Machine learning aids in bridging international news gaps.
problem Limited global understanding due to segmented news and language barriers.
method Combining machine learning, journalism studies, and statistics.
result Improved global perspective on international issues.
This article asks how planning scholarship may effectively gain impact in planning practice through media exposure. In liberal democracies the public sphere is dominated by mass media. Therefore, working with such media is a prerequisite for effective public impact of planning research. Using the example of megaproject…
ManifoldMind uses adaptive-curvature probabilistic spheres for trustworthy recommendations in semantic hierarchies.
problem Sparse and abstract recommendation domains where users explore diverse conceptual paths.
method Adaptive-curvature probabilistic spheres, soft multi-hop inference, and curvature-aware semantic kernel.
result Superior NDCG, calibration, and diversity compared to baselines on public benchmarks.
Researchers compute degrees of maps between SU(2)-bundles over 5-sphere.
problem Computing degrees of maps between SU(2)-bundles over the 5-sphere.
method Using Steenrod squares to identify obstructions and constructing explicit maps.
result Explicit construction of maps realizing each integer degree.
Study analyzes tweets from Northeastern libraries to understand public relations efforts.
problem Analyzing vast social media data for public relations.
method Computational methods to extract and analyze tweets.
result Libraries use Twitter to represent services and disseminate information.
The paper explores learning with a mix of private and public data while maintaining privacy.
problem Learning with a mix of private and public data while ensuring differential privacy.
method Designing a learning algorithm that satisfies differential privacy only with respect to private examples.
result A hypothesis class of VC-dimension d can be agnostically learned up to an excess error of α using only (roughly) d/α public examples and d/α^2 private labeled examples.
Study public-data assisted private stochastic optimization with labeled or unlabeled public data.
problem Limits and capability of public-data assisted differentially private (PA-DP) algorithms in stochastic convex optimization.
method Lower bounds for PA-DP mean estimation and novel methods for leveraging public data in private supervised learning.
result Achieved dimension independent rate for GLM with unlabeled public data, showing optimality.
Public pretraining improves private model training even in extreme distribution shift scenarios.
problem Improving private model training accuracy in settings with large distribution shift.
method Empirical evaluation and theoretical explanation of public representations improving private training accuracy.
result Public representations can improve private training accuracy by up to 67% over private training from scratch in settings with large distribution shift.
Private estimation with public data reduces sample complexity.
problem Estimating private distributions with limited public data.
method Differentially private estimation with public data under constraints of pure or concentrated DP.
result Public data can significantly reduce private sample complexity for estimation.
Algorithm selects public datasets for private machine learning.
problem Choosing the most suitable public dataset for private machine learning.
method Measures gradient subspace distance between public and private datasets.
result Excess risk scales with the subspace distance between gradients.
Developed Merton's model for public companies using observed liabilities.
problem Estimating default risk for public companies.
method Campbell and Shiller's approximation method for risk-neutral values and default probabilities.
result Formulas and ML estimators for public companies' default probabilities.
Private distribution learning with public data, leveraging sample compression schemes.
problem Private distribution learning with public and private samples under differential privacy constraints.
method Connection to sample compression schemes and list learning.
result At least d public samples are necessary for private learnability of Gaussians in R^d.
Study uses AI to predict changes in international public finances based on US markets.
problem Understanding correlations between US and international public finances.
method Artificial intelligence and neural networks to model and predict changes.
result Neural network model achieved MSE of 2.79, indicating significant correlation and impact of US market volatility on international markets.
ELM detects mislabels in Finnish academic publication ranks.
problem Detecting mislabeled academic publication ranks in Finland.
method Used Extreme Learning Machine (ELM) with features characterizing publication channels.
result ELM-based approach accurately detected mislabels compared to reference results.
Net2Vis automates CNN visualization for publications.
problem Lack of consistent visual representations in deep learning papers.
method Proposes a visual grammar and automated system for generating publication-ready CNN visualizations.
result Reduces time and ambiguity in generating network visualizations.
