arXiv subjects

Yitao Shen

Publications and source records attributed to Yitao Shen.

2 recordsLinked to original sources

arXiv2026

Change-in-velocity detection for multidimensional data

In this work, we introduce CPLASS (Continuous Piecewise-Linear Approximation via Stochastic Search), an algorithm for detecting changes in velocity within multidimensional data. The one-dimensional version of this problem is known as the change-in-slope problem (see Fearnhead & Grose, 2022; Baranowski et al., 2019). Unlike traditional changepoint detection methods that focus on changes in mean, detecting changes in velocity requires a specialized approach due to continuity constraints and parameter dependencies, which frustrate popular algorithms like binary segmentation and dynamic programming. To overcome these difficulties, we introduce a specialized penalty function to balance improvements in likelihood due to model complexity, and a Markov Chain Monte Carlo (MCMC)-based approach with tailored proposal mechanisms for efficient parameter exploration. Our method is particularly suited for analyzing intracellular transport data, where the multidimensional trajectories of microscale cargo are driven by teams of molecular motors that undergo complex biophysical transitions. To ensure biophysical realism in the results, we introduce a speed penalty that discourages overfitted of short noisy segments while maintaining consistency in the large-sample limit. Additionally, we introduce a summary statistic called the Cumulative Speed Allocation, which is robust with respect to idiosyncracies of changepoint detection while maintaining the ability to discriminate between biophysically distinct populations.

stat.ME↗

arXiv2021

A framework for massive scale personalized promotion

Technology companies building consumer-facing platforms may have access to massive-scale user population. In recent years, promotion with quantifiable incentive has become a popular approach for increasing active users on such platforms. On one hand, increased user activities can introduce network effect, bring in advertisement audience, and produce other benefits. On the other hand, massive-scale promotion causes massive cost. Therefore making promotion campaigns efficient in terms of return-on-investment (ROI) is of great interest to many companies. This paper proposes a practical two-stage framework that can optimize the ROI of various massive-scale promotion campaigns. In the first stage, users' personal promotion-response curves are modeled by machine learning techniques. In the second stage, business objectives and resource constraints are formulated into an optimization problem, the decision variables of which are how much incentive to give to each user. In order to do effective optimization in the second stage, counterfactual prediction and noise-reduction are essential for the first stage. We leverage existing counterfactual prediction techniques to correct treatment bias in data. We also introduce a novel deep neural network (DNN) architecture, the deep-isotonic-promotion-network (DIPN), to reduce noise in the promotion response curves. The DIPN architecture incorporates our prior knowledge of response curve shape, by enforcing isotonicity and smoothness. It out-performed regular DNN and other state-of-the-art shape-constrained models in our experiments.

cs.LG↗