The paper provides a method to minimize regret in estimate-then-optimize decision-making.
problem Errors in estimation lead to sub-optimal decisions in data-driven decision-making.
method A novel bound on regret for smooth and unconstrained optimization problems, followed by experimental design to minimize this regret.
result A general procedure for experimental design to minimize regret resulting from estimate-then-optimize.
Paper investigates optimal transport map estimation in infinite-dimensional spaces.
problem Estimating optimal transport maps in infinite-dimensional spaces is challenging.
method Characterizes γ γ γ -smoothness for optimal transport maps and develops a polynomial-rate estimator. result Shows polynomial-order minimax risk for optimal transport map estimation.
New methods estimate transport-growth pairs in unbalanced optimal transport.
problem Statistical guarantees for Monge-type estimation in unbalanced optimal transport remain limited.
method Developed two estimators for transport-growth pairs under different setups.
result Achieved minimax optimal rate for estimation of transport-growth pairs.
The paper optimizes estimating transport maps between distributions.
problem Estimating optimal transport maps between distributions.
method Plugin approach using optimal couplings and extensions.
result Minimax optimality of the proposed estimators.
The paper proves strong holomorphic Morse inequalities on complex manifolds with optimal estimates.
problem Holomorphic Morse inequalities on non-compact complex manifolds with optimal fundamental estimates.
method Established strong holomorphic Morse inequalities under optimal fundamental estimates.
result Strong holomorphic Morse inequalities hold true on non-compact complex manifolds with optimal fundamental estimates.
Pessimistic estimator improves multi-objective policy optimization.
problem Optimizing multi-objective policies from existing data.
method Pessimistic estimator based on inverse propensity scores (IPS).
result Pessimistic estimator outperforms naive IPS estimator in theory and experiments.
Private KL distribution estimation improved with instance-optimality.
problem Minimizing KL divergence between true and estimated distributions.
method Construct minimax optimal private estimators, then focus on instance-optimality.
result Achieved instance-optimality up to constant factors for KL estimation.
Integrates estimation and optimization for uncertain parameters.
problem Optimizing with uncertain parameters whose distributions can be estimated.
method Integrated Conditional Estimation-Optimization (ICEO) framework.
result Asymptotically consistent and provides finite performance guarantees.
New methods reduce bias in estimating optimality gaps for risk-averse stochastic programs.
problem Optimality gap estimation bias in risk-averse stochastic programs.
method Two independent samples, each estimating a different component of the optimality gap.
result Our method reduces bias in estimating optimality gaps for risk-averse problems.
Study optimal transport for stationary processes, estimating joinings and costs.
problem Optimal transport for stationary stochastic processes.
method Introduced estimators for optimal joinings and costs, established consistency and error rates.
result Consistent estimators of optimal joinings and costs under mild and stronger mixing assumptions.
Kernel estimator optimally recovers function from noisy exponential Radon transform.
problem Inverting noisy exponential Radon transform of a function.
method Proposed a kernel estimator to estimate the true function.
result The estimator converges to the true function at minimax optimal rate.
Efficiently estimates optimal transport maps with rigorous guarantees.
problem Estimating optimal transport maps between distributions efficiently.
method Entropic version of Brenier's theorem, Sinkhorn's algorithm.
result Estimator is parallelizable and efficient for massive data sets.
We consider the fundamental learning problem of estimating properties of distributions over large domains. Using a novel piecewise-polynomial approximation technique, we derive the first unified methodology for constructing sample- and time-efficient estimators for all sufficiently smooth, symmetric and non-symmetric, …
New Riemannian optimization improves variance estimation in mixed models.
problem Challenges in estimating variance parameters in linear mixed models due to constraints.
method Formulated as an optimization problem on a Riemannian manifold, using Riemannian gradient and Hessian.
result Yields higher quality variance parameter estimates compared to existing methods.
New methods solve sparse estimation robustly, even with outliers.
problem Sparse estimation in high-dimensional data with outliers.
method Non-convex optimization formulations for robust sparse mean estimation and PCA.
result Any approximate stationary point yields near-optimal solutions.
Paper optimizes experimental design for estimating treatment effect.
problem Estimating treatment effect with heterogeneous subjects and treatments.
method Adaptive experimental design incorporating bandit learning.
result Demonstrates optimality of proposed adaptive experiment framework.
Efficiently estimates distributed mean with side information, near-optimal and universal.
problem Distributed mean estimation with side information in communication constrained settings.
method Wyner-Ziv estimators for communication and computation efficiency.
result Near-optimal and universal recovery guarantees for distributed optimization and compression.
