Dream the Impossible: Outlier Imagination with Diffusion Models

Utilizing auxiliary outlier datasets to regularize the machine learning model has demonstrated promise for out-of-distribution (OOD) detection and safe prediction. Due to the labor intensity in data collection and cleaning, automating outlier data generation has been a long-desired alternative. Despite the appeal, generating photo-realistic outliers in the high dimensional pixel space has been an open challenge for the field. To tackle the problem, this paper proposes a new framework DREAM-OOD, which enables imagining photo-realistic outliers by way of diffusion models, provided with only the in-distribution (ID) data and classes. Specifically, DREAM-OOD learns a text-conditioned latent space based on ID data, and then samples outliers in the low-likelihood region via the latent, which can be decoded into images by the diffusion model. Different from prior works, DREAM-OOD enables visualizing and understanding the imagined outliers, directly in the pixel space. We conduct comprehensive quantitative and qualitative studies to understand the efficacy of DREAM-OOD, and show that training with the samples generated by DREAM-OOD can benefit OOD detection performance. Code is publicly available at https://github.com/deeplearning-wisc/dream-ood.Comment: NeurIPS 202

Heterogeneous Effects of Mortgage Rates on Housing Returns: Evidence from an Interacted Panel VAR

This paper develops a theoretical and empirical framework to assess the heterogeneous effects of mortgage rates on housing returns when accounting for the zero lower bound regime of the policy interest rate and local market supply and demand conditions. Based on an interacted panel VAR, estimated on a dataset comprising of 146 metropolitan statistical areas for a time period between January 1995 and December 2020, our empirical findings show that the response of housing returns to a mortgage rate shock is larger in magnitude when the federal funds rate is at its zero lower bound. Various supply and demand conditions, including housing permits, personal income, employment, and population, matter for the transmission of a mortgage rate shock to housing returns in local markets. A partial equilibrium model supports our empirical results

A Transformed System GMM Estimator for Dynamic Panel Data Models

The system GMM estimator developed by Blundell and Bond (1998) for dynamic panel data models has been widely used in empirical work; however, it does not perform well with weak instruments. This paper proposes a variation on the system GMM estimator, based on a simple transformation of the dependent variable. Simulation results indicate that, in finite samples, this transformed system GMM estimator greatly outperforms its conventional counterpart in estimating the coefficient of the lagged dependent variable, especially when the variation in the fixed effects is large relative to that in the idiosyncratic shocks and when the dependent variable is highly persistent. Applying this transformation also substantially strengthens the reliability of inferences on the overall model specification based upon the Sargan/Hansen test. As illustrations, the transformed system GMM estimator is applied to two empirical examples from the literature: a production function and an employment equation

Toward the Tradeoffs between Privacy, Fairness and Utility in Federated Learning

Federated Learning (FL) is a novel privacy-protection distributed machine learning paradigm that guarantees user privacy and prevents the risk of data leakage due to the advantage of the client's local training. Researchers have struggled to design fair FL systems that ensure fairness of results. However, the interplay between fairness and privacy has been less studied. Increasing the fairness of FL systems can have an impact on user privacy, while an increase in user privacy can affect fairness. In this work, on the client side, we use fairness metrics, such as Demographic Parity (DemP), Equalized Odds (EOs), and Disparate Impact (DI), to construct the local fair model. To protect the privacy of the client model, we propose a privacy-protection fairness FL method. The results show that the accuracy of the fair model with privacy increases because privacy breaks the constraints of the fairness metrics. In our experiments, we conclude the relationship between privacy, fairness and utility, and there is a tradeoff between these.Comment: 17 pages, 3 figures, conferenc

Learning interactions to boost human creativity with bandits and GPT-4

This paper considers how interactions with AI algorithms can boost human creative thought. We employ a psychological task that demonstrates limits on human creativity, namely semantic feature generation: given a concept name, respondents must list as many of its features as possible. Human participants typically produce only a fraction of the features they know before getting "stuck." In experiments with humans and with a language AI (GPT-4) we contrast behavior in the standard task versus a variant in which participants can ask for algorithmically-generated hints. Algorithm choice is administered by a multi-armed bandit whose reward indicates whether the hint helped generating more features. Humans and the AI show similar benefits from hints, and remarkably, bandits learning from AI responses prefer the same prompting strategy as those learning from human behavior. The results suggest that strategies for boosting human creativity via computer interactions can be learned by bandits run on groups of simulated participants

The effect in the film thickness reducing mechanism of functional groups in porous carbon sulfuric acid supercapacitor

In this paper, the different types and number of functional groups in porous carbon–carbon pore channels are discussed in the thinning mechanism of ionic solvent thin films, which has a significant impact on the absorption of H2SO4 electrolyte based Electric Double Layer Capacitors (EDLC). By exploring the binding energy of –OH, –COOH, –SO3H, –NO2 and other four functional groups with sulfuric acid and hexahydrate sulfuric acid of porous carbon channel and hexahydrate sulfuric acid, it was found that –OH had no repulsive effect on the cathode of the battery, and –COOH, –SO3H, –NO2 and other functional groups had obvious repulsive effect on the cathode of EDLC with the increase of the functional groups number, that is, there was an effect of increasing the capacitance of EDLC by increasing the number of sulfide molecular. This will excavate the potential electrode material in the practical application

OptScaler: A Hybrid Proactive-Reactive Framework for Robust Autoscaling in the Cloud

Autoscaling is a vital mechanism in cloud computing that supports the autonomous adjustment of computing resources under dynamic workloads. A primary goal of autoscaling is to stabilize resource utilization at a desirable level, thus reconciling the need for resource-saving with the satisfaction of Service Level Objectives (SLOs). Existing proactive autoscaling methods anticipate the future workload and scale the resources in advance, whereas the reliability may suffer from prediction deviations arising from the frequent fluctuations and noise of cloud workloads; reactive methods rely on real-time system feedback, while the hysteretic nature of reactive methods could cause violations of the rigorous SLOs. To this end, this paper presents OptScaler, a hybrid autoscaling framework that integrates the power of both proactive and reactive methods for regulating CPU utilization. Specifically, the proactive module of OptScaler consists of a sophisticated workload prediction model and an optimization model, where the former provides reliable inputs to the latter for making optimal scaling decisions. The reactive module provides a self-tuning estimator of CPU utilization to the optimization model. We embed Model Predictive Control (MPC) mechanism and robust optimization techniques into the optimization model to further enhance its reliability. Numerical results have demonstrated the superiority of both the workload prediction model and the hybrid framework of OptScaler in the scenario of online services compared to prevalent reactive, proactive, or hybrid autoscalers. OptScaler has been successfully deployed at Alipay, supporting the autoscaling of applets in the world-leading payment platform