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1. Housing, Distribution, and Welfare

   https://doi.org/10.1111/jmcb.13136  

   Kiyotaki, Nobuhiro; Michaelides, Alexander; Nikolov, Kalin (February 2024, Journal of money credit and banking)

   Housing is a long-lived asset whose value is sensitive to variations in expectations of long-run growth rates and interest rates. When a large fraction of households has leverage, housing price fluctuations cause large-scale redistribution and consumption volatility.We find that a practical way to insure the young and the poor from the housing market fluctuations is through awell-functioning rental market. In practice, homeownership subsidies keep the rental market small and the housing cycle affects aggregate consumption. Removing homeownership subsidies hurts old homeowners, while leverage limits hurt young homeowners 

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2. Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization

   https://doi.org/10.1109/ICDM59182.2024.00108  

   Wang, Song; Yang, Xiaodong; Islam, Rashidul; Chen, Huiyuan; Xu, Minghua; Li, Jundong; Cai, Yiwei (December 2024, IEEE)

   To deal with distribution shifts in graph data, various graph out-of-distribution (OOD) generalization techniques have been recently proposed. These methods often employ a two-step strategy that first creates augmented environments and subsequently identifies invariant subgraphs to improve generalizability. Nevertheless, this approach could be suboptimal from the perspective of consistency. First, the process of augmenting environments by altering the graphs while preserving labels may lead to graphs that are not realistic or meaningfully related to the origin distribution, thus lacking distribution consistency. Second, the extracted subgraphs are obtained from directly modifying graphs, and may not necessarily maintain a consistent predictive relationship with their labels, thereby impacting label consistency. In response to these challenges, we introduce an innovative approach that aims to enhance these two types of consistency for graph OOD generalization. We propose a modifier to obtain both augmented and invariant graphs in a unified manner. With the augmented graphs, we enrich the training data without compromising the integrity of label-graph relationships. The label consistency enhancement in our framework further preserves the supervision information in the invariant graph. We conduct extensive experiments on real-world datasets to demonstrate the superiority of our framework over other state-of-the-art baselines. 

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3. The generalized Poisson-binomial distribution and the computation of its distribution function

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   Zhang, Man; Hong, Yili; Balakrishnan, Narayanaswamy (February 2018, Journal of Statistical Computation and Simulation)
4. Formalizing and Estimating Distribution Inference Risks

   https://doi.org/10.56553/20220121  

   Suri, Anshuman; Evans, David (October 2022, Proceedings on Privacy Enhancing Technologies)

   Distribution inference, sometimes called property inference, infers statistical properties about a training set from access to a model trained on that data. Distribution inference attacks can pose serious risks when models are trained on private data, but are difficult to distinguish from the intrinsic purpose of statistical machine learning—namely, to produce models that capture statistical properties about a distribution. Motivated by Yeom et al.’s membership inference framework, we propose a formal definition of distribution inference attacks general enough to describe a broad class of attacks distinguishing between possible training distributions. We show how our definition captures previous ratio-based inference attacks as well as new kinds of attack including revealing the average node degree or clustering coefficient of training graphs. To understand distribution inference risks, we introduce a metric that quantifies observed leakage by relating it to the leakage that would occur if samples from the training distribution were provided directly to the adversary. We report on a series of experiments across a range of different distributions using both novel black-box attacks and improved versions of the state-of-the-art white-box attacks. Our results show that inexpensive attacks are often as effective as expensive meta-classifier attacks, and that there are surprising asymmetries in the effectiveness of attacks. 

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5. Parton Distribution Functions and Lattice QCD

   https://doi.org/10.1051/epjconf/201920601003  

   Lin, Huey-Wen; Yang, A.; Wang, W.Y.; Ng, S.C.C.; Chan, A.H.; Oh, C.H.; Phua, K.K. (January 2019, EPJ Web of Conferences)

   Recently, there have been rapid developments in lattice-QCD calculations of proton structure, especially in the parton distribution functions (PDFs). We overcame a longstanding obstacle and for the first time in lattice-QCD are able to directly calculate the Bjorken- x dependence of the quark, helicity and transversity distributions. The PDFs are obtained using the large-momentum effective field theory (LaMET) framework where the full Bjorken- x dependence of finite-momentum PDFs, called “quasi-PDFs”, can be calculated on the lattice. The quasi-PDF nucleon matrix elements are renormalized non-perturbatively in RI/MOM-scheme. Following a nonperturbative renormalization of the parton quasi-distribution in a regularization-independent momentum-subtraction scheme, we establish its matching to the $$ \overline {{\rm{MS}}} $$ PDF and calculate the non-singlet matching coefficient at next-to-leading order in perturbation theory. In this proceeding, I will show the progress that has been made in recent years, highlighting the latest state-of-the art PDF calculations at the physical pion mass. Future impacts on the large- x global PDF fits are also discussed. 

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