Study identifies negative data externalities affecting model performance on specific groups.
problem Negative data externalities on group performance in machine learning models.
method Characterized and detected data-model inefficiencies, focusing on specific types of externalities.
result Negative data externalities can lower model performance on specific sub-groups, even with larger datasets.
Study finds apartment owners can set Airbnb policies to be socially efficient.
problem Negative externalities from Airbnb hosts on neighbors.
method Modeling competitive rental market and apartment sorting.
result Equilibrium Airbnb fraction is socially efficient if tenants can sort.
Study explores how exploration in online learning can unfairly burden certain users.
problem The unfairness of exploration in online learning algorithms.
method Introduced the concept of group externality and studied its impact under the linear contextual bandits model.
result Greedy algorithms can almost match the best possible Bayesian regret rate and eliminate negative group externalities under certain data diversity conditions.
Study examines remittances in Nepal, linking external demand and domestic monetary conditions.
problem Understanding the dynamics of remittances in Nepal's economy.
method Constructed composite indices via PCA for external demand and domestic monetary conditions. Used ARDL, cointegration, DOLS, ECM, and machine learning for analysis.
result Strong positive long-run effect of external demand on remittances, significant negative impact of tighter domestic monetary conditions.
This paper calculates risk-dependent centrality of Brazilian stocks, showing rankings vary with external risk and crisis events.
problem Understanding asset rankings in the Brazilian stock market under varying external risks.
method Computed risk-dependent centrality (RDC) for Brazilian stocks traded from 2008 to 2020, analyzing volatility and returns.
result Asset rankings based on RDC vary with external risk and crisis events, with higher volatility in crisis periods.
Dual Teaching improves semi-supervised learning for practical applications.
problem Difficulty in applying semi-supervised wrapper methods to real-world data.
method Dual Teaching uses two external classifiers to estimate and adjust for false positives and negatives, training a base learner from partially labeled data.
result Dual Teaching effectively trains a base learner from partially labeled data as effectively as a fully-labeled-data-trained classifier.
The study finds significant financial sector volatility and tail risk spillovers to real economy sectors.
problem Volatility and tail risk spillovers from financial to real economy sectors.
method New measure of tail risk spillover, empirical analysis of U.S. economy 2001-2011.
result Significant volatility and tail risk spillovers from financial to real economy sectors, especially during crises.
Study shows how Twitter users' sentiment levels correlate with network structure and external events.
problem Understanding how Twitter users' sentiment levels are influenced by network structure and external events.
method Used a large dataset of tweets, applied sentiment scoring algorithms, and created an agent-based model.
result People with larger communication reach use positive sentiment more often and negative sentiment less often.
The question of how to stabilize financial systems has attracted considerable attention since the global financial crisis of 2007-2009. Recently, Beale et al. ("Individual versus systemic risk and the regulator's dilemma", Proc Natl Acad Sci USA 108: 12647-12652, 2011) demonstrated that higher portfolio diversity among…
The negative externalities from an individual bank failure to the whole system can be huge. One of the key purposes of bank regulation is to internalize the social costs of potential bank failures via capital charges. This study proposes a method to evaluate and allocate the systemic risk to different countries/regions…
GCBS improves contrastive learning performance efficiently.
problem Efficiently mining hard negatives for contrastive learning.
method Global Contrastive Batch Sampling (GCBS) approximates batch assignment problem.
result GCBS improves state-of-the-art performance in sentence embedding and code-search tasks.
Enhances word embedding by transferring external knowledge.
problem Low-frequency words in semantic space.
method Latent Semantic Imputation (LSI) integrating graph theory and spectral embeddings.
result LSI generates reliable embedding vectors for low-frequency words.
Interpretable text-response modelling for structured outcomes
problem Predicting structured responses alongside textual data
method Joint non-negative matrix factorisation and binomial regression
result Recovering stable response-relevant textual signals
FinTech negatively impacts Chinese banks' financial sustainability.
problem Impact of FinTech on financial sustainability of Chinese commercial banks.
method Three-stage network DEA-Malmquist model and two-way fixed effects model.
result FinTech primarily undermines financial sustainability by eroding loan efficiency and profitability.
Deep neural network learns hierarchical language family structure.
problem Language family dependency and external information affect classification accuracy.
method Hierarchical attentive units learn auxiliary tasks for robust internal representation.
result Improved classification accuracy on small and big language corpora.
The study identifies and predicts extreme stock price fluctuations using HHT and SVM.
problem Sporadic large stock price fluctuations due to various factors.
method Hilbert-Huang Transformation (HHT) for identifying extreme events (EEs) and Support Vector Regression (SVR) for forecasting.
result High instantaneous energy concentration in stock price during both positive and negative extreme events.
