arXiv subjects

Ashley Peake

Publications and source records attributed to Ashley Peake.

3 recordsLinked to original sources

arXiv2026

A Jump-Diffusion Model of Home Price Dynamics and Neighborhood Change

We develop a dynamical model that links the fluctuations of individual home prices to changes in the full distribution of home prices in an urban area. We model the price of an individual home as a jump-diffusion stochastic differential equation. To capture the dynamic and hyper-local economic conditions that drive the residential real estate market, we allow all parameters to depend on both time and neighborhood. In the mean-field limit, we derive the associated Fokker-Planck partial integro-differential equation (PIDE) governing the evolution of the home price distribution in a neighborhood. We quantify spatial heterogeneity within a city by using the Wasserstein-2 distance to compare home price distributions across the city's neighborhoods. We validate our model using data from Boston, Massachusetts as a case study.

physics.soc-ph↗

arXiv2021

End-To-End Bias Mitigation: Removing Gender Bias in Deep Learning

Machine Learning models have been deployed across many different aspects of society, often in situations that affect social welfare. Although these models offer streamlined solutions to large problems, they may contain biases and treat groups or individuals unfairly based on protected attributes such as gender. In this paper, we introduce several examples of machine learning gender bias in practice followed by formalizations of fairness. We provide a survey of fairness research by detailing influential pre-processing, in-processing, and post-processing bias mitigation algorithms. We then propose an end-to-end bias mitigation framework, which employs a fusion of pre-, in-, and post-processing methods to leverage the strengths of each individual technique. We test this method, along with the standard techniques we review, on a deep neural network to analyze bias mitigation in a deep learning setting. We find that our end-to-end bias mitigation framework outperforms the baselines with respect to several fairness metrics, suggesting its promise as a method for improving fairness. As society increasingly relies on artificial intelligence to help in decision-making, addressing gender biases present in deep learning models is imperative. To provide readers with the tools to assess the fairness of machine learning models and mitigate the biases present in them, we discuss multiple open source packages for fairness in AI.

cs.LG↗

arXiv2021

Deep Reinforcement Learning for Adaptive Exploration of Unknown Environments

Performing autonomous exploration is essential for unmanned aerial vehicles (UAVs) operating in unknown environments. Often, these missions start with building a map for the environment via pure exploration and subsequently using (i.e. exploiting) the generated map for downstream navigation tasks. Accomplishing these navigation tasks in two separate steps is not always possible or even disadvantageous for UAVs deployed in outdoor and dynamically changing environments. Current exploration approaches either use a priori human-generated maps or use heuristics such as frontier-based exploration. Other approaches use learning but focus only on learning policies for specific tasks by either using sample inefficient random exploration or by making impractical assumptions about full map availability. In this paper, we develop an adaptive exploration approach to trade off between exploration and exploitation in one single step for UAVs searching for areas of interest (AoIs) in unknown environments using Deep Reinforcement Learning (DRL). The proposed approach uses a map segmentation technique to decompose the environment map into smaller, tractable maps. Then, a simple information gain function is repeatedly computed to determine the best target region to search during each iteration of the process. DDQN and A2C algorithms are extended with a stack of LSTM layers and trained to generate optimal policies for the exploration and exploitation, respectively. We tested our approach in 3 different tasks against 4 baselines. The results demonstrate that our proposed approach is capable of navigating through randomly generated environments and covering more AoI in less time steps compared to the baselines.

cs.LG↗