Polestar optimizes public transportation routes for efficiency and user satisfaction.
problem Difficulty in finding optimal public transportation routes due to complex networks and dynamic situations.
method Developed a Public Transportation Graph (PTG) and a route search algorithm with station binding and ranking modules.
result Demonstrated superior efficiency and user satisfaction compared to existing systems.
Study private query release with public data, reducing sample sizes.
problem Answering a wide range of statistical queries while maintaining privacy.
method Combines public and private samples to answer queries with differential privacy.
result Private and public sample complexities for different query classes.
Optimal DP model training with public data improves privacy and accuracy.
problem Ensuring privacy while training models with public data.
method Proves optimal error rates for DP model training with public data, develops novel algorithms.
result Optimal error rates can be achieved by using public data or optimal DP algorithms.
New private learning algorithms improve utility in tasks with public features.
problem Private learning with public features in recommendation and ad prediction.
method Developed algorithms that protect only certain sufficient statistics, improving utility for linear regression and private recommendation benchmarks.
result Achieved state-of-the-art performance on private recommendation benchmarks.
Publicly pretraining models on Web data may undermine differential privacy.
problem The use of large Web-scraped datasets in differential privacy models.
method Critical review of leveraging pretrained models on public datasets for differential privacy.
result Publicizing pretrained models as 'private' could harm trust and generalize poorly.
Efficiently learns private models using public data.
problem Improving private learning performance with public data.
method Proves computationally efficient algorithms for private learning with public data.
result First computationally efficient algorithms for private learning with public data.
We calculate the dynamics of tax evasion within a multi-agent econophysics model which is adopted from the theory of magnetism and previously has been shown to capture the main characteristics from agent-based based models which build on the standard Allingham and Sandmo approach. In particular, we implement a feedback…
Estimates citation impact to recommend best publication venue.
problem Choosing optimal publication venue for academic papers.
method Treatment effect estimation and bias correction method.
result Effective recommendation of publication venues based on citation potential.
Survey examines public views on facial recognition technology.
problem Public acceptance and privacy concerns with facial recognition.
method Cross-national survey of 4 countries.
result Public views vary significantly across countries.
Public benchmark for machine learning models in critical care.
problem Lack of public benchmarks for machine learning in critical care.
method Defined four tasks (mortality prediction, length of stay, phenotyping, decompensation risk) and compared clinical and deep learning models on eICU dataset.
result First public benchmark on multi-centre critical care dataset, comparing clinical models with predictive models.
The aim of the present article is to treat the Greek public debt issue strictly as a curve fitting problem. Thus, based on Eurostat data and using the Mathematica technical computing software, an exponential function that best fits the data is determined modelling how the Greek public debt expands with time. Exploring …
This paper provides a guide to using machine learning in public administration.
problem Lack of clarity in proper use and potential pitfalls of machine learning methods.
method Provides a foundational view of machine learning and demonstrates its use in public administration research.
result Machine learning techniques can enrich public administration research and practice.
Hurd's career overview and publications listed.
problem None explicitly stated in the abstract.
method Not applicable as it's an introduction.
result No specific key result mentioned.
Study links public concern in Italy to financial markets worldwide.
problem Understanding public concern's impact on financial markets during pandemics.
method Used Google Trends data from YouTube, News, and Search to measure public concern and correlate it with stock index returns.
result Public concern in Italy drives concerns in other countries and explains stock index returns of multiple nations.
Researchers organize and analyze a large public safety imagery dataset.
problem Efficiently organizing and analyzing a vast public safety imagery dataset.
method Hierarchical organization approach using Lincoln Laboratory Supercomputing Cluster for compute and storage.
result Successfully organized and evaluated the dataset with large-scale imagery inference across terabytes of data.
Improves zeroth-order optimization for private machine learning with public data.
problem High computation and memory cost of first-order DP methods.
method PAZO (Public Data Assisted Zeroth-order Optimization) framework.
result Achieves superior privacy/utility tradeoffs across tasks.
Paper introduces CageBO for optimizing complex public policy problems.
problem Complex decision-making and implicit constraints in public policy.
method CageBO framework using conditional variational autoencoder.
result CageBO outperforms baselines in optimizing large-scale police redistricting.
