Yoad Lewenberg

Inclusive Block Chain Protocols

Distributed cryptographic protocols such as Bitcoin and Ethereum use a data structure known as the block chain to synchronize a global log of events between nodes in their network. Blocks, which are batches of updates to the log, reference the parent they are extending, and thus form the structure of a chain. Previous research has shown that the mechanics of the block chain and block propagation are constrained: if blocks are created at a high rate compared to their propagation time in the network, many conflicting blocks are created and performance suffers greatly. As a result of the low block creation rate required to keep the system within safe parameters, transactions take long to securely confirm, and their throughput is greatly limited.

Using emotions to predict user interest areas in online social networks

We examine the relation between the emotions users express on social networks and their perceived areas of interests, based on a sample of Twitter users. Our methodology relies on training machine learning models to classify the emotions expressed in tweets, according to Ekmans six high-level emotions. We then used raters, sourced from Amazons Mechanical Turk, to examine several Twitter profiles and to determine whether the profile owner is interested in various areas, including sports, movies, technology and computing, politics, news, economics, science, arts, health and religion. We find that the propensity of a user to express various emotions correlates with their perceived degree of interest in various areas. We present several models that use the emotional distribution of a Twitter user, as reflected by their tweets, to predict whether they are interested or disinterested in a topic or to determine their degree of interest in a topic.

Predicting personal traits from facial images using convolutional neural networks augmented with facial landmark information

We consider the task of predicting various traits of a person given an image of their face. We aim to estimate traits such as gender, ethnicity and age, as well as more subjective traits as the emotion a person expresses or whether they are humorous or attractive. Due to the recent surge of research on Deep Convolutional Neural Networks (CNNs), we begin by using a CNN architecture, and corroborate that CNNs are promising for facial attribute prediction. To further improve performance, we propose a novel approach that incorporates facial landmark information for input images as an additional channel, helping the CNN learn face-specific features so that the landmarks across various training images hold correspondence. We empirically analyze the performance of our proposed method, showing consistent improvement over the baselines across traits. We demonstrate our system on a sizeable Face Attributes Dataset (FAD), comprising of roughly 200,000 labels, for 10 most sought-after traits, for over 10,000 facial images.

Predicting Gaming Related Properties from Twitter Profiles

We present a system for predicting gaming-related properties from Twitter profiles. Our system predicts various traits of users based on the tweets publicly available on their profiles. Such inferred traits include degrees of tech-savviness, knowledge on computer games, actual gaming performance, preferred platform, degree of originality, humor and influence on others. Our approach is based on machine learning models trained on crowd-sourced data. Our system enables people to select Twitter profiles of their fellow gamers, examine the trait predictions made by our system, and the main drivers of these predictions. We present empirical results on the performance of our system based on its accuracy on our crowd-sourced dataset. Ultimately, we are motivated by the automated discovery of influential gamers in social media, and its potential for streamlining product campaigns.

Machine Learning Techniques for MultiAgent Systems.

Research in artificial intelligence ranges over many subdisciplines, such as Natural Language Processing, Computer Vision, Machine Learning, and MultiAgent Systems. Recently, AI techniques have become increasingly robust and complex, and there has been enhanced interest in research at the intersection of seemingly disparate research areas. Such work is motivated by the observation that there is actually a great deal of commonality among areas, that can be exploited within subfields. One example of a successful combination is the intersection of machine learning and multiagent systems. For example, [Kearns et al., 2001] proposed an efficient graphical model-based algorithm for calculating Nash equilibria. Going in the other direction, [Datta et al., 2015] showed that solution concepts from cooperative game theory can be used to uniquely characterize the influence measure of classifiers.