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Register a new userStrategy-proofness on the Non-Paretian Subdomain
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Let g be a strategy-proof rule on the domain NP of profiles where no alternative Pareto-dominates any other. Then we establish a result with a Gibbard-Satterthwaite flavor: g is dictatorial if its range contains at least three alternatives.
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We consider fair division problems where indivisible items arrive one-by-one in an online fashion and are allocated immediately to agents who have additive utilities over these items. Many existing offline mechanisms do not work in this online setting. In addition, many existing axiomatic results often do not transfer from the offline to the online setting. For this reason, we propose here three new online mechanisms, as well as consider the axiomatic properties of three previously proposed online mechanisms. In this paper, we use these mechanisms and characterize classes of online mechanisms that are strategy-proof, and return envy-free and Pareto efficient allocations, as well as combinations of these properties. Finally, we identify an important impossibility result.
Let g be a strategy-proof rule on the domain NP of profiles where no alternative Pareto-dominates any other and let g have range S on NP. We complete the proof of a Gibbard-Satterthwaite result - if S contains more than two elements, then g is dictatorial - by establishing a full range result on two subdomains of NP.
Let $f$ be a non-negative square-integrable function on a finite volume hyperbolic surface $Gammabackslashmathbb{H}$, and assume that $f$ is non-autocorrelated, that is, perpendicular to its image under the operator of averaging over the circle of a fixed radius $r$. We show that in this case the support of $f$ is small, namely, it satisfies $mu(supp{f}) leq (r+1)e^{-frac{r}{2}} mu(Gammabackslashmathbb{H})$. As a corollary, we prove a lower bound for the measurable chromatic number of the graph, whose vertices are the points of $Gammabackslashmathbb{H}$, and two points are connected by an edge if there is a geodesic of length $r$ between them. We show that for any finite covolume $Gamma$ the measurable chromatic number is at least $e^{frac{r}{2}}(r+1)^{-1}$.
Given a domain $G subsetneq Rn$ we study the quasihyperbolic and the distance ratio metrics of $G$ and their connection to the corresponding metrics of a subdomain $D subset G$. In each case, distances in the subdomain are always larger than in the original domain. Our goal is to show that, in several cases, one can prove a stronger domain monotonicity statement. We also show that under special hypotheses we have inequalities in the opposite direction.
Reducing domain divergence is a key step in transfer learning problems. Existing works focus on the minimization of global domain divergence. However, two domains may consist of several shared subdomains, and differ from each other in each subdomain. In this paper, we take the local divergence of subdomains into account in transfer. Specifically, we propose to use low-dimensional manifold to represent subdomain, and align the local data distribution discrepancy in each manifold across domains. A Manifold Maximum Mean Discrepancy (M3D) is developed to measure the local distribution discrepancy in each manifold. We then propose a general framework, called Transfer with Manifolds Discrepancy Alignment (TMDA), to couple the discovery of data manifolds with the minimization of M3D. We instantiate TMDA in the subspace learning case considering both the linear and nonlinear mappings. We also instantiate TMDA in the deep learning framework. Extensive experimental studies demonstrate that TMDA is a promising method for various transfer learning tasks.
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