New method improves density estimation accuracy and resistance to dimensionality issues.
problem Improving density estimation accuracy and resistance to dimensionality issues.
method Best-scored random forest density estimation method.
result The method achieves better estimation results than other state-of-the-art approaches.
The abstract establishes a model-independent relationship for life insurance valuation and validation.
problem Validating best estimate calculation models in traditional life insurance.
method Derives a model-independent relationship and lower bound formula for valuation.
result Validates models for Solvency~II best estimate calculation using publicly available data.
A rule selects the best gradient estimator for faster convergence in machine learning.
problem Choosing the best gradient estimator for faster convergence in machine learning.
method Analyzed convergence rates of SGD as a function of time, resulting in a simple rule to select the best estimator.
result The selected estimator leads to the best optimization convergence guarantee, same for different SGD variants and objective types.
Stacked regressions improve predictive accuracy by combining estimators.
problem Improve predictive accuracy in regression models.
method Analogous to least-squares, learn combination weights by minimizing regularized empirical risk with nonnegativity constraint.
result The stacked estimator has strictly smaller population risk than the best single estimator, especially when signal-to-noise ratio is small.
This note fills the gap in market-consistent valuation of lifelong health insurance products.
problem Market-consistent valuation of lifelong health insurance products is not well-addressed.
method Constructs a valuation portfolio to separate Best Estimate into policy data and financial instrument prices.
result The Best Estimate valuation is not uniquely determined by prevailing term structures and requires a stochastic model.
New strategy optimally identifies best arm in unknown variance Gaussian bandits.
problem Identifying the best arm in two-armed Gaussian bandits with unknown variances.
method Proposes a Neyman Allocation (NA)-Augmented Inverse Probability weighting (AIPW) strategy to estimate variances and draw arms adaptively.
result Demonstrates asymptotic optimality of the proposed strategy in the small-gap regime.
This paper provides estimation and inference methods for the best linear predictor (approximation) of a structural function, such as conditional average structural and treatment effects, and structural derivatives, based on modern machine learning (ML) tools. We represent this structural function as a conditional expec…
Optimizes subset selection in sparse learning problems.
problem Sparse learning problems, particularly best subset selection.
method Developed an efficient primal-dual algorithm leveraging dual range estimation and incremental strategy.
result Improves solutions of best subset selection with reduced redundant computation.
Paper improves action selection for accurate parameter estimation in linear bandits.
problem Best action identification in stochastic linear bandits with fixed confidence constraints.
method Designs a sequential adaptive policy to estimate underlying parameter efficiently.
result The designed policy achieves the same estimation error scaling as a lower bound.
A fast algorithm selects best subsets in high-dimensional models.
problem Identifying sparse models in high-dimensional generalized linear models.
method Splicing technique for fast and consistent best subset selection.
result Our algorithm achieves high certainty in selecting best subsets with polynomial computational complexity.
In this paper, we propose a verified numerical method for obtaining a sharp inclusion of the best constant for the embedding H01(Ω)↪Lp(Ω) on bounded convex domain in R2. We estimate the best constant by computing the corresponding extremal function using a verified numerical com…
Study evaluates model selection methods for time series forecasting.
problem Evaluating which model is best for time series forecasting.
method Compared various estimation methods for selecting the best model.
result Accuracy of model selection estimators is low, and performance loss is significant.
This text is a survey on cross-validation. We define all classical cross-validation procedures, and we study their properties for two different goals: estimating the risk of a given estimator, and selecting the best estimator among a given family. For the risk estimation problem, we compute the bias (which can also be …
Algorithm identifies best arm with biased proxy and selective ground truth audits.
problem Fixed-confidence best-arm identification with biased proxy and selective ground truth.
method Propensity-weighted estimator and adaptive auditing algorithm.
result Plug-in Neyman rule achieves near-oracle audit efficiency.
Synth-validation selects the best causal inference method for a dataset.
problem Selecting the best causal inference method for a given dataset.
method Cross-validation analogy applied to causal inference methods.
result Synth-validation lowers expected estimation error.
Optimal tuning of Tikhonov regularizers achieves best performance without additional cost.
problem Selecting the best estimator among Tikhonov regularized estimators or their linear combinations.
method Convex aggregation procedure based on Q-aggregation. result Error term does not depend on penalty matrix or number of estimators.
