A key problem in mechanism design is the construction of protocols that reach socially efficient decisions with minimal information revelation. This can reduce agent communication, and further, potentially increase privacy in the sense that agents reveal no more private information than is needed to determine an optimal outcome. This is not always possible: previous work has explored the tradeoff between communication cost and efficiency, and more recently, communication and privacy. We explore a third dimension: the tradeoff between privacy and efficiency. By sacrificing efficiency, we can improve the privacy of a variety of existing mechanisms. We analyze these tradeoffs in both second-price auctions and facility location problems (introducing new incremental mechanisms for facility location along the way). Our results show that sacrifices in efficiency can provide gains in privacy (and communication), in both the average and worst case.

For the problem of allocating resources among multiple strategic agents, the well-known Vickrey-Clarke-Groves (VCG) mechanism is efficient, strategy-proof, and it never incurs a deficit. However, in general, under the VCG mechanism, payments flow out of the system of agents, which reduces the agents' utilities. VCG redistribution mechanisms aim to return as much of the VCG payments as possible back to the agents, without affecting the desirable properties of the VCG mechanism. Most previous research on VCG redistribution mechanisms has focused on settings with homogeneous items and/or settings with unit-demand agents. In this paper, we study VCG redistribution mechanisms in the more general setting of combinatorial auctions. We show that when the gross substitutes condition holds, we are able to design mechanisms that guarantee to redistribute a large fraction of the VCG payments.

Nonnegative Matrix Factorization (NMF) techniques has aroused considerable interests from the field of artificial intelligence in recent years because of its good interpretability and computational efficiency. However, in many real world applications, the data features usually evolve over time smoothly. In this case, it would be very expensive in both computation and storage to rerun the whole NMF procedure after each time when the data feature changing. In this paper, we propose Evolutionary Nonnegative Matrix Factorization (eNMF), which aims to incrementally update the factorized matrices in a computation and space efficient manner with the variation of the data matrix. We devise such evolutionary procedure for both asymmetric and symmetric NMF. Finally we conduct experiments on several real world data sets to demonstrate the efficacy and efficiency of eNMF.

A main challenging problem for many machine learning and data mining applications is that the amount of data and features are very large, so that low-rank approximations of original data are often required for efficient computation. We propose new multi-level clustering based low-rank matrix approximations which are comparable and even more compact than Singular Value Decomposition (SVD). We utilize the cluster indicators of data clustering results to form the subspaces, hence our decomposition results are more interpretable. We further generalize our clustering based matrix decompositions to tensor decompositions that are useful in high-order data analysis. We also provide an upper bound for the approximation error of our tensor decomposition algorithm. In all experimental results, our methods significantly outperform traditional decomposition methods such as SVD and high-order SVD.

We present a new approach to characterizing the semantics for the integration of rules and first-order logic in general, and description logics in particular, based on a circumscription characterization of answer set programming, introduced earlier by Lin and Zhou. We show that both Rosati's semantics based on NM-models and Lukasiewicz's answer set semantics can be characterized by circumscription, and the difference between the two can be seen as a matter of circumscription policies. This approach leads to a number of new insights. First, we rebut a criticism on Lukasiewicz's semantics for its inability to reason for negative consequences. Second, our approach leads to a spectrum of possible semantics based on different circumscription policies, and shows a clear picture of how they are related. Finally, we show that the idea of this paper can be applied to first-order general stable models.

We address the problem of transferring information learned from experiments to a different environment, in which only passive observations can be collected. We introduce a formal representation called "selection diagrams" for expressing knowledge about differences and commonalities between environments and, using this representation, we derive procedures for deciding whether effects in the target environment can be inferred from experiments conducted elsewhere. When the answer is affirmative, the procedures identify the set of experiments and observations that need be conducted to license the transport. We further discuss how transportability analysis can guide the transfer of knowledge in non-experimental learning to minimize re-measurement cost and improve prediction power.

Description Logics (DLs) provide a clear and broadly accepted paradigm for modeling and reasoning about terminological knowledge. However, it has been often noted, that although DLs are well-suited for representing a single, global viewpoint on an application domain, they offer no formal grounding for dealing with knowledge pertaining to multiple heterogeneous viewpoints — a scenario ever more often approached in practical applications, e.g. concerned with reasoning over distributed knowledge sources on the Semantic Web. In this paper, we study a natural extension of DLs, in the style of two-dimensional modal logics, which supports declarative modeling of viewpoints as contexts, in the sense of McCarthy, and their semantic interoperability. The formalism is based on two-dimensional semantics, where one dimension represents a usual object domain and the other a (possibly infinite) domain of viewpoints, addressed by additional modal operators and a metalanguage, on the syntactic level. We systematically introduce a number of expressive fragments of the proposed logic, study their computational complexity and connections to related formalisms.

We introduce aProbLog, a generalization of the probabilistic logic programming language ProbLog. An aProbLog program consists of a set of definite clauses and a set of algebraic facts; each such fact is labeled with an element of a semiring. A wide variety of labels is possible, ranging from probability values to reals (representing costs or utilities), polynomials, Boolean functions or data structures. The semiring is then used to calculate labels of possible worlds and of queries. We formally define the semantics of aProbLog and study the aProbLog inference problem, which is concerned with computing the label of a query. Two conditions are introduced that allow one to simplify the inference problem, resulting in four different algorithms and settings. Representative basic problems for each of these four settings are: is there a possible world where a query is true (SAT), how many such possible worlds are there (#SAT), what is the probability of a query being true (PROB), and what is the most likely world where the query is true (MPE). We further illustrate these settings with a number of tasks requiring more complex semirings.

The fact that birds have feathers and ice is cold seems trivially true. Yet, most machine-readable sources of knowledge either lack such common sense facts entirely or have only limited coverage. Prior work on automated knowledge base construction has largely focused on relations between named entities and on taxonomic knowledge, while disregarding common sense properties. In this paper, we show how to gather large amounts of common sense facts from Web n-gram data, using seeds from the ConceptNet collection. Our novel contributions include scalable methods for tapping onto Web-scale data and a new scoring model to determine which patterns and facts are most reliable. The experimental results show that this approach extends ConceptNet by many orders of magnitude at comparable levels of precision.

Blocking is a technique commonly used in manual statistical analysis to account for confounding variables. However, blocking is not currently used in automated learning algorithms. These algorithms rely solely on statistical conditioning as an operator to identify conditional independence. In this work, we present relational blocking as a new operator that can be used for learning the structure of causal models. We describe how blocking is enabled by relational data sets, where blocks are determined by the links in the network. By blocking on entities rather than conditioning on variables, relational blocking can account for both measured and unobserved variables. We explain the mechanism of these methods using graphical models and the semantics of d-separation. Finally, we demonstrate the effectiveness of relational blocking for use in causal discovery by showing how blocking can be used in the causal analysis of two real-world social media systems.