Bayesian inference reconstructs external potentials in DFT for many-particle systems.
problem Reconstructing external potentials in classical density-functional theory (DFT) for many-particle systems.
method Combines Bayesian inference with classical DFT to probabilistically reconstruct external potentials.
result Accurately infers external potentials and density profiles with uncertainty quantification.
Study evaluates uncertainty in BP estimation from PPG signals under domain shift.
problem Uncertainty quantification in healthcare, especially for cuffless BP estimation.
method Compared deep ensembles, Monte Carlo dropout, and various recalibration techniques.
result Deep ensembles provide stronger robustness under domain shift.
We use the Chebyshev knot diagram model of Koseleff and Pecker in order to introduce a random knot diagram model by assigning the crossings to be positive or negative uniformly at random. We give a formula for the probability of choosing a knot at random among all knots with bridge index at most 2. Restricted to this c…
New analysis shows rational actors will deploy AGI despite negative social value due to catastrophic risk.
problem Rational actors will deploy AGI despite negative social value due to shared catastrophic risk.
method Continuous-time preemption game with shared catastrophic externalities, showing suicide region and welfare distortion.
result The suicide region widens as catastrophic risk grows, and two mechanisms can close it.
Optimized model tackles global industrial externalities in non-OECD countries.
problem Analyzing and mitigating externalities from pollution, agriculture, and energy industries in non-OECD countries.
method Integrated systems modeling approach combining fiscal, monetary policies and incentives for cooperative behavior.
result Proposes an optimized solution to internalize externalities in non-OECD countries.
Proposes a method to use external machine-learning predictions in multinomial logistic regression.
problem Improving statistical inference using summary-level external machine-learning predictions.
method Empirical-likelihood framework incorporating moment constraints from external nonparametric machine-learning predictions.
result Fused estimator achieves strict efficiency gain over primary-only estimator under mild conditions.
Algorithm learns to solve tasks by gradually expanding policy space.
problem Reinforcement learning difficulty progression.
method Decreasingly constrained policy space expansion.
result Superior learning rate in Tetris tasks.
The paper models insurance market dynamics under uncertainty and financial frictions.
problem Modeling insurer behavior under uncertainty and financial frictions.
method Dynamic equilibrium model of insurance market with competitive insurers maximizing shareholder value.
result Investment can lead to lower insurance prices and negative loadings under certain conditions.
Paper proposes AI for stock market forecasting using external knowledge.
problem Forecasting stock prices influenced by external factors.
method Learning from historical data and external temporal knowledge graphs modeled as Hawkes processes.
result Dynamic representations effectively rank stocks based on returns.
New estimator improves ATT estimation efficiency with external controls.
problem Reduced efficiency when incorporating external controls into ATT estimation.
method Proposes a novel doubly robust estimator for ATT that maintains higher efficiency than standard approaches.
result Demonstrates improved efficiency of the new estimator compared to standard approaches, even under model misspecification.
Method estimates model performance on external samples from limited statistical characteristics.
problem Limited access to multiple datasets due to privacy and commercial restrictions.
method Search for weights that match external statistics and are closest to uniform, using model performance on weighted internal sample as an estimation.
result Estimated external performance is closer to actual performance than internal performance.
The study assesses external validity by evaluating worst-case treatment effects across subpopulations.
problem Underrepresentation of marginalized groups and limited study populations.
method Develops a semiparametrically efficient estimator for worst-case treatment effects (WTE) and uses cross-fitting to guard against brittle findings.
result The proposed framework guards against invalid findings due to unanticipated population shifts.
A method for logistic regression inference using both internal and external data.
problem Inability to estimate intercept and marginal case proportion in case-control logistic regression.
method Empirical likelihood approach integrating internal and external data.
result Intercept parameter becomes identifiable with external information, and all parameters are estimable consistently.
Model predicts stock correlations based on investors' expected returns.
problem Lack of microscopic explanation for stock correlations.
method Agent-based model derived from minority game.
result Stock returns are positively/negatively correlated when agents' expected returns for one stock are positively/negatively correlated with the historical return of the other.
Economics examines social networks through externality effects.
problem Understanding how individual actions impact others in social networks.
method Analyzes network formation and interactions within networks from an economic perspective.
result Externalities are crucial in explaining network dynamics and behaviors.
Study long-term asset liquidation behavior with external flows.
problem Investigate optimal liquidation in presence of external flows.
method Convergence analysis of BSDEs for value function and strategy.
result Long-term liquidation may not occur due to external flows.