Sharp ABP estimate on metric spaces via optimal transport.
problem Sharp ABP estimate on metric measure spaces.
method Optimal transport theory.
result Established a sharp ABP estimate on metric measure spaces.
Optimal kernel improves estimation accuracy in modal statistical methods.
problem Estimation accuracy of kernel-based modal statistical methods depends on the kernel used.
method The study theoretically shows an optimal kernel that minimizes asymptotic error criterion.
result An optimal kernel minimizes the error criterion when using an optimal bandwidth.
New findings show optimization is crucial for OPL in large action spaces.
problem Challenges in optimizing policies for large action spaces in offline contextual bandits.
method Weighed log-likelihood objectives and estimator-aware policy parametrization.
result Simple weighted log-likelihood objectives enjoy better optimization properties and recover competitive policies.
Two simulation-based methods improve optimal sampling design in systems biology.
problem Optimal selection of sampling points for accurate parameter estimation in dynamical systems.
method E-optimal-ranking (EOR) and LSTM neural network-based methods.
result Simulation studies show the proposed methods outperform random selection and classical E-optimal design.
Meta learning of optimal classifier error rates allows an experimenter to empirically estimate the intrinsic ability of any estimator to discriminate between two populations, circumventing the difficult problem of estimating the optimal Bayes classifier. To this end we propose a weighted nearest neighbor (WNN) graph es…
Optimizes calibration error estimators for better classifier trustworthiness.
problem Lack of guidance on selecting and tuning calibration error estimators.
method Reformulates calibration estimation as a regression problem with i.i.d. input pairs.
result Demonstrates the effectiveness of optimized calibration estimators on image classification tasks.
New optimizer Eve uses examplewise gradients for better second-moment estimates.
problem Improving optimization methods for machine learning.
method Adaptive optimization with examplewise gradients.
result Eve optimizer slightly outperforms Adam on small scale benchmarks.
Paper develops efficient methods for estimating Hessian inverses in stochastic optimization.
problem Estimating the inverse Hessian for convex function minimization.
method Robbins-Monro procedure for recursive estimation of the inverse Hessian.
result Develops universal stochastic Newton methods with improved efficiency.
Optimally estimates a functional using nuisance function tuning and sample splitting.
problem Estimating optimal rates for a doubly robust functional.
method Combines nuisance function tuning and sample splitting strategies.
result Shows optimal rates of convergence for various estimators.
A central question for active learning (AL) is: "what is the optimal selection?" Defining optimality by classifier loss produces a new characterisation of optimal AL behaviour, by treating expected loss reduction as a statistical target for estimation. This target forms the basis of model retraining improvement (MRI), …
New protocols show 1-bit mean estimation can be order-optimal without interaction.
problem Can 1-bit mean estimation be optimal without interaction?
method Adaptive and non-adaptive threshold and interval queries, with one adaptive transition.
result Arbitrary non-adaptive quantizers can match the adaptive rate, suggesting interaction is not necessary.
Study online monotone density estimation with expert aggregation and log-optimal calibration.
problem Online monotone density estimation and log-optimal calibration.
method Proposed two online estimators: Grenander estimator and expert aggregation estimator.
result Online estimators achieve O ( n 1 / 3 ) O(n^{1/3}) O ( n 1/3 ) cumulative log-likelihood gap and n log n \sqrt{n\log{n}} n log n pathwise regret bound. Optimizes shortfall risk using gradient-based methods.
problem Optimizing utility-based shortfall risk measures.
method Gradient-based stochastic optimization, non-asymptotic bounds derivation.
result Non-asymptotic convergence rate for optimizing UBSR.
Optimizes heat equation estimates on noncompact manifolds.
problem Improving gradient estimates for heat equations on noncompact manifolds.
method Localized and global noncompact versions of Hamilton's gradient estimate for positive solutions to the heat equation.
result Essentially optimal estimates significantly improve previous results.
Kelly investing improved with options to reduce estimation risk.
problem Estimation risk in Kelly investing leads to suboptimal portfolios.
method Introduced European options into the Kelly framework in a binomial model.
result Constructed growth optimal portfolios robust to estimation risk.
The paper analyzes rates of convergence for optimal transport map estimators using barycentric projections.
problem Estimating optimal transport maps from data sampled according to two distributions.
method Comprehensive analysis of rates of convergence for plug-in estimators defined via barycentric projections.
result New stability estimate for barycentric projections under minimal smoothness assumptions.
