multi-view semi-supervised learning with partially observed views. CIKM Brian Quanz, Jun Huan
2012	J	`jnl`	Knowledge Transfer with Low-Quality Data: A Feature Extraction Issue. IEEE Trans. Knowl. Data Eng. Brian Quanz, Jun Huan, Meenakshi Mishra
2012	—	`—`	Learning with Low-Quality Data: Multi-View Semi-Supervised Learning with Missing Views. Brian Quanz
2012	—	`conf`	When Additional Views are Not Free: Active View Completion for Multi-view Semi-Supervised Learning. ICDM Workshops Brian Quanz, Jun Huan
2011	A*	`conf`	Knowledge transfer with low-quality data: A feature extraction issue. ICDE Brian Quanz, Jun Huan, Meenakshi Mishra
2010	A	`conf`	Regularization and feature selection for networked features. CIKM Hongliang Fei, Brian Quanz, Jun Huan
2009	A	`conf`	Aligned Graph Classification with Regularized Logistic Regression. SDM Brian Quanz, Jun Huan
2009	B	`conf`	Anomaly Detection with Sensor Data for Distributed Security. ICCCN 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
2009	—	`conf`	GLSVM: Integrating Structured Feature Selection and Large Margin Classification. ICDM Workshops Hongliang Fei, Brian Quanz, Jun Huan
2009	A	`conf`	Large margin transductive transfer learning. CIKM Brian Quanz, Jun Huan
2008	—	`conf`	Biological pathways as features for microarray data classification. DTMBIO Brian Quanz, Meeyoung Park, Jun Huan
2008	—	`conf`	Determining Object Safety Using a Multiagent, Collaborative System. SASO Workshops Brian Quanz, Costas Tsatsoulis