29 citations · 70 across the 19 of their papers we have counts for
20 papers
Quantum feature encoding optimization
Tommaso Fioravanti, Brian Quanz, Gabriele Agliardi +3
Quantum Machine Learning (QML) holds the promise of enhancing machine learning modeling in terms of both complexity and accuracy. A key challenge in this domain is the encoding of…
Enhanced fill probability estimates in institutional algorithmic bond trading using statistical learning algorithms with quantum computers
Axel Ciceri, Austin Cottrell, Joshua Freeland +13
The estimation of fill probabilities for trade orders represents a key ingredient in the optimization of algorithmic trading strategies. It is bound by the complex dynamics of fina…
Aligning Learning and Endogenous Decision-Making
Rares Cristian, Pavithra Harsha, Georgia Perakis +1
Many of the observations we make are biased by our decisions. For instance, the demand of items is impacted by the prices set, and online checkout choices are influenced by the ass…
Efficient End-to-End Learning for Decision-Making: A Meta-Optimization Approach
Rares Cristian, Pavithra Harsha, Georgia Perakis +1
End-to-end learning has become a widely applicable and studied problem in training predictive ML models to be aware of their impact on downstream decision-making tasks. These end-t…
CoRe: Coherency Regularization for Hierarchical Time Series
Rares Cristian, Pavithra Harhsa, Georgia Perakis +1
Hierarchical time series forecasting presents unique challenges, particularly when dealing with noisy data that may not perfectly adhere to aggregation constraints. This paper intr…
Inter-Series Transformer: Attending to Products in Time Series Forecasting
Rares Cristian, Pavithra Harsha, Clemente Ocejo +4
Time series forecasting is an important task in many fields ranging from supply chain management to weather forecasting. Recently, Transformer neural network architectures have sho…
Quantum Multiple Kernel Learning in Financial Classification Tasks
Shungo Miyabe, Brian Quanz, Noriaki Shimada +11
Financial services is a prospect industry where unlocked near-term quantum utility could yield profitable potential, and, in particular, quantum machine learning algorithms could p…
An Optimistic-Robust Approach for Dynamic Positioning of Omnichannel Inventories
Pavithra Harsha, Shivaram Subramanian, Ali Koc +4
We introduce a new class of data-driven and distribution-free optimistic-robust bimodal inventory optimization (BIO) strategy to effectively allocate inventory across a retail chai…
Hierarchical Proxy Modeling for Improved HPO in Time Series Forecasting
Arindam Jati, Vijay Ekambaram, Shaonli Pal +6
Selecting the right set of hyperparameters is crucial in time series forecasting. The classical temporal cross-validation framework for hyperparameter optimization (HPO) often lead…
Boosting Method for Automated Feature Space Discovery in Supervised Quantum Machine Learning Models
Vladimir Rastunkov, Jae-Eun Park, Abhijit Mitra +5
Quantum Support Vector Machines (QSVM) have become an important tool in research and applications of quantum kernel methods. In this work we propose a boosting approach for buildin…
Towards Creativity Characterization of Generative Models via Group-based Subset Scanning
Celia Cintas, Payel Das, Brian Quanz +3
Deep generative models, such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs), have been employed widely in computational creativity research. However,…
Deep Policy Iteration with Integer Programming for Inventory Management
Pavithra Harsha, Ashish Jagmohan, Jayant Kalagnanam +2
We present a Reinforcement Learning (RL) based framework for optimizing long-term discounted reward problems with large combinatorial action space and state dependent constraints.…
Learning to shortcut and shortlist order fulfillment deciding
Brian Quanz, Ajay Deshpande, Dahai Xing +1
With the increase of order fulfillment options and business objectives taken into consideration in the deciding process, order fulfillment deciding is becoming more and more comple…
Predicting Deep Neural Network Generalization with Perturbation Response Curves
Yair Schiff, Brian Quanz, Payel Das +1
The field of Deep Learning is rich with empirical evidence of human-like performance on a variety of prediction tasks. However, despite these successes, the recent Predicting Gener…
Towards creativity characterization of generative models via group-based subset scanning
Celia Cintas, Payel Das, Brian Quanz +3
Deep generative models, such as Variational Autoencoders (VAEs), have been employed widely in computational creativity research. However, such models discourage out-of-distribution…
Gi and Pal Scores: Deep Neural Network Generalization Statistics
Yair Schiff, Brian Quanz, Payel Das +1
The field of Deep Learning is rich with empirical evidence of human-like performance on a variety of regression, classification, and control tasks. However, despite these successes…
Temporal Latent Auto-Encoder: A Method for Probabilistic Multivariate Time Series Forecasting
Nam Nguyen, Brian Quanz
Probabilistic forecasting of high dimensional multivariate time series is a notoriously challenging task, both in terms of computational burden and distribution modeling. Most prev…
Practical application improvement to Quantum SVM: theory to practice
Jae-Eun Park, Brian Quanz, Steve Wood +2
Quantum machine learning (QML) has emerged as an important area for Quantum applications, although useful QML applications would require many qubits. Therefore our paper is aimed a…
Machine learning based co-creative design framework
Brian Quanz, Wei Sun, Ajay Deshpande +2
We propose a flexible, co-creative framework bringing together multiple machine learning techniques to assist human users to efficiently produce effective creative designs. We demo…
Toward A Neuro-inspired Creative Decoder
Payel Das, Brian Quanz, Pin-Yu Chen +2
Creativity, a process that generates novel and meaningful ideas, involves increased association between task-positive (control) and task-negative (default) networks in the human br…