7 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…
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…
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…