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Brian Quanz
Person information
- affiliation: IBM Research, Yorktown Heights, NY, USA
- affiliation (PhD): University of Kansas, Lawrence, KS, USA
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2020 – today
- 2024
- [c19]I-Chi Chen, Harshdeep Singh
, V. L. Anukruti, Brian Quanz
A Survey of Classical and Quantum Sequence Models. COMSNETS 2024: 1006-1011 - [i14]Rares Cristian, Pavithra Harsha, Clemente Ocejo, Georgia Perakis, Brian Quanz, Ioannis Spantidakis, Hamza Zerhouni:
Inter-Series Transformer: Attending to Products in Time Series Forecasting. CoRR abs/2408.03872 (2024) - 2023
- [c18]Rares Cristian, Pavithra Harsha, Georgia Perakis, Brian Leo Quanz, Ioannis Spantidakis:
End-to-End Learning for Optimization via Constraint-Enforcing Approximators. AAAI 2023: 7253-7260 - [c17]Arindam Jati
Hierarchical Proxy Modeling for Improved HPO in Time Series Forecasting. KDD 2023: 891-900 - [i13]Pavithra Harsha, Shivaram Subramanian, Ali Koc, Mahesh Ramakrishna, Brian Quanz
An Optimistic-Robust Approach for Dynamic Positioning of Omnichannel Inventories. CoRR abs/2310.12183 (2023) - [i12]I-Chi Chen, Harshdeep Singh, V. L. Anukruti, Brian Quanz
A Survey of Classical And Quantum Sequence Models. CoRR abs/2312.10242 (2023) - 2022
- [c16]Dhaval Salwala, Seshu Tirupathi, Brian Quanz
Distributed Incremental Machine Learning for Big Time Series Data. IEEE Big Data 2022: 2356-2363 - [c15]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: 4929-4935 - [i11]Celia Cintas, Payel Das, Brian Quanz
Towards Creativity Characterization of Generative Models via Group-based Subset Scanning. CoRR abs/2203.00523 (2022) - [i10]Arindam Jati, Vijay Ekambaram, Shaonli Pal, Brian Quanz
Hierarchy-guided Model Selection for Time Series Forecasting. CoRR abs/2211.15092 (2022) - 2021
- [c14]Nam Nguyen, Brian Quanz:
Temporal Latent Auto-Encoder: A Method for Probabilistic Multivariate Time Series Forecasting. AAAI 2021: 9117-9125 - [c13]Yair Schiff, Brian Quanz, Payel Das, Pin-Yu Chen:
Predicting Deep Neural Network Generalization with Perturbation Response Curves. NeurIPS 2021: 21176-21188 - [i9]Nam Nguyen, Brian Quanz:
Temporal Latent Auto-Encoder: A Method for Probabilistic Multivariate Time Series Forecasting. CoRR abs/2101.10460 (2021) - [i8]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 abs/2104.00479 (2021) - [i7]Yair Schiff, Brian Quanz, Payel Das, Pin-Yu Chen:
Gi and Pal Scores: Deep Neural Network Generalization Statistics. CoRR abs/2104.03469 (2021) - [i6]Yair Schiff, Brian Quanz, Payel Das, Pin-Yu Chen:
Predicting Deep Neural Network Generalization with Perturbation Response Curves. CoRR abs/2106.04765 (2021) - [i5]Brian Quanz, Ajay Deshpande, Dahai Xing, Xuan Liu:
Learning to shortcut and shortlist order fulfillment deciding. CoRR abs/2110.01668 (2021) - [i4]Pavithra Harsha, Ashish Jagmohan, Jayant R. Kalagnanam, Brian Quanz, Divya Singhvi:
Math Programming based Reinforcement Learning for Multi-Echelon Inventory Management. CoRR abs/2112.02215 (2021) - 2020
- [c12]Payel Das, Brian Quanz, Pin-Yu Chen, Jae-wook Ahn, Dhruv Shah:
Toward a neuro-inspired creative decoder. IJCAI 2020: 2746-2753 - [i3]Brian Quanz
Machine learning based co-creative design framework. CoRR abs/2001.08791 (2020) - [i2]Jae Eun Park, Brian Quanz, Steve Wood, Heather Higgins, Ray Harishankar:
Practical application improvement to Quantum SVM: theory to practice. CoRR abs/2012.07725 (2020)
2010 – 2019
- 2019
- [i1]Payel Das, Brian Quanz, Pin-Yu Chen, Jae-wook Ahn:
Toward A Neuro-inspired Creative Decoder. CoRR abs/1902.02399 (2019) - 2018
- [c11]Yada Zhu, Jianbo Li, Jingrui He, Brian Leo Quanz
A Local Algorithm for Product Return Prediction in E-Commerce. IJCAI 2018: 3718-3724 - 2012
- [b1]Brian Quanz:
Learning with Low-Quality Data: Multi-View Semi-Supervised Learning with Missing Views. University of Kansas, USA, 2012 - [j1]Brian Quanz
Knowledge Transfer with Low-Quality Data: A Feature Extraction Issue. IEEE Trans. Knowl. Data Eng. 24(10): 1789-1802 (2012) - [c10]Brian Quanz
CoNet: feature generation for multi-view semi-supervised learning with partially observed views. CIKM 2012: 1273-1282 - [c9]Brian Quanz
When Additional Views are Not Free: Active View Completion for Multi-view Semi-Supervised Learning. ICDM Workshops 2012: 169-178 - 2011
- [c8]Brian Quanz
Knowledge transfer with low-quality data: A feature extraction issue. ICDE 2011: 769-779 - 2010
- [c7]Hongliang Fei, Brian Quanz
Regularization and feature selection for networked features. CIKM 2010: 1893-1896
2000 – 2009
- 2009
- [c6]Brian Quanz
Large margin transductive transfer learning. CIKM 2009: 1327-1336 - [c5]Brian Quanz
Anomaly Detection with Sensor Data for Distributed Security. ICCCN 2009: 1-6 - [c4]Hongliang Fei, Brian Quanz
GLSVM: Integrating Structured Feature Selection and Large Margin Classification. ICDM Workshops 2009: 362-367 - [c3]Brian Quanz, Jun Huan:
Aligned Graph Classification with Regularized Logistic Regression. SDM 2009: 353-364 - 2008
- [c2]Brian Quanz
Biological pathways as features for microarray data classification. DTMBIO 2008: 5-12 - [c1]Brian Quanz
Determining Object Safety Using a Multiagent, Collaborative System. SASO Workshops 2008: 25-30
Coauthor Index
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last updated on 2024-10-07 21:21 CEST by the dblp team
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