AI predicts employee attrition to prevent turnover.
problem Predicting and preventing employee attrition.
method Ensemble classification and Linear Regression models.
result Predicts employee attrition with over 91% accuracy.
The paper examines how risk reduction and insurance choices interact under convex premium principles.
problem Interaction between self-protection and insurance demand under convex premium principles.
method Investigates optimal prevention efforts and insurance shares using distortion risk measures.
result Self-protection and insurance are complementary, but ex ante moral hazard can turn this into a substitution effect.
We provide direct evidence of market manipulation at the beginning of the financial crisis in November 2007. The type of manipulation, a "bear raid," would have been prevented by a regulation that was repealed by the Securities and Exchange Commission in July 2007. The regulation, the uptick rule, was designed to preve…
The importance of the global financial system cannot be exaggerated. When a large financial institution becomes problematic and is bailed out, that bank is often claimed as "too big to fail". On the other hand, to prevent bank's failure, regulatory authorities adopt the Prompt Corrective Action (PCA) against a bank tha…
Framework insures AI actions with reserve capital, preventing loss.
problem Ensuring safety and accountability for AI actions with varying side effects.
method Developed Actuarial Action Interface (AAI) and Authority Frontier to price and gate AI actions.
result Found common refusal and release patterns across domains, with varying required reserve capital.
New groups prevent certain geometric actions on spaces.
problem Preventing certain geometric actions on spaces.
method Analyzing cyclic orders on boundaries of trees.
result Groups prevent actions on PD(n) spaces.
The paper gauges non-linear sigma models using Lie algebroid actions.
problem Gauging non-linear sigma models with Lie algebroid actions.
method Proposes gauging non-linear sigma models with Lie algebroid actions and discusses conditions for gauging.
result It is possible to find a set of vector fields which will (locally) admit a Lie algebroid gauging.
Study optimal contracts for pandemic risk, offering fixed shares and prevention mechanisms.
problem Optimal delegation contracts in the face of pandemic shutdown risk.
method Dynamic principal-agent model with exogenous early termination risk.
result Explicit characterization of optimal wage and action for prevention mechanisms.
A novel score decouples shape deformations for better shape analysis.
problem High-dimensional deformations absorb lower-dimensional components, affecting statistical analysis.
method Introduces a coupling score using varifold representation of vector fields to quantify and decouple deformation modes.
result The coupling score effectively decouples distinct deformation modes during registration, improving shape analysis.
New technique improves imitation learning by preventing local minima and exploring states.
problem Behavioral cloning gets stuck in local minima and lacks effective exploration.
method Two-phase model with sampling mechanisms and self-attention modules.
result Significantly outperforms previous state-of-the-art in various environments.
This paper discusses fairness in machine learning and its legal implications.
problem Discrimination in machine learning algorithms that unfairly treat certain groups.
method Explains moral philosophy, legislation, and strategies to detect and prevent discrimination.
result Discusses the need for fairness in machine learning and legal measures to enforce it.
Enhances reinforcement learning safety through risk-averse exploration.
problem Safety concerns in reinforcement learning due to sub-optimal actions.
method Distributionally robust policy iteration scheme with lower bound guarantees.
result Efficient algorithm that prevents poor decisions and converges to optimal policy.
Improved Q-learning for multi-agent reinforcement learning by weighting joint action values.
problem QMIX restricts Q-values to monotonic mixtures, limiting complex value functions. method Introduced weighted projection to recover optimal policies, improving performance.
result CW QMIX and OW QMIX outperform baseline QMIX on multi-agent tasks.
Q-Distribution Guided Q-Learning corrects overestimation of uncertain OOD actions in offline RL.
problem Overestimation of Q-values for out-of-distribution actions in offline reinforcement learning.
method QDQ applies a pessimistic adjustment to Q-values in uncertain OOD regions based on a consistency model.
result QDQ improves performance on the D4RL benchmark and achieves significant improvements across many tasks.
Randomized value functions offer a promising approach towards the challenge of efficient exploration in complex environments with high dimensional state and action spaces. Unlike traditional point estimate methods, randomized value functions maintain a posterior distribution over action-space values. This prevents the …
New method interprets neural agent's actions to prevent unwanted outcomes.
problem Blackbox issue in RL where agents learn without foreseeing all outcomes.
method Action-conditional β-VAE for disentangled representation learning.
result Interpretable latent features enable modeling entire state space.