FlexCodeTS is a flexible time series density estimator.
problem Estimating conditional densities for time series data.
method Nonparametric conditional density estimator based on arbitrary regression methods.
result FlexCodeTS adapts its convergence rate based on the chosen regression method.
Study optimizes experimental design for best treatment arm identification.
problem Identifying the best treatment arm given contextual information.
method Adaptive Sampling-Policy Learning (PLAS) strategy for minimax rate optimality.
result PLAS strategy achieves minimax rate optimality in expected simple regret.
New method optimizes language model performance for test-time strategies.
problem Mismatch between training objectives and test-time deployment of large language models.
method Tail-Extrapolated estimators to approximate best-of-N performance from limited training rollouts.
result Improved performance of best-of-N deployment across various models and datasets.
We estimate the rate of change of the best constant in the Sobolev inequality of a Euclidean domain which moves outward. Along the way we prove an inequality which reverses the usual Holder inequality, which may be of independent interest.
New method finds best arm minimizing mean-squared error in correlated bandits.
problem Finding an arm that best captures information about other arms.
method Formulated correlated bandit problem, derived MSE estimator, proposed algorithm.
result Proposed algorithm identifies best arm with error probability bounds.
EBBS integrates expert assessments into MIO best-subsets problem.
problem Identifying relevant features in statistical models.
method Expert-implied Bayesian approach using MIO.
result Reduces to classical best-subsets when experts are silent.
Algorithm selects best model based on state, reducing costs.
problem Choosing the best model among many in different states of the world.
method Reinforcement learning algorithm to estimate optimal policy.
result Algorithm consistently selects optimal model based on covariates.
RealCause provides a realistic benchmark for causal inference.
problem Lack of a reliable benchmark for comparing causal effect estimators.
method Flexible generative models to create a benchmark that is both ground-truth and realistic.
result Evaluation of over 1500 causal estimators provides evidence for choosing hyperparameters using predictive metrics.
New model bridges pricing and reserving for insurance claims.
problem Incomplete claim data due to reporting and settlement delays.
method Develops an occurrence and development model to estimate both claims and premiums.
result Effective resolution of pricing and reserving inconsistencies.
Study best arm identification with contextual info, achieving optimal misidentification probability.
problem Identify the best treatment arm with minimal misidentification probability in a small gap scenario.
method Developed RS-AIPW strategy that matches lower bound of misidentification probability in the small-gap regime.
result RS-AIPW strategy is asymptotically optimal for best arm identification.
The paper tackles best arm identification in contaminated bandits with optimal error guarantees and sample complexity.
problem Best arm identification in stochastic bandits with adversarial reward contamination.
method Proposes two algorithms: a gap-based algorithm and a successive elimination-based algorithm for sub-Gaussian bandits.
result Asymptotically optimal sample complexity for both algorithms.
Optimal strategy found for identifying best arm in bandits with small gap.
problem Best arm identification in two-armed bandits with a fixed budget and small gap.
method Neyman allocation rule augmented with inverse probability weighting.
result Proposed strategy is asymptotically optimal when gap is small.
New algorithms identify best arm with less pulls, adapting to arm covariances.
problem Best arm identification under dependent and correlated arm distributions.
method Adaptive algorithms estimating arm covariances to minimize pulls.
result Substantial improvement in best arm identification over standard setting.
Efficient algorithm solves best subset selection problem.
problem Sparse learning problems, especially best subset selection.
method Primal-dual method based on dual forms of ℓ0-regularized problems. result Improves solutions of best subset selection with reduced redundant computation.
New algorithm identifies best arm efficiently in stochastic bandits.
problem Efficiently identifying the best arm in stochastic bandits with optimal performance.
method Develops a computationally efficient algorithm for optimal best arm identification.
result Achieves optimal performance with minimal computational complexity.
Profile entropy measures learnability and compressibility of discrete distributions.
problem Understanding the learnability and compressibility of discrete distributions.
method Investigates profile entropy, showing its role in estimation, inference, and compression.
result Profile entropy is a fundamental measure unifying estimation, inference, and compression.
Optimal strategy identified for minimizing regret in fixed-budget best arm selection.
problem Minimizing expected simple regret in fixed-budget best arm selection.
method Two-Stage (TS)-Hirano-Imbens-Ridder (HIR) strategy using HIR estimator.
result TS-HIR strategy is asymptotically minimax optimal.