Optimal tuning for estimating ECC in proportional asymptotics.
problem Estimating Expected Conditional Covariance (ECC) under proportional asymptotics.
method Debiased ridge regression estimators for nuisance functions, sample splitting strategies, and asymptotic variance analysis.
result Prediction-optimal tuning parameters may not minimize asymptotic variance of ECC estimator.
Paper proposes robust estimators for GANs under Wasserstein contamination.
problem Robust estimation of distributions under contamination.
method Wasserstein GAN-based estimators for location, covariance, and regression.
result Proposed estimators are minimax optimal in many scenarios.
Self-distillation optimally improves model performance in spiked covariance models.
problem Improving model performance in spiked covariance models.
method Developed spectral shrinkage estimators and analyzed self-distillation.
result Self-distillation achieves optimal performance among spectral shrinkage estimators for spiked covariance matrices.
This paper shows using sub-sample estimates can improve optimization results in large-scale problems.
problem Large-scale optimization problems with uncertain parameters often lead to suboptimal solutions due to mis-specifications or extreme sample characteristics.
method The paper introduces the use of sub-sample estimates to reduce errors in stochastic optimization models, providing theoretical analysis and numerical examples.
result Sub-sample optimization can achieve improved results over full-sample solution estimates in large-scale problems.
New method for estimating and optimizing MDPs without stationarity.
problem Challenges in offline contextual MDP estimation without stationarity.
method Introduces a new adaptive estimation and cost optimization approach for contextual MDPs.
result First robust, theoretically backed method for offline contextual MDP estimation.
Proposes glocal hypergradient estimation for hyperparameter optimization.
problem Combining reliability and efficiency in hyperparameter optimization.
method Uses Koopman operator theory to approximate global hypergradients from local ones.
result Achieves both reliability and efficiency in hyperparameter optimization.
This paper explores how entropic regularization improves Wasserstein estimators' performance.
problem Improving the approximation and estimation properties of Wasserstein estimators.
method Entropic regularization of optimal transport costs to smooth Wasserstein estimators.
result Entropic regularization can achieve comparable statistical performance to un-regularized estimators at lower computational cost.
New deep learning method simplifies parameter estimation design.
problem Optimal experimental design for parameter estimation with non-linear systems.
method Training a deep network as a Likelihood Free Estimator to simplify design process.
result Deep design improves parameter recovery quality and simplifies design process.
New method estimates and optimizes policy differences using orthogonal learning.
problem Offline reinforcement learning with safety concerns and cost limitations.
method Dynamic R-learner for estimating and optimizing Q π ( s , 1 ) − Q π ( s , 0 ) Q^π(s,1)-Q^π(s,0) Q π ( s , 1 ) − Q π ( s , 0 ) , leveraging orthogonal estimation. result Consistent policy optimization with improved convergence rates.
New method designs experiments robustly for nonlinear estimation, improving parameter knowledge.
problem Designing robust experiments for nonlinear estimation under parametric uncertainty.
method Multi-stage robust optimization framework for sequential experiments.
result Identifies experiments better conducted early for improved parameter knowledge.
Non-linear shrinkage isn't optimal for portfolio optimization, especially when asset dependence is non-stationary.
problem Optimizing portfolios with non-stationary asset dependence structures.
method Derived and compared non-linear shrinkage with an optimal target for covariance matrix estimation.
result Non-linear shrinkage can be significantly improved for portfolio optimization.
MUSE provides unbiased stopping estimates for optimal problems.
problem Estimating the utility of optimal stopping problems.
method Backward recursive construction of the Multilevel Unbiased Stopping Estimator (MUSE).
result MUSE achieves ε-accuracy with O(1/ε^2) computational cost.
Study on distributed nonparametric function estimation with optimal rate and cost of adaptation.
problem Optimal rate of convergence and cost of adaptation in distributed nonparametric function estimation.
method Distributed minimax estimation and adaptive estimation under communication constraints for Gaussian sequence model and white noise model.
result Established minimax rate of convergence and exact communication cost for adaptation.
PULSE estimator improves prediction in causal inference with bounded interventions.
problem Optimizing causal models for bounded interventions.
method Relates K-class estimators to anchor regression, introduces PULSE estimator for minimization of mean squared prediction error with bounded constraints.
result PULSE estimator outperforms other estimators in real data and simulation experiments, especially in weak instrument settings.
A new method detects and corrects outliers using optimal transport.
problem Outliers in data can skew estimation results, leading to inaccurate conclusions.
method Optimal transport with a concave cost function for outlier detection and correction.
result The method effectively identifies and corrects outliers, improving estimation accuracy.