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.
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arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
169,341 papers · 148 categories
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48 results for “minimax utility”
New method generates private synthetic data with optimal utility for smooth queries.
problem Achieving strong utility guarantees for meaningful downstream analysis of sensitive datasets.
method Proposes a polynomial-time algorithm for generating -differentially private synthetic data with minimax optimal error rates for smooth queries.
result Achieves a minimax error rate of for -smooth queries, up to a factor.
Investigates optimal strategies under financial uncertainty, proving convergence as uncertainty increases.
problem Utility maximization in financial markets with model uncertainty.
method Explicit representation of optimal strategy, minimax theorem, convergence analysis.
result Optimal strategy converges to a generalized uniform diversification strategy as uncertainty increases.
New algorithm solves federated minimax optimization problems.
problem Federated minimax optimization challenges.
method Federated Stochastic Smoothed Gradient Descent Ascent (FESS-GDA).
result FESS-GDA uniformly solves federated minimax problems.
Efficient algorithm for global optimization of multivariate Lipschitz functions.
problem Global optimization of multivariate Lipschitz continuous functions.
method Proposes an efficient minimax optimal algorithm using a predetermined query creation rule.
result Achieves an average regret bound of , minimax optimal.
Private two-sample tests under LDP achieve minimax rates for multinomial and continuous data.
problem Achieving statistical utility while maintaining privacy in two-sample testing.
method Private permutation tests for multinomial data and adaptive tests for continuous data.
result Minimax optimal tests for private two-sample testing under LDP.
Paper investigates optimal transport map estimation in infinite-dimensional spaces.
problem Estimating optimal transport maps in infinite-dimensional spaces is challenging.
method Characterizes -smoothness for optimal transport maps and develops a polynomial-rate estimator.
result Shows polynomial-order minimax risk for optimal transport map estimation.
Paper proposes efficient methods for clustering and signal recovery in high-dimensional data with block structures.
problem High-dimensional clustering and signal recovery under block signal structures.
method CFA-PCA and MA-PCA methods for sparse and dense block signals.
result Proposed methods achieve computational minimax optimality for clustering and signal recovery.
Privacy utility trade offs for parameter estimation in degree heterogeneous higher order networksstat.ML
Study privacy vs. utility in estimating network parameters with aggregated data.
problem Privacy-preserving estimation of network parameters from aggregated node degrees.
method β model, local and central differential privacy, minimax lower bounds, simple estimators.
result Achieved minimax-optimal risk bounds for parameter estimation under privacy constraints.
Develops locally private methods for nonparametric contextual bandits.
problem Privacy concerns in sequential decision-making on sensitive data.
method Uniform-confidence-bound-type estimator and jump-start scheme.
result Minimax optimality of proposed methods supported by lower bounds.
Investors only hold risk-free assets if certain conditions are met.
problem Investment behavior under multiprior minimax utility maximization.
method Continuous-time financial market analysis with semimartingales.
result Portfolio consists only of risk-free asset if specific conditions on priors are met.
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.
A new algorithm calculates optimal strategies for two-player zero-sum games.
problem Computing the optimal strategies for two-player zero-sum games.
method Extending successive relaxation to two-player zero-sum games and developing a generalized minimax Q-learning algorithm.
result The proposed algorithm converges and effectively computes optimal strategies.
In this paper, we give a new sharp generalization bound of lp-MKL which is a generalized framework of multiple kernel learning (MKL) and imposes lp-mixed-norm regularization instead of l1-mixed-norm regularization. We utilize localization techniques to obtain the sharp learning rate. The bound is characterized by the d…
Study exact minimax rates for density estimation over convex classes, extending previous work.
problem Deriving minimax rates for density estimation over convex density classes.
method Building on Le Cam's work, determine exact minimax rates using local metric entropy.
result Exact minimax rates derived for any convex density class, including nonparametric and parametric cases.
The paper addresses decision making with partially calibrated forecasts, offering a robust approach.
problem Developing a decision-making strategy for forecasts that are only partially calibrated.
method A minimax approach to mapping predictions to actions, considering worst-case distributions.
result The minimax optimal decision rule is to trust predictions and act accordingly, even for partially calibrated forecasts.
Paper develops efficient mechanisms for estimating variance and covariance under differential privacy in the add-remove model.
problem Estimating variance and covariance under differential privacy in the add-remove model.
method Developed mechanisms based on the Bézier mechanism, a novel moment-release framework.
result Proved minimax optimality of the Bézier-based estimator in the high-privacy regime and demonstrated its better utility in instance-wise analysis.