Ten sharp lower estimates of the first non-trivial eigenvalue of Laplacian on compact Riemannian manifolds are reviewed and compared. An improved variational formula, a general common estimate, and a new sharp one are added. The best lower estimates are now updated. The new estimates provide a global picture of what on…
New approach for reward-free exploration reduces estimation error.
problem Reward-free exploration in reinforcement learning.
method Adaptive approach reducing MDP estimation error.
result Reward-free UCRL algorithm improves sample complexity.
The study provides guarantees for diffusion-based models under log-concave data, offering best-known convergence rates.
problem Theoretical guarantees for convergence of diffusion-based generative models under log-concave data distributions.
method Assumption of strongly log-concave data distributions, Lipschitz continuous functions for score estimation, and novel auxiliary process.
result Best known upper bounds for Wasserstein-2 distance between Gaussian distribution and sampling algorithm.
The paper improves neural network-based conditional density estimation for finance.
problem Capturing statistical relationships between variables using neural networks.
method Best practices and benchmarks for conditional density estimation with noise regularization and data normalization.
result Proposed methodology outperforms other estimators in various benchmarks.
New methods recover best rank-r approximations from few entries.
problem Recovering best rank-r approximations from limited data entries.
method Two agnostic approaches: spectral truncation and projected gradient descent.
result Projected gradient descent yields superior performance.
A novel algorithm reduces communication costs in federated best arm identification.
problem Identifying the best arm in a federated multi-armed bandit setup with minimal communication cost.
method Proposes a novel algorithm called FedElim that communicates only in exponential time steps.
result Demonstrates that communication is almost cost-free in FedElim, with a total cost at most 3 times the maximum under its variant.
The tGARCH-NIG model best estimates Bitcoin volatility.
problem Estimating volatility of Bitcoin with skewed and leptokurtic distributions.
method Three GARCH models (sGARCH, iGARCH, tGARCH) with different distributions.
result tGARCH-NIG model best captures Bitcoin volatility.
New algorithm reduces high-dimensional data processing costs and achieves true sparsity.
problem High computational costs and difficulty in achieving true sparsity in distributed inference.
method Two-stage distributed best subset selection with oracle property.
result Correctly finds true sparsity pattern and achieves the oracle property.
New method selects best HTE estimator without ground-truth treatment effects.
problem Selecting best HTE estimator from multiple candidates.
method Cross-fitted, exponentially weighted test statistic with two-way sample splitting.
result Empirically, reliable error control and reduced false selections.
Subset selection struggles in high noise; new method improves performance.
problem Subset selection's poor performance in high noise levels.
method Regularized version of least-squares criterion.
result Proposed estimators outperform best subset selection in high noise.
In this paper we discuss the variable selection method from \ell0-norm constrained regression, which is equivalent to the problem of finding the best subset of a fixed size. Our study focuses on two aspects, consistency and computation. We prove that the sparse estimator from such a method can retain all of the importa…
Study best-response learning dynamics in zero-sum polymatrix games under full and minimal information settings.
problem Learning dynamics in zero-sum polymatrix games under different information settings.
method Two-timescale learning dynamics combining smoothed best-response updates and TD-learning for estimating local payoff functions.
result Polynomial-time finite-sample guarantees for convergence to an ε-Nash equilibrium in the minimal information case.
New algorithm reduces best-in-class regret in contextual bandits.
problem Compete with the best policy in a class without model restrictions.
method Proposes an algorithm that updates policies by minimizing a pessimistic objective, including a clipped inverse-propensity estimate and variance penalty.
result Achieves fast best-in-class regret rates, including polylogarithmic rates in the parametric case.
C-Learner improves stability of plug-in estimators for causal inference.
problem Limited overlap between treatment and control groups leads to unstable estimates.
method Constrained learning framework that achieves stability and asymptotic properties.
result Constrained learning produces stable estimates with desirable asymptotic properties.
Best-of-Majority improves inference performance in Pass@k settings.
problem Inference in difficult tasks often underperforms with single-shot selection methods.
method Combining majority voting and Best-of-N, Best-of-Majority restricts candidates to high-frequency responses.
result Best-of-Majority achieves minimax optimal regret and outperforms other methods.