Customer temporal behavioral data was represented as images in order to perform churn prediction by leveraging deep learning architectures prominent in image classification. Supervised learning was performed on labeled data of over 6 million customers using deep convolutional neural networks, which achieved an AUC of 0…
In reinforcement learning, agents learn by performing actions and observing their outcomes. Sometimes, it is desirable for a human operator to \textit{interrupt} an agent in order to prevent dangerous situations from happening. Yet, as part of their learning process, agents may link these interruptions, that impact the…
Improved CEM for fast real-time planning in high-dimensional control tasks.
problem Sampling inefficiency of CEM in real-time planning.
method Novel additions to CEM including temporally-correlated actions and memory.
result 2.7-22x less samples and 1.2-10x performance increase.
SCQRNN prevents quantile crossing and improves computational efficiency.
problem Quantile crossing issue in regression models.
method Integrates ad hoc sorting in training to prevent quantile crossing and enhance computational efficiency.
result SCQRNN achieves faster convergence and non-intersecting quantiles.
The paper models commodity price dynamics influenced by producers and traders, preventing arbitrage and finding optimal derivative positions.
problem Preventing arbitrage opportunities in commodity option pricing influenced by producers and traders.
method Three continuous-time models of commodity price dynamics, semi-explicit solutions, closed-form expressions of derivative prices.
result Producers can compensate losses from increased volatility by strategically setting derivative prices.
Optimistic Actor-Critic improves exploration efficiency in reinforcement learning.
problem Poor sample efficiency in existing actor-critic methods.
method Introduces Optimistic Actor-Critic, approximating upper and lower bounds on state-action value function.
result Achieves state-of-the-art sample efficiency in challenging continuous control tasks.
Investors with asymmetric information play a game to optimize their portfolios.
problem Two investors with different information levels compete in portfolio selection.
method Modelled as a Stackelberg game with entropy-regularized mean-variance objectives.
result Equilibria exist where follower's strategy depends on leader's actions.
Study designs logging policies to minimize off-policy evaluation error.
problem Minimizing OPE error with logging policies for target policies.
method Characterizes reward-coverage tradeoff, proposes a unifying framework, derives optimal policies.
result Provides actionable guidance for firms choosing recommendation systems.
Solves label switching in mixture models using optimal transport.
problem Label switching in mixture model posterior inference prevents meaningful statistics assessment.
method Proposes an algorithm leveraging optimal transport to compute posterior statistics in a quotient space.
result Demonstrates advantages over alternative approaches on simulated and real data.
EBMs improve sample efficiency and generalization in RL.
problem Improving sample efficiency and generalization in reinforcement learning.
method Developed an online algorithm to train EBMs for model-based planning, leveraging their ability to infer intermediate states.
result EBMs lead to significantly better online learning and state space planning compared to feed-forward networks.
BCCNet combines biased crowd labels to train classifiers for disaster response.
problem Improper labels from citizen scientists limit machine learning applications.
method Bayesian classifier combination neural network (BCCNet) aggregates and trains classifiers from imperfect labels.
result BCCNet effectively processes large unstructured data for disaster prevention and response.
Bayesian model reduces health disparities in the U.S.
problem Health and longevity gaps in the U.S. due to socio-economic factors.
method Bayesian Decision Network integrating healthcare, socio-economic data.
result Quantifiable policy actions to reduce longevity gap.
Study on financial impacts of zombie outbreak on economy.
problem Financial and economic consequences of a zombie epidemic.
method Epidemiological modeling and financial computation.
result GDP losses of 23.44% and financial market drop of 29.30% in a major industrialized nation.
Band-limited SAC improves learning efficiency and stability in simulated environments.
problem Improving sample efficiency and stability in SAC algorithms.
method Artificially bandlimiting the target critic's spatial resolution using a convolutional filter.
result Bandlimited SAC outperforms classic twin-critic SAC in various Gym environments and is more stable.
This paper presents the first deep reinforcement learning (DRL) framework to estimate the optimal Dynamic Treatment Regimes from observational medical data. This framework is more flexible and adaptive for high dimensional action and state spaces than existing reinforcement learning methods to model real-life complexit…
Bayesian optimization gains efficiency by leveraging symmetries through a modified max kernel.
problem Improving Bayesian optimization efficiency for functions with group symmetries.
method Developed a PSD projection of the max kernel to exploit symmetries without violating kernel properties.
result The modified max kernel achieves lower regret compared to existing invariant and non-invariant kernels.
