STEVE improves sample efficiency in reinforcement learning.
problem Combining model-free and model-based reinforcement learning with low sample complexity.
method Dynamic interpolation between model rollouts of various horizon lengths.
result STEVE achieves an order-of-magnitude increase in sample efficiency.
BERET improves binary expansion test for multivariate independence.
problem Testing independence of random vectors in arbitrary dimensions.
method Ensemble approach using sum of squared symmetry statistics and distance correlation.
result Improves power while preserving interpretability.
Ensembles dynamic models using random feature approximations.
problem Online scalable Bayesian learning with dynamic models and ensembling.
method Random feature approximations and dynamic models using random walks.
result Better performance with alternative basis expansions like Hilbert space Gaussian processes.
Paper improves training physics-informed neural networks with model ensembles.
problem Training physics-informed neural networks (PINNs) is difficult due to convergence to wrong solutions.
method Proposes training an ensemble of PINNs, using ensemble agreement to expand the solution interval.
result Algorithm stabilizes PINN training and yields competitive performance.
Residual networks' depth is mathematically equivalent to expanding an implicit ensemble size.
problem Understanding why deep residual networks are effective.
method Formal analysis of residual networks as ensembles of shallow models.
result Increasing network depth is equivalent to expanding the size of an implicit ensemble, revealing a hierarchical structure.
TREX explains tree ensembles by identifying key training examples.
problem Identifying which training examples most influence tree ensemble predictions.
method TREX builds a surrogate model using a kernel that captures tree ensemble structure, approximating the original model.
result TREX provides accurate and effective explanations for tree ensembles.
ELMV uses ensemble learning to handle missing values in EHR data.
problem Significant missing values in EHR data cause bias and unreliable conclusions.
method ELMV constructs multiple subsets with lower missing rates and uses a support set for ensemble learning.
result ELMV outperforms conventional methods in critical feature identification and outcome prediction.
Improved online learning for fuzzy min-max neural networks.
problem Classification performance issues due to expansion and contraction steps.
method Proposes an improved online learning algorithm without contraction for overlapping hyperboxes.
result Significant improvement in classification accuracy and stability.
Paper develops streaming algorithms to estimate classifier accuracy on unlabeled data.
problem Estimating classifier accuracy on unlabeled data with noisy decisions.
method Two algebraic evaluators: majority voting and a novel method to handle correlated classifiers.
result The novel method can be as accurate as 1% when handling small amounts of correlation.
We establish multiparameter resolvent trace expansions for elliptic boundary value problems, polyhomogeneous both in the resolvent and the auxiliary parameter. The present analysis is rooted in the joint project with Matthias Lesch on multiparameter resolvent trace expansions on revolution surfaces with applications to…
A new stopping rule based on E-values helps efficiently use sampling in Bayesian Deep Ensembles.
problem How long should sampling continue in Bayesian Deep Ensembles to yield significant improvements?
method Formulated as a sequential anytime-valid hypothesis test, using E-values to decide when to stop sampling.
result Only a fraction of the full-chain budget is often required for significant improvements.
Surrogate models improve tidal model calibration efficiency.
problem Efficiently calibrate complex tidal models for climate change scenarios.
method Proposes two surrogate-based methods to replace complex models: PODEn3DVAR and POD-PCE-3DVAR.
result Both methods show superior convergence and robustness to noise compared to classical 3DVAR.
Study on random representations of surface groups into SU(n), focusing on asymptotic expansions.
problem Understanding random representations of surface groups into special unitary groups.
method Use of a symplectic form on moduli space, establishing asymptotic expansions for trace values.
result Existence of large n asymptotic expansions for expected values of trace of elements under random representations.
Ensemble method for fast portfolio valuation and risk management.
problem Dynamic portfolio valuation and risk management from cash flow data.
method Regression trees for dynamic value process learning.
result Fast and accurate estimator with closed-form solution.
Ensemble method detects time series anomalies without preselecting parameter values.
problem Anomaly detection requires known anomaly length, limiting practicality.
method Ensemble grammar induction for variable-length anomalies.
result Ensemble approach outperforms existing methods with different parameter selections.
Approximates CVA of European options with WWR using correlation expansions.
problem Computing CVA of European options with Wrong Way Risk in a default intensity setting.
method Exploits a correlation expansion approach to approximate option pricing.
result Numerical evaluations show the method's performance compared to existing methods.
The paper finds optimal N for maximizing Sudler products using Ostrowski expansions.
problem Maximizing Sudler products for given α. method Characterizes N using Ostrowski expansions and cotangent sums. result Precise estimates for max and sum of Sudler products.
Despite the advancement of supervised image recognition algorithms, their dependence on the availability of labeled data and the rapid expansion of image categories raise the significant challenge of zero-shot learning. Zero-shot learning (ZSL) aims to transfer knowledge from labeled classes into unlabeled classes to r…
Paper improves risk estimation for extreme events.
problem Estimating extreme risks accurately.
method Modified Bayes risk for expectiles, asymptotic expansions, efficient estimators.
result Asymptotic normality of estimators proved.
