While the public views the federal government as a monolith, in truth United States departments and agencies often function independently. Due to privacy regulations and statutory reasons, they often cannot share data about individuals with each other. Yet, such data collaboration would facilitate the development of artificial intelligence models to significantly improve the provision of social services. We argue that a particular privacy-enhancing technology (split learning operating over vertically partitioned data), together with data governance through metadata supply chains, can be part of larger sociotechnical systems that can improve life for the most vulnerable among us.

We consider the fractional influence maximization problem, i.e., identifying users on a social network to be incentivized with potentially partial discounts to maximize the influence on the network. The larger the discount given to a user, the higher the likelihood of its activation (adopting a new product or innovation), who then attempts to activate its neighboring users, causing a cascade effect of influence through the network. Our goal is to devise efficient algorithms that assign initial discounts to the network's users to maximize the total number of activated users at the end of the cascade, subject to a constraint on the total sum of discounts given. In general, the activation likelihood could be any non-decreasing function of the discount, whereas, our focus lies on the case when the activation likelihood is an affine function of the discount, potentially varying across different users. As this problem is shown to be NP-hard, we propose and analyze an efficient (1-1/e)-approximation algorithm. Furthermore, we run experiments on real-world social networks to show the performance and scalability of our method.

Many information sources are not just sequences of distinguishable symbols but rather have invariances governed by alternative counting paradigms such as permutations, combinations, and partitions. We consider an entire classification of these invariances called the twelvefold way in enumerative combinatorics and develop a method to characterize lossless compression limits. Explicit computations for all twelve settings are carried out for i.i.d. uniform and Bernoulli distributions. Comparisons among settings provide quantitative insight.

We consider the problem of coding for computing with maximal distortion, where the sender communicates with a receiver, which has its own private data and wants to compute a function of their combined data with some fidelity constraint known to both agents. We show that the minimum rate for this problem is equal to the conditional entropy of a hypergraph and design practical codes for the problem. Further, the minimum rate of this problem may be a discontinuous function of the fidelity constraint. We also consider the case when the exact function is not known to the sender, but some approximate function or a class to which the function belongs is known and provide efficient achievable schemes.

Deep neural networks have shown incredible performance for inference tasks in a variety of domains. Unfortunately, most current deep networks are enormous cloud-based structures that require significant storage space, which limits scaling of deep learning as a service (DLaaS). This paper is concerned with finding universal lossless compressed representations of deep feedforward networkswith synaptic weights drawn from discrete sets. The basic insight that allows much less rate than naive approaches is the recognition that the bipartite graph layers of feedforward networks have a kind of permutation invariance to the labeling of nodes, in terms of inferential operation. We provide efficient algorithms to dissipate this irrelevant uncertainty and then use arithmetic coding to nearly achieve the entropy bound in a universal manner.

We consider a network quantizer design setting where agents must balance fidelity in representing their local source distributions against their ability to successfully communicate with other connected agents. By casting the problem as a network game, we show existence of Nash equilibrium quantizer designs. For any agent, under Nash equilibrium, the word representing a given partition region is the conditional expectation of the mixture of local and social source probability distributions within the region. Further, the network may converge to equilibrium through a distributed version of the Lloyd-Max algorithm. In contrast to traditional results in the evolution of language, we find several vocabularies may coexist in the Nash equilibrium, with each individual having exactly one of these vocabularies. The overlap between vocabularies is high for individuals that communicate frequently and have similar local sources. Finally, we argue error in translation along a chain of communication does not grow if and only if the chain consists of agents with shared vocabulary.

Built on top of human resources management databases within the enterprise, we present a decision support system for managing and optimizing screening activities during the hiring process in a large organization. The basic idea is to prioritize the efforts of human resource practitioners to focus on candidates that are likely of high quality, that are likely to accept a job offer if made one, and that are likely to remain with the organization for the long term. To do so, the system first individually ranks candidates along several dimensions using a keyword matching algorithm and several bipartite ranking algorithms with univariate loss trained on historical actions. Next, individual rankings are aggregated to derive a single list that is presented to the recruitment team through an interactive portal. The portal supports multiple filters that facilitate effective identification of candidates. We demonstrate the usefulness of our system on data collected from a large organization over several years with business value metrics showing greater hiring yield with less interviews. Similarly, using historical pre-hire data we demonstrate accurate identification of candidates that will have quickly left the organization. The system has been deployed as described in a large globally integrated enterprise.

The effect of quantization of prior probabilities in a collection of distributed Bayesian binary hypothesis testing problems over which the priors themselves vary is studied. In a setting with fusion of local binary decisions by majority rule, optimal local decision rules are discussed. Quantization is first considered under the constraint that agents employ identical quantizers. A method for design is presented that exploits an equivalence to a single-agent problem with a different likelihood function, the optimal quantizers are thus different than in the single-agent case. Removing the constraint of identical quantizers is demonstrated to improve performance. A method for design is presented that exploits an equivalence between agents having diverse K-level quantizers and agents having identical (3K-2)-level quantizers.

The effect of quantization of prior probabilities in a collection of distributed Bayesian binary hypothesis testing problems over which the priors themselves vary is studied, with focus on conflicting agents. Conflict arises from differences in Bayes costs, even when all agents desire correct decisions and agree on the meaning of correct. In a setting with fusion of local binary decisions by majority rule, Nash equilibrium local decision strategies are found. Assuming that agents follow Nash equilibrium decision strategies, designing quantizers for prior probabilities becomes a strategic form game, we discuss its Nash equilibria. We also propose two different constrained quantizer design games, find Nash equilibrium quantizer designs, and compare performance. The system has deadweight loss: equilibrium decisions are not Pareto optimal.

Suppose a function of N real source variables X1N= (X1, X2, ..., XN) is desired at a destination constrained to receive a limited number of bits. If the result of evaluating the function, Y = G(X1N), can be itself encoded, this is the optimal strategy-the origin of Y becomes irrelevant to the communication problem. We consider two alternative scenarios: distributed quantization, in which each Ximust be separately encoded; and linear transform coding of X1N. Optimal fixed- and variable-rate scalar quantizers are derived under the conventional assumptions of high-resolution quantization theory, and we find optimal transforms for transform coding. For certain classes of functions, examples demonstrate large improvements over using quantizers designed to minimize distortion of the Xis.

The problem of communicating (unordered) sets, rather than (ordered) sequences is formulated. Elementary results in all major branches of source coding theory, including lossless coding, high-rate and low-rate quantization, and rate distortion theory are presented. In certain scenarios, rate savings of log n! bits for sets of size n are obtained. Asymptotically in the set size, the entropy rate is zero and for sources with an ordered parent alphabet, the (0,0) point is the rate distortion function.