New method predicts customer churn using mixed-penalty logistic regression.
problem Predicting customer churn in CRM systems.
method Mixed-penalty logistic regression for big data analysis.
result Proposed method enhances logistic regression for better predictive analytics.
Many immunization strategies have been proposed to prevent infectious viruses from spreading through a network. In this study, we propose efficient immunization strategies to prevent a default contagion that might occur in a financial network. An essential difference from the previous studies on immunization strategy i…
We construct embedded Willmore tori with small area constraint in Riemannian three-manifolds under some curvature condition used to prevent Möbius degeneration. The construction relies on a Lyapunov-Schmidt reduction; to this aim we establish new geometric expansions of exponentiated small symmetric Clifford tori and a…
MimosaNet prevents model stealing by making neural networks sensitive to weight changes.
problem Neural networks are vulnerable to model stealing due to robustness to minor parameter changes.
method Develops a method to create a sensitive version of a trained neural network.
result The sensitive network produces the same responses but is highly sensitive to weight changes, preventing model stealing.
Paper solves pendulum swing-up problem using RL.
problem Solving the classic pendulum swing-up problem.
method Deep Deterministic Policy Gradient algorithm applied to continuous action domain.
result Optimal pendulum achieved with increasing average return and decreasing loss.
With a point of departure in the concept "uncomfortable knowledge," this article presents a case study of how the American Planning Association (APA) deals with such knowledge. APA was found to actively suppress publicity of malpractice concerns and bad planning in order to sustain a boosterish image of planning. In th…
New method combines regional HIV prevention trial data without sharing individual patient info.
problem Regional differences in HIV prevention efficacy, privacy concerns, and data sharing limitations.
method Federated learning approach that combines site-specific estimators via L1-regularization.
result Improved precision in estimating region-specific survival curves.
Modified model prevents volatility from approaching zero.
problem Volatility in the Gatheral model can approach zero, making it statistically indistinguishable.
method Proposed a modified model with Skorokhod reflection to prevent volatility from approaching zero.
result The modified model prevents volatility from approaching zero, preserving the model's flexibility.
This paper examines how skip connections prevent rank collapse in sequence models.
problem Rank collapse in sequence models, leading to reduced expressivity and training instabilities.
method Analytical and ablation studies of lambda-skip connections in SSMs.
result A sufficient condition to prevent rank collapse across various architectures.
The paper explores how sinks and diagonal patterns prevent attention oversmoothing.
problem Preventing attention oversmoothing in neural networks.
method Analyzing geometric conditions and conditions for dense vs. sparse attention, proving equivalence between sinks and hard attention switch, and comparing the costs of sinks vs. diagonal patterns.
result Sinks and diagonal patterns effectively prevent attention oversmoothing, and diagonal patterns provide a more flexible approach.
Game theory models incentivizes honesty in collaborative learning among competitors.
problem Incentivizing honest updates among competitors in collaborative learning schemes.
method Formulated a game to model interactions, studied two learning tasks, proposed mechanisms to incentivize honest communication.
result Rational clients are incentivized to manipulate their updates, preventing learning; proposed mechanisms ensure comparable learning quality to full cooperation.
USAC balances pessimism and optimism in actor-critic training for better exploration and performance.
problem Excessive pessimism limits exploration, while excessive optimism leads to high-risk behaviors.
method Utility Soft Actor-Critic (USAC) dynamically adapts exploration based on critic uncertainty.
result USAC consistently outperforms state-of-the-art algorithms in continuous control tasks.
Prevents sensitive data generation in diffusion models using labeled and unlabeled data.
problem Generating sensitive data in diffusion models using unlabeled data.
method Positive-Unlabeled Diffusion Models, approximating ELBO with labeled and unlabeled data.
result Prevents the generation of sensitive data without compromising image quality.
StratLearner learns strategies to prevent misinformation in social networks.
problem Learning strategies to protect against misinformation in social networks without knowing the diffusion model.
method Structured prediction framework using random features and large margin method.
result Our method produces near-optimal protectors without diffusion model information and outperforms other methods.
Geometric obstructions prevent gravity in high dimensions.
problem Obstacles to realizing gravity in various geometries.
method Analyzing the tetradic Einstein-Hilbert-Palatini action in different geometric settings.
result Gravity is only meaningful in Lorentzian geometry for dimensions n≥4. Mobile apps and machine learning improve malaria prevention and treatment.
problem High malaria cases and deaths in low-income countries.
method Adaptive interventions using mobile health apps and machine learning.
result Increased malaria testing, adherence, and provider skills.