Study learns linear utility functions from comparisons, showing learnability gaps between passive and active learning.
problem Learn linear utility functions from pairwise comparison queries.
method Analyzes passive and active learning settings, considering noise-free and noisy query responses.
result Efficient learnability of linear utilities in passive learning, but not for utility parameters without strong assumptions.
New method for fair resource allocation in AI-aware networks with unknown utility functions.
problem Fair resource allocation in AI-aware communication networks with unknown utility functions.
method Distributed, data-driven bilevel optimization approach to learn surrogate utility functions.
result The proposed algorithm learns from data to autotune surrogate utility functions for unknown utility functions.
The paper tackles optimal policy learning with asymmetric counterfactual utilities in healthcare decisions.
problem Learning optimal policies from observed data with asymmetric counterfactual utilities.
method The approach involves identifying and minimizing the maximum expected utility loss using statistical decision theory and solving intermediate classification problems.
result One can learn minimax loss decision rules from observed data.
New approach for learning with unknown utilities without explicit specification.
problem Learning with unknown and context-dependent utility functions.
method Agnostic learning with unknown utilities, using k-comparison oracle.
result Learning a decision function with small excess risk using sampled data and k-comparison oracle.
Develops deep learning methods for solving S-shaped utility maximisation problems.
problem Optimizing portfolios with S-shaped utility and random benchmarks.
method Uses deep learning and duality methods to solve the Hamilton-Jacobi-Bellman equation and adjoint equation.
result Demonstrates the accuracy of deep learning methods for non-concave utility maximisation problems.
Deep learning solves dynamic programming with recursive utility.
problem Challenges in solving high-dimensional discrete-time dynamic programming problems with recursive utility.
method Certainty Equivalent Learning (CEL) algorithm that learns certainty-equivalent value directly with neural networks.
result Accurate value and policy approximations in high-dimensional problems, comparable to VFI in some cases.
The paper proposes a method to learn the structure of continuous-action games with non-parametric utilities using a limited number of samples.
problem Learning the exact structure of continuous-action games with non-parametric utility functions.
method An ℓ1 regularized method that encourages sparsity of the Fourier transform coefficients of the utility functions, accessed via a few Nash equilibria and their noisy utilities. result The method recovers the exact structure of the utility functions and the game structure with provable theoretical guarantees.
Study uses reinforcement learning to optimize portfolios under recursive utility.
problem Improving portfolio allocation using risk-sensitive objectives.
method Approximated certainty equivalent via Monte Carlo, trained actor-critic algorithms (PPO, A2C).
result Recursive-utility agent outperforms discounted baseline in Sharpe ratio, max drawdown, and cumulative return.
CEFOL uses deep learning for dynamic programming with recursive utility.
problem Challenges in solving dynamic programming problems with recursive utility.
method Introduces a separate neural network for certainty equivalent, uses first-order optimality conditions to learn value and policy functions.
result CEFOL achieves high accuracy in learning value and policy functions, matching VFI benchmarks.
Paper establishes utility theory for synthetic data generation.
problem Lack of theoretical understanding in synthetic data utility.
method Statistical learning framework with two utility metrics: generalization and model ranking.
result Theoretical bounds for synthetic data utility metrics ensure comparable generalization and consistent model comparison.
We consider a framework involving behavioral economics and machine learning. Rationally inattentive Bayesian agents make decisions based on their posterior distribution, utility function and information acquisition cost Renyi divergence which generalizes Shannon mutual information). By observing these decisions, how ca…
Study on utility maximization with Tsallis entropy in reinforcement learning.
problem Exploring utility maximization with Tsallis entropy in reinforcement learning.
method Introducing Tsallis entropy regularizer to induce exploration, investigating specific examples, characterizing well-posedness, designing reinforcement learning algorithm.
result Characterized well-posedness and provided semi-closed-form solutions for specific examples, found distinct optimal strategies.
Neural networks approximate random utility models for choice prediction.
problem Approximating random utility models with neural networks.
method RUMnets, a neural network-based model inspired by RUM framework.
result RUMnets can approximate any RUM model arbitrarily closely and vice versa.
Prediction markets show considerable promise for developing flexible mechanisms for machine learning. Here, machine learning markets for multivariate systems are defined, and a utility-based framework is established for their analysis. This differs from the usual approach of defining static betting functions. It is sho…
Improved privacy and utility in machine learning with adaptive differential privacy.
problem Enhancing privacy in machine learning models while maintaining utility.
method Adaptive differentially private (ADP) learning method that optimally adapts noise to stepsize.
result ADP method significantly improves utility compared to standard differentially private methods.