Bayesian market views improve asset allocation performance.
problem Leveraging public mood for trusted and interpretable asset allocation.
method Formalize public mood into market views, use Bayesian asset allocation model, train neural models.
result Formalized market views increase portfolio profitability by 5-10% annually.
Study of public and private VC relationships in France using qualitative methods.
problem Understanding interactions between public and private venture capitalists in France.
method Qualitative approach with semi-structured interviews and thematic content analysis.
result Formal or informal relationships between public and private VCs are a 'economico-cognitive' approach to networking and innovation.
New method uses public data to achieve optimal nonparametric classification with privacy constraints.
problem Optimal nonparametric classification under local differential privacy constraints.
method Locally differentially private classification tree with data-driven pruning.
result Achieves mini-max optimal convergence rate with public data.
This paper investigates the relationship between private and public investment in R&D, while taking into account the effect of several instruments policies such as subsidies and taxes. We design a new look of knowledge spillovers and R&D cooperation to explain the contribution of public and private R&D on growth. We pr…
Discond-VAE separates continuous and discrete factors in data.
problem Separating shared and class-specific variations in real-world data.
method Introduces private and public latent variables to represent continuous and discrete factors, respectively.
result Discond-VAE successfully disentangles class-dependent continuous factors from discrete factors.
An idea of Hopf's for applying complex analysis to the study of constant mean curvature spheres is generalized to cover a wider class of spheres, namely, those satisfying a Weingarten relation of a certain type, namely H = f(H^2-K) for some smooth function f, where H and K are the mean and Gauss curvatures, respectivel…
New method for private learning with public features improves convergence rates.
problem Private estimation with public features under local differential privacy.
method Semi-feature LDP, HistOfTree estimator.
result HistOfTree reaches mini-max optimal convergence rate.
TMLE improves unbiased estimation in public health studies.
problem Improving unbiased estimation in observational studies.
method Targeted Maximum Likelihood Estimation (TMLE) integrates machine learning and statistical theory.
result TMLE has been adopted by researchers worldwide, especially outside the US.
Paper uses Twitter data to analyze public opinion on economic issues during elections.
problem Limited, expensive, and time-consuming surveys for economic issues.
method Combines sentiment analysis and topic modeling for Twitter data.
result Effective analysis of economic concerns during the 2012 US presidential election.
PMT uses public data moments to make DP feasible for unbounded data.
problem Applying differential privacy to unbounded data distributions.
method Public-moment-guided Truncation (PMT) using second-moments from public data.
result PMT improves the accuracy and stability of DP models.
This study analyzes Twitter posts from US west coast libraries.
problem Lack of automated methods to analyze thousands of library tweets.
method Used Twitter API to collect and analyze 138,000 tweets.
result Identified 20 topics in library tweets, categorized into public relations, book, event, training, and social good.
Enhances DP linear regression using public data moments.
problem Limited utility of traditional DP methods in linear regression.
method Transform private data using public second-moment matrix for a better OLSE.
result Improved accuracy and robustness of OLSE in DP linear regression.
FASTER platform improves public transport commuter experience with analytics.
problem Operational challenges in urban public transport systems.
method Multi-sensor fusion, prediction, and optimization techniques.
result Improved commuter experience through fine-grained situational awareness and real-time decision support.
The topology of the intersection of two real homogeneous coaxial quadrics was studied by the second author who showed that its intersection with the unit sphere is in most cases diffeomorphic to a connected sum of sphere products. Combining that approach with a recent one (due to Antony Bahri, Martin Bendersky, Fred Co…
The study shows that achieving fairness in risk scores is impossible.
problem Achieving fairness in risk scores is impossible due to inherent trade-offs.
method Formalized three fairness conditions and proved that they cannot be satisfied simultaneously.
result There is no method that can satisfy all three fairness conditions simultaneously.
This paper relaxes the common prior assumption in the public and private information game of Morris and Shin (2000, 2004). For the generalized game, where the agent's prior expectations are heterogenous, it derives a sharp condition for the emergence of unique/multiple equilibria. This condition indicates that unique e…