Paper finds a lower bound for estimating low-rank matrices in logistic regression.
problem Estimating low-rank coefficient matrices in logistic regression.
method Derives a minimax lower bound on the risk.
result The bound depends on matrix dimensions, rank, and sample size.
Proves minimax sample complexity for turn-based stochastic games.
problem Proving theoretical guarantees for reinforcement learning in turn-based stochastic games.
method Developing absorbing TBSG and reward perturbation techniques to handle statistical dependence.
result Empirical Nash equilibrium strategy approximates true Nash equilibrium in turn-based stochastic games.
Optimal algorithms for online convex optimization with missing sub-gradient observations.
problem Online convex optimization with noisy or missing sub-gradient observations.
method Adaptive algorithms using sub-gradient descent with minimax optimal regret guarantees.
result Achieves tight minimax optimal regret bounds with empirical property estimation.
Develops a minimax optimal estimator for system stability under distribution shift.
problem Ensuring system reliability under changes in the underlying environment.
method Minimax optimal estimation of stability defined in terms of acceptable performance degradation.
result Characterizes the minimax convergence rate and demonstrates practical utility.
Paper develops a new method for differential privacy sampling using Wasserstein distance.
problem Sampling from distributions under differential privacy constraints with geometric structure consideration.
method Develops a novel framework with Wasserstein Projection Mechanism (WPM) for minimax optimal mechanisms.
result Proposes efficient algorithms for approximate computation of the Wasserstein Projection Mechanism.
New method for sequential probability assignment reduces regret using contextual Shtarkov sums.
problem Minimizing regret in sequential probability assignment with arbitrary hypothesis classes.
method Introducing contextual Shtarkov sum and contextual Normalized Maximum Likelihood (cNML) algorithm.
result The contextual Shtarkov sum characterizes minimax regret and provides a minimax optimal strategy.
Paper analyzes how unlabeled data improves SSL and adversarial robustness.
problem Understanding how unlabeled data impacts SSL and adversarial robustness.
method Minimax analysis and adversarial training.
result Reconstruction-based SSL algorithm is rate-optimal under various models and enhances adversarial robustness.
Fuzzy hashes learn from data to improve file similarity detection.
problem Measuring similarity between files, especially malware.
method Learned fuzzy hashes using a minimax training framework.
result Learned fuzzy hashes outperform traditional methods for file similarity.
This paper tackles robust control of noisy systems with uncertain distributions.
problem Optimal control of sampled-data stochastic systems with multiplicative noise and distributional ambiguity.
method Develops a convex relaxation to handle the ``concave-max'' geometry and derives a probabilistic performance guarantee.
result Derives an explicit, non-asymptotic bound on the duality gap and proves robust viability conditions.
New method for efficient matrix completion with nonignorable missing data.
problem Nonignorable missing data in matrix completion.
method Nuclear norm regularized U-statistic loss function and accelerated proximal gradient algorithm.
result Near minimax optimal statistical convergence rate for nonignorable missing data.
Hypothesis Testing For Densities and High-Dimensional Multinomials: Sharp Local Minimax Ratesmath.ST
Study on hypothesis testing for densities and multinomials, showing local minimax rates and critical radii.
problem Testing goodness-of-fit for distributions with varying number of categories or unbounded support.
method Developed novel tests for both discrete and continuous cases, considering local minimax rates and critical radii.
result Characterized the dependence of critical radii on the null hypothesis and provided adaptive tests.
A robust method for off-policy evaluation in contextual bandits.
problem Evaluating policies when direct methods are unavailable.
method Robust regression approach to off-policy evaluation.
result Superior empirical performance across benchmarks.
Study optimizes financial strategies in markets with uncertain drift.
problem Optimizing portfolios in markets with unpredictable drift.
method Combines worst-case optimization with filtering techniques to define uncertainty sets.
result Proves minimax theorem and derives optimal strategies for continuous updates.
Paper proposes a new algorithm to minimize AUC disparities in machine learning models.
problem Minimizing unfairness in AUC scores for machine learning models.
method Proposes a minimax learning and bias mitigation framework for AUC optimization.
result Proves the convergence of the proposed algorithm to minimize group-level AUC.
LDP is equivalent to contraction of E_γ-divergence, impacting privacy and utility.
problem Analyzing trade-offs between privacy and utility in estimation problems.
method Equivalence of LDP constraints to contraction coefficients of E_γ-divergence, using f-divergences and estimation-theoretic tools.
result LDP guarantees can be expressed in terms of contraction coefficients of arbitrary f-divergences.
New algorithm for robust density estimation in corrupted data.
problem Density estimation in the presence of adversarial corruption.
method Proposes an algorithm for constructing a density estimator within a star-shaped density class, derived minimax bounds for estimation.
result Obtained minimax upper and lower bounds for density estimation under adversarial corruption.