RUMBoost combines RUMs and deep learning for better choice modelling.
problem Creating interpretable and robust discrete choice models.
method Gradient Boosted Regression Trees for utility functions, with constraints for interpretability and monotonicity.
result RUMBoost outperforms ML and RUM benchmarks in predictive performance and interpretability.
Paper uses ML to predict utility in APS dialogue outcomes.
problem Predict utility for different user subpopulations in APS.
method Develops EAI and EDS ML methods to predict utility functions.
result EDS more effective at predicting utility functions.
Paper develops a federated learning method to protect privacy without sacrificing model utility.
problem Privacy leakage in federated learning due to information exchange between edge devices and server.
method Combines local gradient perturbation, secure aggregation, and zCDP for privacy protection.
result Demonstrates superior trade-off between privacy and model utility through extensive experiments.
Two deep learning algorithms solve utility maximisation problems in finance.
problem Solving utility maximisation problems in finance with deep learning.
method Two algorithms: one for Markovian problems via HJB equation and 2BSDE, the other for non-Markovian problems via adjoint BSDE.
result Highly accurate results with low computational cost, solving problems with power, log, and non-HARA utilities in various models.
The paper improves dropout's utility by reducing interactions in deep neural networks.
problem Over-fitting problem in deep learning.
method Game-theoretic interactions analysis and interaction loss.
result Interaction loss improves dropout's utility and boosts DNN performance.
GBC methods compute expected utility without needing the model's density.
problem Computing expected utility in complex models.
method Density-free generative method using quantile neural estimator.
result Efficient estimation of expected utility from simulated data.
Study optimizes fairness in predictive models by balancing utility and separation.
problem Balancing fairness and utility in predictive models.
method Information-theoretic approach using conditional mutual information (CMI).
result Reduces separation violations while maintaining or improving utility.
Bayesian optimization with preference learning using monotonic neural networks.
problem Optimizing complex systems with multiple conflicting objectives.
method Proposes a neural network ensemble for utility surrogate modeling, leveraging monotonicity.
result Demonstrates superior performance compared to existing methods.
Differential privacy is a mathematical framework for privacy-preserving data analysis. Changing the hyperparameters of a differentially private algorithm allows one to trade off privacy and utility in a principled way. Quantifying this trade-off in advance is essential to decision-makers tasked with deciding how much p…
Proposes a new method for optimizing designs based on uncertain preferences.
problem Optimizing designs with uncertain and time-consuming preferences.
method Bayesian optimization with preference learning for multi-attribute optimization.
result Produces a menu of designs and attributes for the DM to choose from.
Deep Q-Learning optimizes market making by balancing price risk and spread profits.
problem Optimizing liquidity provision in financial markets.
method Reinforcement Learning applied to a market making problem with a reward function.
result Deep Q-Learning algorithms can recover the optimal market making strategy.
Deep learning solves non-Markovian FBSDEs for utility maximization.
problem Solving utility maximization problems under rough volatility.
method Deep learning-based numerical methods for non-Markovian fully coupled FBSDEs.
result Error estimates and convergence provided for the deep learning approach.
Optimizes football play calls using reinforcement learning.
problem Maximizing game outcomes with limited data.
method Reinforcement learning to optimize decision-making.
result Optimized play calls lead to better game outcomes.
Algorithm learns fair division from noisy feedback in uncertain markets.
problem Learning fair division in uncertain markets with noisy feedback.
method Wrapper algorithms using dual averaging to learn item and agent values from bandit feedback.
result Asymptotically achieves optimal Nash social welfare in linear Fisher markets.
Proposes element-level differential privacy for better privacy and utility in statistical learning.
problem Challenges of strong differential privacy in statistical learning applications.
method Introduces element-level differential privacy, extending classical DP to protect specific user elements.
result Provides better utility and more robust results compared to classical DP by allowing finer privacy protections.
Algorithm samples fair rankings to ensure individual fairness while maintaining group fairness.
problem Fair ranking tasks with group fairness constraints and uncertainty in item utilities.
method Efficient algorithm that samples rankings from an individually-fair distribution ensuring group fairness.
result Expected utility of output ranking is at least α times optimal fair solution, where α depends on utilities and constraints.
