Communications


Estimating the Size of a Large Network and its Communities from a Random Sample

Neural Information Processing Systems

Most real-world networks are too large to be measured or studied directly and there is substantial interest in estimating global network properties from smaller sub-samples. One of the most important global properties is the number of vertices/nodes in the network. Estimating the number of vertices in a large network is a major challenge in computer science, epidemiology, demography, and intelligence analysis. In this paper we consider a population random graph G (V;E) from the stochastic block model (SBM) with K communities/blocks. A sample is obtained by randomly choosing a subset W and letting G(W) be the induced subgraph in G of the vertices in W. In addition to G(W), we observe the total degree of each sampled vertex and its block membership.


Inferring Networks From Random Walk-Based Node Similarities

Neural Information Processing Systems

Digital presence in the world of online social media entails significant privacy risks. In this work we consider a privacy threat to a social network in which an attacker has access to a subset of random walk-based node similarities, such as effective resistances (i.e., commute times) or personalized PageRank scores. Using these similarities, the attacker seeks to infer as much information as possible about the network, including unknown pairwise node similarities and edges. For the effective resistance metric, we show that with just a small subset of measurements, one can learn a large fraction of edges in a social network. We also show that it is possible to learn a graph which accurately matches the underlying network on all other effective resistances.


Whitney Cummings: Comedy, Robotics, Neurology, and Love Artificial Intelligence (AI) Podcast

#artificialintelligence

Whitney Cummings is a stand-up comedian, actor, producer, writer, director, and the host of a new podcast called Good for You. Her most recent Netflix special features in part a robot, she affectionately named Bearclaw, that is designed to be visually a replica of Whitney. It's exciting for me to see one of my favorite comedians explore the social aspects of robotics and AI in our society. This conversation is part of the Artificial Intelligence podcast. This episode is presented by Cash App: download it & use code "LexPodcast" The episode is also supported by ZipRecruiter.


Using Social Dynamics to Make Individual Predictions: Variational Inference with a Stochastic Kinetic Model

Neural Information Processing Systems

Social dynamics is concerned primarily with interactions among individuals and the resulting group behaviors, modeling the temporal evolution of social systems via the interactions of individuals within these systems. In particular, the availability of large-scale data from social networks and sensor networks offers an unprecedented opportunity to predict state-changing events at the individual level. Examples of such events include disease transmission, opinion transition in elections, and rumor propagation. Unlike previous research focusing on the collective effects of social systems, this study makes efficient inferences at the individual level. In order to cope with dynamic interactions among a large number of individuals, we introduce the stochastic kinetic model to capture adaptive transition probabilities and propose an efficient variational inference algorithm the complexity of which grows linearly -- rather than exponentially-- with the number of individuals.


Education Is a System of Indoctrination of the Young - Noam Chomsky

#artificialintelligence

Chomsky has been known to vigorously defend and debate his views and opinions, in philosophy, linguistics, and politics. He has had notable debates with Jean Piaget, Michel Foucault, William F. Buckley, Jr., Christopher Hitchens, George Lakoff, Richard Perle, Hilary Putnam, Willard Quine, and Alan Dershowitz, to name a few. In response to his speaking style being criticized as boring, Chomsky said that "I'm a boring speaker and I like it that way.... I doubt that people are attracted to whatever the persona is.... People are interested in the issues, and they're interested in the issues because they are important."


Photoshop for iPad road map details much-needed early 2020 updates

#artificialintelligence

With Photoshop on iPad's current tools, having to fix the edges of this extremely basic selection is annoyingly tedious. Adobe plans to rectify that in the first half of 2020. When Adobe launched the long-awaited iPad version of Photoshop in October, it looked like we had a little more waiting to do before it could really be used as intended; among other things, its masking and retouching tools are underpowered, especially given that Adobe's planning to charge $10 a month for a stand-alone version (at the moment, the cheapest way to get it is as part of the $10 a month Photography plan). So today Adobe has given us more of an idea as to what its priorities are by telling when we can expect some of those essential tools to appear. By the end of this year, it will have incorporated the Select Subject tool that just debuted in the desktop version; Select Subject uses AI to guess what the main subject in a photo is and select it.


Beyond Parity: Fairness Objectives for Collaborative Filtering

Neural Information Processing Systems

We study fairness in collaborative-filtering recommender systems, which are sensitive to discrimination that exists in historical data. Biased data can lead collaborative-filtering methods to make unfair predictions for users from minority groups. We identify the insufficiency of existing fairness metrics and propose four new metrics that address different forms of unfairness. These fairness metrics can be optimized by adding fairness terms to the learning objective. Experiments on synthetic and real data show that our new metrics can better measure fairness than the baseline, and that the fairness objectives effectively help reduce unfairness.


Eliciting Categorical Data for Optimal Aggregation

Neural Information Processing Systems

Models for collecting and aggregating categorical data on crowdsourcing platforms typically fall into two broad categories: those assuming agents honest and consistent but with heterogeneous error rates, and those assuming agents strategic and seek to maximize their expected reward. The former often leads to tractable aggregation of elicited data, while the latter usually focuses on optimal elicitation and does not consider aggregation. In this paper, we develop a Bayesian model, wherein agents have differing quality of information, but also respond to incentives. Our model generalizes both categories and enables the joint exploration of optimal elicitation and aggregation. This model enables our exploration, both analytically and experimentally, of optimal aggregation of categorical data and optimal multiple-choice interface design.


Parallel Streaming Wasserstein Barycenters

Neural Information Processing Systems

Efficiently aggregating data from different sources is a challenging problem, particularly when samples from each source are distributed differently. These differences can be inherent to the inference task or present for other reasons: sensors in a sensor network may be placed far apart, affecting their individual measurements. Conversely, it is computationally advantageous to split Bayesian inference tasks across subsets of data, but data need not be identically distributed across subsets. One principled way to fuse probability distributions is via the lens of optimal transport: the Wasserstein barycenter is a single distribution that summarizes a collection of input measures while respecting their geometry. However, computing the barycenter scales poorly and requires discretization of all input distributions and the barycenter itself.


Collaborative Filtering with Graph Information: Consistency and Scalable Methods

Neural Information Processing Systems

Low rank matrix completion plays a fundamental role in collaborative filtering applications, the key idea being that the variables lie in a smaller subspace than the ambient space. Often, additional information about the variables is known, and it is reasonable to assume that incorporating this information will lead to better predictions. We tackle the problem of matrix completion when pairwise relationships among variables are known, via a graph. We formulate and derive a highly efficient, conjugate gradient based alternating minimization scheme that solves optimizations with over 55 million observations up to 2 orders of magnitude faster than state-of-the-art (stochastic) gradient-descent based methods. On the theoretical front, we show that such methods generalize weighted nuclear norm formulations, and derive statistical consistency guarantees.