Models predict race and ethnicity from names, improving accuracy over census data.
problem Inferring race and ethnicity from names, especially when first names are available.
method Modeling the relationship between characters in a name and race/ethnicity using Long Short-Term Memory.
result Long Short-Term Memory model achieves out-of-sample accuracy of 0.85.
LDR models survival with competing risks using nonparametric Bayesian approach.
problem Survival analysis with competing risks and non-monotonic covariate effects.
method Lomax delegate racing, data augmentation, Gibbs sampler, stochastic gradient descent.
result Distinguished performance in survival analysis with competing risks.
Improved surname geocoding and name supplements enhance race imputation accuracy.
problem Census data problems affecting race imputation accuracy.
method Fully Bayesian Improved Surname Geocoding (fBISG) and name supplements.
result Significant improvement in race imputation accuracy across all racial groups.
Develops a method to quantify racial bias in law enforcement systems.
problem Quantify racial bias in law enforcement systems considering criminality and multi-stage interactions.
method Multi-stage causal framework incorporating criminality.
result Identifies three canonical scenarios of racial bias in law enforcement.
fairadapt uses causal inference to mitigate algorithmic bias in data pre-processing.
problem Mitigating algorithmic bias in machine learning predictions.
method Causal graphical model and observed data to address counterfactual questions.
result The method can help eliminate discrimination and justify fair decisions.
Model shows how investment traps wealth across generations.
problem Existence of wealth traps between social strata.
method Developed a model linking investment and intergenerational wealth.
result Proved a `rat race' theorem showing investment traps wealth.
Reduces gender classification bias by learning race-invariant face representations.
problem Societal bias in gender recognition systems.
method Adversarially trained autoencoder model to learn race-invariant face representations.
result Achieved a significant drop of over 40% in racial bias surrogate metric with race invariant representations.
Develops a learning model predictive controller for competitive racing.
problem Lack of exploration in state space and complexity in obstacle avoidance.
method Explores state space through multiple initializations and develops a new method for convex terminal set selection.
result Yields a richer terminal safe set and maintains convexity.
Modeling horse race betting odds with Ornstein-Uhlenbeck process.
problem Analyzing how herding and informed bettors affect odds movements.
method Deriving an Ornstein-Uhlenbeck process from vote shares and odds movements data.
result Identified microscopic and macroscopic patterns in odds convergence.
Deep RL drone trained to compete against classical path planning in drone racing.
problem Optimizing long-term drone racing strategies using reinforcement learning.
method Used PPO algorithm on a simulated quadrotor in a racing environment created with AirSim.
result Deep RL agent outperformed classical path planning in drone racing competitions.
DeepRacing uses neural networks to predict trajectories for autonomous racing in video games.
problem Training algorithms for high-speed autonomous racing in realistic environments.
method Developed a virtual testbed using F1 video games, trained neural networks to predict trajectories and control commands.
result Trajectory prediction outperforms end-to-end control methods in autonomous racing simulations.
Horse racing odds match random division statistics.
problem Understanding the distribution of horses' winning abilities.
method Comparing horse racing data with the 'randomly broken stick' problem.
result Horses' winning abilities are exponentially distributed.
Overlearning exposes hidden, sensitive attributes in models, threatening privacy and bias.
problem Models learn unintended, sensitive attributes beyond their training objectives.
method Demonstrated and analyzed overlearning in vision and NLP models.
result Overlearning reveals sensitive attributes that break privacy protections and can be re-purposed for harmful tasks.
Black women and white men have the highest income disparity in the U.S.
problem Income inequality between black women and white men in the USA
method Dynamic microeconomic model, analyzing black and white population income since 1930
result Black females and white males are poles of overall income inequality
Improved race prediction model outperforms existing methods.
problem Improving race prediction using voter registration data.
method Trained BiLSTM model on voter registration data and created an ensemble.
result Achieved up to 36.8% higher OOS F1 scores than previous models.
Method debiases alternative data for fair credit underwriting.
problem Bias in alternative data affecting credit underwriting fairness.
method Causal inference applied to machine learning models.
result Improves model accuracy across racial groups without discrimination.