New ranking system balances fairness and user utility.
problem Achieving group fairness in ranking systems.
method Formulated a minimax game between a ranking player and an adversary.
result Better utility for highly fair rankings.
New risk measures for financial and ESG risks using utility functions.
problem Assessing financial and ESG risks using traditional risk measures.
method Developed new risk measures based on utility functions.
result Properties of utility functions translate into properties of risk measures.
Algorithm identifies best item from subsets with random utility model feedback.
problem PAC learning the best item from subsets with random utility model feedback.
method Pairwise relative counts and hierarchical elimination for learning algorithm.
result Near-optimal PAC sample complexity guarantee for identifying ε-optimal item.
Paper improves privacy-utility trade-off in federated learning.
problem Repeated parameter sharing in federated learning leaks private data.
method Proposes a new representation federated learning objective with differential privacy guarantees.
result Algorithm \DPFEDREP\ converges to a global optimal solution with a linear rate and privacy budget-dependent radius.
Optimal defenses protect FL models from gradient reconstruction attacks.
problem Gradient reconstruction attacks compromise FL models by recovering original data from shared gradients.
method Derive a theoretical lower bound of reconstruction error, customize noise and pruning defenses, and achieve optimal trade-off between leakage and utility.
result Our methods outperform Gradient Noise and Pruning in protecting training data and maintaining model utility.
New algorithm solves utility maximization with deep learning for constrained problems.
problem Maximizing utility under convex constraints with random coefficients.
method Developed a new algorithm using stochastic maximum principle and deep learning.
result The new algorithm outperforms existing methods in accuracy and applicability.
In the Bayesian approach to sequential decision making, exact calculation of the (subjective) utility is intractable. This extends to most special cases of interest, such as reinforcement learning problems. While utility bounds are known to exist for this problem, so far none of them were particularly tight. In this pa…
FairDTD improves fairness in GNNs by distilling dual teacher knowledge, balancing utility and bias.
problem Bias in GNN predictions due to sensitive attributes.
method Dual-Teacher Distillation with a causal graph model, feature and structure teachers, and graph-level distillation.
result Achieves optimal fairness while preserving high model utility.
SMOTE-DP enhances synthetic data privacy without sacrificing utility.
problem Balancing privacy and utility in synthetic data generation.
method Integrating SMOTE with differential privacy mechanisms.
result SMOTE-DP produces synthetic data that is both private and useful.
HAL learns hierarchical affordances to prune impossible subtasks, improving reinforcement learning efficiency.
problem Reinforcement learning struggles with complex hierarchical dependency structures.
method HAL learns a model of hierarchical affordances to prune impossible subtasks.
result HAL agents are better at learning complex tasks, navigating stochastic environments, and acquiring diverse skills.
The remarkable success of machine learning, especially deep learning, has produced a variety of cloud-based services for mobile users. Such services require an end user to send data to the service provider, which presents a serious challenge to end-user privacy. To address this concern, prior works either add noise to …
Off-policy reinforcement learning with eligibility traces is challenging because of the discrepancy between target policy and behavior policy. One common approach is to measure the difference between two policies in a probabilistic way, such as importance sampling and tree-backup. However, existing off-policy learning …
A new learning-to-rank approach ensures fairness for item providers in dynamic ranking systems.
problem Myopically optimizing user utility can be unfair to item providers in two-sided markets.
method A controller that integrates unbiased estimators for fairness and utility, dynamically adapting as more data becomes available.
result Empirically, the algorithm is highly practical and robust, ensuring amortized group fairness.
Rugby-Bot predicts multiple metrics from a single source using fine-grain data.
problem Complexity of sporting events requires multiple metrics for accurate analysis.
method Multi-task learning with fine-grain spatial data and wide-and-deep learning.
result Predictions are consistent and can be in distribution form.
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.
We propose a new efficient online algorithm to learn the parameters governing the purchasing behavior of a utility maximizing buyer, who responds to prices, in a repeated interaction setting. The key feature of our algorithm is that it can learn even non-linear buyer utility while working with arbitrary price constrain…
Tutorials on preference learning with Gaussian Processes.
problem Understanding individual preferences and choices for efficient and personalized applications.
method Presentation of a comprehensive framework for preference learning with Gaussian Processes, incorporating rationality principles.
result Construction of preference learning models that encompass various utility models and scenarios.