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.
Delegated votes in Uniswap DAO favor parties with less self-owned votes and a16z-affiliated entities.
problem Incentives for vote delegation in decentralized governance systems.
method Analysis of Uniswap governance DAO using vote delegation data.
result Vote delegation patterns suggest window-dressing around decentralization and merit-based delegation.
Forward hedging reshapes incentive provision in firms.
problem How does forward hedging affect incentive provision in firms?
method We consider a CARA framework to jointly characterize optimal production, compensation, and static hedging in equilibrium.
result Delegation and external hedging are partial substitutes, and delegation can increase firm value even when the agent is more risk averse.
Adaptive AI delegation framework for dynamic decision authority allocation.
problem Dynamic allocation of decision authority to AI-generated recommendations under evolving evidence quality and uncertainty.
method Formulated as a Governance-Aware POMDP, using Bayesian inference for informational state estimation and sequential optimization for authority allocation.
result Sequential Bayesian governance provides the strongest general-purpose policy across AI-quality regimes, adapting to evolving evidence.
New algorithm helps avoid traps in reinforcement learning.
problem Existing reinforcement learning bounds assume no traps, limiting applicability.
method Introduces DRL (delegative reinforcement learning) allowing occasional advisor input.
result Derives a new regret bound without assuming episodic or trap-free environments.
Optimal contracts help principals delegate data collection in decentralized ML.
problem Dealing with information asymmetries in decentralized ML.
method Design of optimal and near-optimal contracts addressing uncertainty in model quality and performance.
result Simple linear contracts achieve 1-1/e fraction of optimal utility.
Human-AI teaming suffers from calibration issues.
problem Human-AI teaming
method Assume calibrated models and humans
result Existing methods for combination do not preserve human's calibration.
We prove optimal mechanisms for general contract spaces.
problem Optimal mechanism design under adverse selection and ambiguity.
method Existence proof for optimal mechanisms in general contract spaces.
result Centralized contracting is equivalent to delegated contracting.
A study on portfolio delegation with random default times, addressing complex uncertainties.
problem Optimal portfolio delegation with uncertain investment horizon due to random default.
method Developed a theoretical framework using BSDEs and control theory, and deep learning for high-dimensional problems.
result Solutions to integro-partial Hamilton-Jacobi-Bellman equations for both scenarios of default time.
Study on limits of LLM-based multi-agent planning reliability.
problem Reliability limits of LLM-based multi-agent planning.
method Modeling LLM-based multi-agent architecture as a decision network, showing dominance by centralized Bayes decision maker.
result Optimizing multi-agent directed acyclic graphs under communication budget is equivalent to choosing a constrained experiment.
AI task delegation faces incentive collapse with unbounded payments as AI accuracy rises.
problem Incentive collapse in AI-assisted task delegation schemes.
method General impossibility result and sentinel-auditing payment mechanism.
result Sentinel-auditing mechanism enforces positive human effort at finite cost, independent of AI accuracy.
Paper characterizes star-shaped risk measures and their properties.
problem Characterizing risk measures in the presence of liquidity risk and competitive delegation.
method Characterization of star-shaped risk measures, study of their properties.
result Star-shaped risk measures include all practically used risk measures.
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.
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.
Private credit markets have expanded significantly, offering unique lending technology to private equity firms.
problem Understanding the growth and characteristics of private credit markets.
method Systematic survey of academic literature, development of integrated theoretical framework, empirical evidence.
result Private credit markets offer a distinct lending technology with higher spreads over syndicated loans.
We analyze and quantify, in a financial market with parameter uncertainty and for a Constant Relative Risk Aversion investor, the utility effects of two different boundedly rational (i.e., sub-optimal) investment strategies (namely, myopic and unconditional strategies) and compare them between each other and with the u…
To answer questions about racial inequality and fairness, we often need a way to infer race and ethnicity from names. One way to infer race and ethnicity from names is by relying on the Census Bureau's list of popular last names. The list, however, suffers from at least three limitations: 1. it only contains last names…
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
As Machine Learning (ML) gets applied to security-critical or sensitive domains, there is a growing need for integrity and privacy for outsourced ML computations. A pragmatic solution comes from Trusted Execution Environments (TEEs), which use hardware and software protections to isolate sensitive computations from the…
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.
The paper introduces a new algorithm for fair decision-making in outcome control tasks.
problem Fair and equitable automated decision-making in outcome control tasks.
method Causal analysis and optimization to ensure fairness in decision-making.
result Developed an algorithm for maximizing Y while ensuring causal fairness. 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%.
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.
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.
We analyze conditional optimization problems arising in discrete time Principal-Agent problems of delegated portfolio optimization with linear contracts. Applying tools from Conditional Analysis we show that some results known in the literature for very specific instances of the problem carry over to translation invari…
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.
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.
We propose a Monte Carlo simulation method to generate stress tests by VaR scenarios under Solvency II for dependent risks on the basis of observed data. This is of particular interest for the construction of Internal Models and requirements on evaluation processes formulated in the Commission Delegated Regulation. The…
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 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.
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.
This short note is intended as a "Letter to the Editor" Perspective in order that it serves as a contribution, in view of reaching the physics community caring about rare events and scaling laws and unexpected findings, on a domain of wide interest: sport and money. It is apparent from the data reported and discussed b…
In this paper, we consider the problem of predicting demographics of geographic units given geotagged Tweets that are composed within these units. Traditional survey methods that offer demographics estimates are usually limited in terms of geographic resolution, geographic boundaries, and time intervals. Thus, it would…
We find a remarkable agreement between the statistics of a randomly divided interval and the observed statistical patterns and distributions found in horse racing betting markets. We compare the distribution of implied winning odds, the average true winning probabilities, the implied odds conditional on a win, and the …
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 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.
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.
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.
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.