arXiv subjects

Hongtao Sun

Publications and source records attributed to Hongtao Sun.

3 recordsLinked to original sources

arXiv2024

Practical Marketplace Optimization at Uber Using Causally-Informed Machine Learning

Budget allocation of marketplace levers, such as incentives for drivers and promotions for riders, has long been a technical and business challenge at Uber; understanding lever budget changes' impact and estimating cost efficiency to achieve predefined budgets is crucial, with the goal of optimal allocations that maximize business value; we introduce an end-to-end machine learning and optimization procedure to automate budget decision-making for cities, relying on feature store, model training and serving, optimizers, and backtesting; proposing state-of-the-art deep learning (DL) estimator based on S-Learner and a novel tensor B-Spline regression model, we solve high-dimensional optimization with ADMM and primal-dual interior point convex optimization, substantially improving Uber's resource allocation efficiency.

cs.LG↗

arXiv2006

Improved Exponential Time Lower Bound of Knapsack Problem under BT model

M.Alekhnovich et al. recently have proposed a model of algorithms, called BT model, which covers Greedy, Backtrack and Simple Dynamic Programming methods and can be further divided into fixed, adaptive and fully adaptive three kinds, and have proved exponential time lower bounds of exact and approximation algorithms under adaptive BT model for Knapsack problem which are $Ω(2^{n/2}/\sqrt n)=Ω(2^{0.5n}/\sqrt n)$ and $Ω((1/ε)^{1/3.17})\approxΩ((1/ε)^{0.315})$(for approximation ratio $1-ε$) respectively (M. Alekhovich, A. Borodin, J. Buresh-Oppenheim, R. Impagliazzo, A. Magen, and T. Pitassi, Toward a Model for Backtracking and Dynamic Programming, \emph{Proceedings of Twentieth Annual IEEE Conference on Computational Complexity}, pp308-322, 2005). In this note, we slightly improved their lower bounds to $Ω(2^{(2-ε)n/3}/\sqrt{n})\approx Ω(2^{0.66n}/\sqrt{n})$ and $Ω((1/ε)^{1/2.38})\approxΩ((1/ε)^{0.420})$, and proposed as an open question what is the best achievable lower bounds for knapsack under adaptive BT models.

cs.CC↗

arXiv2006

On Lower Bound of Worst Case Error Probability for Quantum Fingerprinting with Shared Entanglement

This paper discusses properties of quantum fingerprinting with shared entanglement. Under certain restriction of final measurement, a relation is given between unitary operations of two parties. Then, by reducing to spherical coding problem, this paper gives a lower bound of worst case error probability for quantum fingerprinting with shared entanglement, showing a relation between worst case error probability and the amount of entanglement(measured by Schmidt number).

quant-ph↗