New measure of policy regret shows compatibility with traditional external regret in adversarial games.
problem Incompatibility between traditional and new policy regret measures in adaptive adversaries.
method Revisited policy regret and compared it with external regret; introduced policy equilibrium.
result Policy regret and external regret are compatible in adversarial games.
New algorithm BE reduces regret in bandit learning with positive externalities.
problem Self-reinforcing user preferences in platforms lead to suboptimal performance in bandit learning.
method Developed Balanced Exploration (BE) algorithm and an adaptive variant.
result BE and adaptive BE achieve optimal asymptotic regret in bandit learning with positive externalities.
Framework for estimating treatment effects using external control data.
problem Improving efficiency in estimating average treatment effects (ATE) in hybrid trials.
method Developed a formal causal inference framework based on exchangeability assumptions and graphical criteria. Proposed estimators and efficient doubly-robust methods.
result Established finite-sample performance and demonstrated application to spinal muscular atrophy trial.
Algorithm learns RBMs with arbitrary external fields, improving on previous constraints.
problem Learning RBMs with arbitrary external fields, improving on previous constraints.
method Greedy algorithm that maximizes covariance between observed nodes sharing latent neighbors.
result Algorithm can learn RBMs with arbitrary external fields, improving on previous constraints.
Survey of methods to incorporate external knowledge into stock price prediction.
problem Challenges in predicting stock prices due to market volatility and non-linearity.
method Survey of methods for acquiring and incorporating external knowledge into stock price prediction models.
result Systematic synthesis of previous studies on external knowledge types and their application in stock price prediction.
Estimates non-parametric logistic model using case-control data and external summary info.
problem Imbalanced binary data in case-control studies.
method Two-step estimation procedure with deep neural network for functional approximation.
result Proposed estimator achieves optimal convergence rate in non-parametric regression.
The importance of nodes in a network constantly fluctuates based on changes in the network structure as well as changes in external interest. We propose an evolving teleportation adaptation of the PageRank method to capture how changes in external interest influence the importance of a node. This framework seamlessly g…
UAMM uses external market prices to improve AMM efficiency and reduce liquidity provider risk.
problem Traditional AMMs lack consideration of external markets and risk management.
method UAMM calculates prices by incorporating external market prices and impermanent loss, maintaining constant product curve properties.
result UAMM eliminates arbitrage opportunities when external market prices are efficient, reducing liquidity provider risk.
Neural network memorizes external stimuli through synaptic strength changes.
problem Memory and classification in neural networks.
method One-to-one mapping between stimulus and synaptic strength under synaptic plasticity constraints.
result Neural network can memorize external stimuli through synaptic changes.
Enhances clustering by using external categorical data.
problem Improving clustering outcomes using external categorical evidence.
method Evidence transfer method that manipulates autoencoder latent representations based on external categorical data.
result Our method effectively manipulates latent representations with real evidence and remains robust with low quality evidence.
Model shows PoS networks can be captured by external finance, leading to centralization.
problem Long-term centralization of PoS networks under external finance pressures.
method Heterogeneous macroeconomic model with two actor classes: investors and consumers.
result External finance forces PoS networks to centralize, leading to zero internal staking yield.
The paper simplifies complex mechanical systems with external forces.
problem Analyzing symmetric discrete mechanical systems with external forces.
method Lagrangian reduction and reconstruction for principal bundles.
result Evolution of momentum maps and Poisson structures under different conditions.
Estimates personalized policies robust to shifts in target populations.
problem Estimating policies that perform well in diverse target populations.
method Develops methods for estimating robust policies considering shifts in outcomes and characteristics.
result Welfare-maximizing policies are robust to certain shifts in potential outcomes.
This paper addresses external validity bias in causal inference.
problem Estimating causal effects in a target population.
method Synthesis of approaches for generalizability and transportability, including tests for heterogeneity of treatment effects and differences between study and target populations.
result Framework for addressing external validity bias in causal inference.
Study finds multifractal cross-correlations between agricultural markets and external uncertainties.
problem Investigating relationships between agricultural spot markets and external uncertainties.
method Multifractal detrending moving-average cross-correlation analysis (MF-X-DMA).
result Maize exhibits intrinsic joint multifractality with all uncertainty proxies.
Study on how China's SMEs finance changed post-crisis, focusing on internal vs. external financing.
problem Analyzing SME financing problems before and after the global financial crisis.
method Regression analysis based on Trade-Off Theory, empirical research on 158 firms.
result SMEs with high growth rates are more likely to obtain external financing after a financial crisis.