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This paper introduces an innovative approach to enhance distributed cooperative learning using Gaussian process (GP) regression in multi-agent systems (MASs). The key contribution of this work is the development of an elective learning algorithm, namely ...

A universal feature of human societies is the adoption of systems of rules and norms in the service of cooperative ends. How can we build learning agents that do the same, so that they may flexibly cooperate with the human institutions they are embedded ...

The Event Based Model (EBM) is a probabilistic generative model to explore biomarker changes occurring as a disease progresses. Disease progression is hypothesized to occur through a sequence of biomarker dysregulation “events”. The EBM estimates ...${}^{\mathrm{}}$$\mathrm{}\mathrm{}$

Learning an opponent's preference is critical to achieving a win-win situation in automated bilateral multi-issue negotiations. Most of the existing opponent preference-learning techniques are not scalable to many kinds of opponents with different ...

Real-Time Bidding (RTB) is an important mechanism in modern online advertising systems. Advertisers employ bidding strategies in RTB to optimize their advertising effects subject to various financial requirements, especially the return-on-investment (ROI)...

One of the most distinctive characteristics in biological evolution is to not only learn and reinforce knowledge from prior experience, but also develop a solution in (pseudo) real-time for future events by studying the past choices. Inspired by this ...

We describe results from Dasaratha and He [DH21a] and Dasaratha and He [DH20] about how network structure influences social learning outcomes. These papers share a tractable sequential model that lets us compare learning dynamics across networks. With ...

In social-learning settings where individuals receive private signals and observe network neighbors' actions, the network structure often obstructs information aggregation. We consider sequential social learning with rational agents and Gaussian signals ...

With the increasing use of online social networks as a source of news and information, the propensity for a rumor to disseminate widely and quickly poses a great concern, especially in disaster situations where users do not have enough time to fact-...

Deep learning is developed as a learning process from source inputs to target outputs where the inference or optimization is performed over an assumed deterministic model with deep structure. A wide range of temporal and spatial data in language and ...

This tutorial addresses the fundamentals and advances in deep Bayesian mining and learning for natural language with ubiquitous applications ranging from speech recognition to document summarization, text classification, text segmentation, information ...

This tutorial addresses the advances in deep Bayesian mining and learning for natural language with ubiquitous applications ranging from speech recognition to document summarization, text classification, text segmentation, information extraction, image ...

In this paper we study a model of information consumption where consumers sequentially interact with a platform that offers a menu of signals (posts) about an underlying state of the world (fact). At each time, incapable of consuming all posts, ...

Bayesian agents learn about a moving target, such as a commodity price, using private signals and their network neighbors' estimates. The weights agents place on these sources of information are endogenously determined by the precisions and correlations ...

Many bioinformatics studies aim to identify markers, or features that can be used to discriminate between distinct groups. In problems where strong individual markers are not available, or where interactions between gene products are of primary interest,...

Probabilistic topic models are generative models that describe the content of documents by discovering the latent topics underlying them. However, the structure of the textual input, and for instance the grouping of words in coherent text spans such as ...

An important use of machine learning is to learn what people value. What posts or photos should a user be shown? Which jobs or activities would a person find rewarding? In each case, observations of people's past choices can inform our inferences about ...

This paper presents a new Bayesian nonnegative matrix factorization (NMF) for monaural source separation. Using this approach, the reconstruction error based on NMF is represented by a Poisson distribution, and the NMF parameters, consisting of the ...

We present a Bayesian non-negative tensor factorization model for count-valued tensor data, and develop scalable inference algorithms (both batch and online) for dealing with massive tensors. Our generative model can handle overdispersed counts as well ...

Personalized routing recommendation is receiving increasing attention in both academia and engineering. The methodology of how to customize multi-modal routing recommendation to personal preferences of users however is still subject of current research. ...