RankNet forecasts car racing positions with improved accuracy and stability.
problem Forecasting rank positions in car racing, especially considering pit stops.
method Cause-effect decomposition in RankNet, incorporating probabilistic forecasting.
result RankNet outperforms baselines significantly, improving MAE by over 10%.
ProMoD models human race drivers with probabilistic movement primitives and neural networks.
problem Challenging task of modeling human driver behavior due to variability and complexity.
method Modular framework with Probabilistic Movement Primitives, clothoids, and neural networks.
result Significant advantages in imitation accuracy and robustness compared to other algorithms.
BBE simulates sports betting exchanges for data generation.
problem Creating synthetic data for betting strategy testing.
method Agent-based model (ABM) for sports betting exchange simulation.
result Simulation runs up to 1000 times faster with GPU.
A new algorithm resamples Bernoulli race particle filters using true weights.
problem Handling intractable weights in particle filters.
method Proposes a novel resampling method using true weights with an unbiased estimator.
result Demonstrates lower variance in filtering estimates compared to standard methods.
Lognormal distribution used for predicting team rankings in an orienteering relay race.
problem Predicting final team rankings in an orienteering relay race.
method Used lognormal distribution and Fenton-Wilkinson approximations for order statistics.
result Accurate predictions of team rankings using order statistics.
New method detects bias in AI models that generate data.
problem Detecting bias in AI models that generate data.
method Formalized causal fairness in generative AI, derived new decomposition results, established identification conditions, and introduced efficient estimators.
result Demonstrated the value of new methodology in analyzing bias in large language models.
A new model predicts race places using changeover-times and log-normal distributions.
problem Predicting race places in orienteering races.
method Fenton-Wilkinson Order Statistics model based on log-normal leg-times and changeover-times.
result The model accurately predicts race places with smaller root-mean-square-errors.
Overparameterized models are more vulnerable to membership inference attacks.
problem Vulnerability of overparameterized models to membership inference attacks.
method Theoretical and empirical analysis of overparameterized linear and ridge-regularized linear regression models in the Gaussian data setting.
result Increased number of parameters and model complexity increase vulnerability to membership inference attacks.
A method for selective classification using gambling-inspired loss function.
problem Achieving high performance while covering all data points.
method Transformed classification to selective classification problem, using portfolio theory-inspired loss function.
result Our method identifies uncertainty and achieves strong results on datasets.
Bayesian optimisation finds optimal race track driving policies.
problem Optimizing a robot's race track performance with limited interactions.
method Sequential coordinate descent Bayesian optimisation in RKHS.
result Algorithm finds optimal policies with minimal interactions.
Paper predicts demographics at finer geographic resolutions using geotagged tweets.
problem Limited traditional survey methods for demographics estimates at finer geographic resolutions.
method Adapting prior work to predict gender and race/ethnicity counts at the blockgroup-level.
result Achieves high correlations (0.671 for gender, 0.692 for race) compared to prior work.
New method debiases word embeddings for multiclass settings like race and religion.
problem Word embeddings in online texts perpetuate human stereotypes, including race and religion.
method Proposes a novel methodology to debias word embeddings in multiclass settings.
result Demonstrates robust multiclass debiasing that maintains NLP task efficacy.
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.
New approach to fairness in machine learning using contrastive questions.
problem Ensuring fairness in algorithmic decision-making.
method Causal inference to address contrastive fairness.
result Mathematical tools for contrastive fairness in machine learning.
New framework improves fraud prediction with incremental data balancing for massive data streams.
problem Class imbalance problem in massive imbalanced data streams.
method Incremental data balancing framework using Racing Algorithm for automated balancing and Random Forest for classification.
result Better results than Batch mode on European Credit Card dataset.
Proposes a method to enforce fairness in machine learning models without sensitive data.
problem Bias in machine learning models from historical data.
method Infers sensitive attributes from auxiliary features and integrates fairness constraints into model training.
result Mitigates bias while preserving predictive accuracy.
This work improves autonomous racing by creating diverse opponents and adapting risk.
problem Balancing performance and safety in autonomous racing environments.
method Developed a self-play method using replica-exchange Markov chain Monte Carlo for diverse opponents and a distributionally robust bandit optimization for adaptive risk adjustment.
result Demonstrated real-time motion-planning methods achieving speeds comparable to Formula One racecars.