Improved FTPL algorithm reduces regret in predictable minimax games.
problem Online learning and minimax games with predictable loss sequences.
method Optimistic modification of FTPL with dual regularization view.
result Tighter regret bounds for predictable sequences, accuracy.
New learning rates derived for Tikhonov-regularized problems without kernel assumptions.
problem Learning rates for Tikhonov-regularized learning problems.
method Minimax adaptive rates derived using Fourier isocapacitary condition and interpolation theory.
result Derivation of minimax adaptive rates without requiring kernel assumptions.
The paper explores the information-theoretic nature of excess risk in machine learning.
problem Understanding the excess risk in machine learning models.
method Formulates the minimax excess risk as a zero-sum game and modifies it to allow swapping of the order of play.
result Proves that under certain conditions, the duality gap is zero, allowing for the application of Bayesian results to provide bounds on minimax excess risk.
Study optimizes option pricing with robust strategies, ensuring consistency with vanilla option prices.
problem Optimizing exotic option pricing with robust strategies.
method Introduces semistatic strategies and robust convex integral functionals on bounded continuous functions.
result Consistent indifference prices with observed vanilla option prices.
Paper tackles transfer learning for contextual multi-armed bandits under covariate shift.
problem Nonparametric contextual multi-armed bandits with covariate shift.
method Established minimax rate of convergence, proposed transfer learning algorithm.
result Achieved near-optimal statistical guarantees for learning in target domain.
The paper proposes differentially private sliced inverse regression algorithms for high-dimensional data.
problem Privacy concerns in high-dimensional data analysis.
method Differentially private sliced inverse regression algorithms designed for privacy preservation.
result Achieves minimax lower bounds up to logarithmic factors.
The paper addresses privacy in rank aggregation using randomized responses.
problem Preserving privacy while aggregating pairwise rankings.
method Adaptive debiasing method for randomized response rankings.
result Established minimax rates for estimation errors and optimal privacy guarantees.
A new classifier uses Fermat distance for semi-supervised learning in high dimensions.
problem Semi-supervised classification with limited labeled data in high-dimensional settings.
method Proposes weighted k-NN and MDS classifiers using Fermat distance.
result The weighted k-NN classifier is minimax optimal and outperforms other methods.
Paper finds exact global optima for adversarial representation learning.
problem Obtaining data representations invariant to sensitive attributes.
method Spectral learning for linear functions, kernel representation for non-linear functions.
result Exact closed-form expression for global optima with performance guarantees.
SAMPLR optimizes for ground truth in aleatoric parameters to avoid curriculum-induced covariate shift.
problem Curriculum learning shifts training distribution, leading to suboptimal policies in aleatoric settings.
method SAMPLR optimizes ground-truth utility function, avoiding curriculum-induced covariate shift.
result SAMPLR preserves optimality under ground-truth distribution, promoting robustness across various environments.
Understanding Expert Structures on Minimax Parameter Estimation in Contaminated Mixture of Expertscs.LG
Study analyzes convergence of parameter estimation in contaminated mixture of experts.
problem Challenges in learning from prompts in large-scale models.
method Convergence analysis, distinguishability condition, partial differential equations.
result Comprehensive convergence rates and minimax lower bounds for parameter estimation.
The paper tackles individualized decision-making under unmeasured confounding, providing a novel minimax solution and a paradox.
problem Unmeasured confounding in causal inference leads to biased estimates and affects individualized decision-making.
method The authors establish a formal link between individualized decision-making under partial identification and classical decision theory, providing a minimax solution and a paradox.
result A novel minimax solution for individualized decision-making/policy assignment is provided, and an interesting paradox is drawn.
New Riemannian radial distributions help estimate parameters on symmetric spaces.
problem Challenges in manifold data analysis due to lack of parametric distributions.
method Introduced Riemannian radial distributions on symmetric spaces, utilized symmetry, and developed M-estimators.
result MLE achieves root-n convergence rate up to logarithmic terms, demonstrating optimality.
Enhances exploration in hierarchical networks using mutual information.
problem Limited exploration in hierarchical Deep Q-Networks.
method Adversarial Soft Actor-Critic with mutual information optimization.
result Improves hierarchical network exploration through mutual information maximization.
Novel methods for accelerating optimization in complex bilevel and minimax problems.
problem Optimization challenges in bilevel and minimax problems, especially when strong convexity assumptions are not met.
method Accelerated fully first-order methods for Bilevel Optimization (BLO) and Minimax Optimization (NCSC).
result State-of-the-art complexity for finding approximate second-order stationary points in BLO and NCSC.