Multiagent sequential decision making has seen rapid progress with formal models such as
decentralized MDPs and POMDPs. However, scalability to large multiagent systems and …

We investigate the problem of learning discrete graphical models in a differentially private
way. Approaches to this problem range from privileged algorithms that conduct learning …

We propose a probabilistic model for estimating population flow, which is defined as
populations of the transition between areas over time, given aggregated spatio-temporal …

Abstract Collective Flow Diffusion Model (CFDM) is a general framework to find the hidden
movements underlying aggregated population data. The key procedure in CFDM analysis is …

Collective graphical model (CGM) is a probabilistic model that provides a framework for
analyzing aggregated count data. Maximum a posteriori (MAP) inference of unobserved …

In this thesis, we study reinforcement learning algorithms to collectively optimize
decentralized policy in a large population of autonomous agents. We notice one of the main …

Optimal Transport (OT) is being widely used in various fields such as machine learning and
computer vision, as it is a powerful tool for measuring the similarity between probability …

In this work, we investigate a method to learn a discriminative model predicting a binary
label, from aggregated data only. Our aggregated data consists in a set of contingency …

The importance of aggregated count data, which is calculated from the data of multiple
individuals, continues to increase. Collective Graphical Model (CGM) is a probabilistic …

Complete co-occurrence data are unavailable in many applications, including purchase
records and medical histories, because of their high cost or privacy protection. Even with …