Study compares SPG and PPO for racing games, finding SPG more stable with weighted actions.
problem Training continuous action reinforcement learning algorithms for racing games.
method Introduced novel racing environment, tested SPG and PPO with modifications and experience replay.
result Experience replay not beneficial for PPO in continuous action spaces, SPG more stable with weighted actions.
The paper develops a framework for fair machine learning predictions.
problem Fairness in machine learning models for legal and ethical decisions.
method Uses causal inference to model fairness, defining counterfactual fairness.
result Demonstrates the framework on predicting success in law school.
Simple scaling law explains gains of Tour de France teams.
problem Understanding financial gains of competitive sports teams.
method Analysis of recent bicycle race data.
result Simple scaling law describes gains of teams in recent bicycle races.
Study removes bias from chest X-ray embeddings using orthogonalization.
problem Reduces bias in chest X-ray embeddings due to protected features.
method Orthogonalization technique to remove protected feature effects.
result Orthogonalization removes bias and makes predictions of protected attributes infeasible.
New methods for fair machine learning reduce bias in data analysis.
problem Bias in machine learning algorithms that treat sensitive features like gender or race.
method Novel fair regression and dimensionality reduction methods using Hilbert Schmidt independence criterion and kernel functions.
result The methods simplify the problem and allow dealing with multiple sensitive variables simultaneously.
New approach to counterfactual reasoning avoids demographic interventions.
problem Limitations of traditional counterfactual reasoning in AI systems.
method Backtracking counterfactual approach instead of interventional.
result Allows addressing social concerns without demographic interventions.
New research shows adversarial defenses increase risk of membership inference attacks.
problem Combining security and privacy domains to measure adversarial defenses' impact on membership inference attacks.
method Measuring the success of membership inference attacks against six state-of-the-art adversarial defense methods.
result Adversarial defense methods can increase the risk of membership inference attacks.
The paper examines fairness issues in decision-making systems when protected class labels are unobserved.
problem Fairness assessment challenges when protected class labels are unavailable.
method Decomposes biases in estimating outcome disparity via threshold-based imputation and proposes a weighted estimator.
result Threshold-based imputation generally overestimates disparities, while the weighted estimator has a simpler negative bias.
The paper tackles fair decision-making by correcting biases in data.
problem Discriminatory biases in data and decision-making perpetuate injustice.
method Causal inference and constrained optimization to learn fair policies.
result The approach ensures fair policies that satisfy given constraints.
Perceptual ad-blocking is vulnerable to attacks, creating new security risks.
problem Vulnerability of perceptual ad-blocking to attacks and new security risks.
method Analysis and creation of adversarial examples to bypass perceptual ad-blocking.
result Perceptual ad-blocking can be bypassed using adversarial examples, introducing new security risks.
Efficient Thompson sampling for non-conjugate priors.
problem Thompson sampling's computational intractability with non-conjugate priors.
method Reformulate Thompson sampling as an optimization problem and use Gumbel-Max trick.
result Proposes an efficient algorithm for non-conjugate priors.
China and EU race to develop hydrogen for energy transition.
problem Developing hydrogen for sustainable energy systems.
method Comparative analysis framework using key factors.
result Customized solutions for local hydrogen industries.
BBE simulates betting exchanges to generate synthetic data for AI research.
problem Lack of real data for AI/ML in betting exchanges.
method Agent-based simulation model of a sports-betting exchange.
result Generates large, high-resolution synthetic data for AI/ML.
Proposes adversarial learning for counterfactual fairness in machine learning.
problem Ensuring fairness at the individual level by simulating counterfactual samples.
method Adversarial neural learning approach to infer counterfactual samples.
result Significant improvements in counterfactual fairness for both discrete and continuous settings.
Bayesian models evaluate sentence comprehension, showing direct access model fits data better.
problem Evaluating models of retrieval in sentence comprehension.
method Implemented Bayesian hierarchical models to compare activation-based and direct access models.
result Direct access model fits data better than activation-based model.