This work explores functional expansions to handle path dependence in various fields.
problem Path dependence and infinite-dimensional problems in non-Markovian systems.
method Generalizes Wiener series and functional Taylor expansion to handle static and dynamic functionals.
result Elegant separation of functionals from future trajectories in dynamic cases.
Bayesian optimization tackles unknown search spaces with automatic expansion.
problem Bayesian optimization in unknown search spaces is challenging.
method Proposes a systematic volume expansion strategy to find points close to the objective function maximum without specifying parameters.
result Derives analytic expressions for expansion triggers and sizes, achieving epsilon-accuracy after a finite number of iterations.
This study introduces a new GAS blending ensemble model for Bitcoin price prediction.
problem Predicting Bitcoin price fluctuations in the cryptocurrency market.
method Integrates advanced ensemble learning methods, feature selection algorithms, and sentiment analysis.
result The GAS model demonstrates excellent performance in daily Bitcoin trend prediction.
This paper reviews weighted clustering ensemble methods.
problem Improving clustering results from individual methods.
method Different types of weights and approaches to determining weight values.
result Unified framework for selecting appropriate weighting mechanisms.
Secure Aggregation protocols allow a collection of mutually distrust parties, each holding a private value, to collaboratively compute the sum of those values without revealing the values themselves. We consider training a deep neural network in the Federated Learning model, using distributed stochastic gradient descen…
A novel approach uses an ensemble of Gaussian processes for robust and adaptive reinforcement learning.
problem Adaptive reinforcement learning in large or continuous state spaces.
method Online scalable (OS) approach with a weighted ensemble of Gaussian processes.
result The ensemble approach improves performance in adversarial settings.
The Magnus expansion is a universal finite type invariant of pure braids with values in the space of horizontal chord diagrams. The Conway polynomial composed with the short circuit map from braids to knots gives rise to a series of finite type invariants of pure braids and thus factors through the Magnus map. We descr…
Paper applies theorem to find optimal investment boundary in stochastic capacity expansion.
problem Finding optimal investment boundary in a stochastic, time-inhomogeneous capacity expansion problem.
method Applies Bank and El Karoui Representation Theorem to solve first order conditions involving a non-integral term.
result Existence of base capacity ly⋆(t), showing optimal investment process becomes active at this level. A reinforcement learning framework combining value function and tree search planner for strategic and tactical decisions.
problem Strategic and tactical decision-making in discrete environments.
method Combines value function and tree search planner, using uncertainty modeling and risk measurement.
result Improves performance and learning speed on hard exploration environments.
Paper proves existence of minimal surfaces with alternating multiple zeta values.
problem Existence and properties of minimal surfaces.
method Complex analytic methods to deform Lawson surfaces.
result Area of minimal surfaces ξ1,g is monotonically increasing in genus g. Randomized gradient-based ensemble improves prediction accuracy.
problem Improving prediction accuracy in machine learning.
method Randomization and gradient-based aggregation of weakly-correlated estimators.
result The method outperforms existing techniques in terms of increased accuracy.
Expands method for pricing foreign exchange options under stochastic volatility and interest rates.
problem Approximating pricing of foreign exchange options with no exact formula.
method Directly expands the expectation value of payoff function with respect to the volatility of volatility, then uses it to price options in the stochastic volatility model.
result Shows numerically comparable results to Grzelak et al. (2012) using characteristic function approximation.
New technique prevents Q-learning collapse by maximizing diversity among ensembles.
problem Value function collapse in ensemble Q-learning.
method Maximizing representation diversity through regularization.
result Regularized approach significantly outperforms existing methods.
Let φ∈C∞(Cn) be a given real valued function. We assume that $\pr\ddbarφ$ is non-degenerate of constant signature (n−,n+) on Cn. When q=n−, it is well-known that the Bergman kernel for (0,q) forms with respect to the k-th weight e−2kφ, k>0, admits a full asymptotic expansi…
Formula for Toeplitz operator kernel on CR manifolds.
problem Analyzing Toeplitz operators on CR manifolds.
method Formula for the symbol of the kernel, asymptotic expansions.
result Formula for the values at the diagonal of the second coefficient in the expansion of the symbol of the kernel.
Study of matrix group integrals for O(n) and Sp(n) via surface maps and mapping class groups.
problem Understanding the expected value of traces in orthogonal and symplectic groups using surface maps and mapping class groups.
method Analyzes the Laurent expansion of expected values of traces in orthogonal and symplectic groups, relating them to surface maps and mapping class groups.
result Obtains a convergent Laurent expansion for TrwO(n) involving surface maps and mapping class groups, respecting automorphism symmetry. A new EnKF method for elliptic PDEs reduces dimensionality for accurate state estimation.
problem Elliptic PDEs in fluid flows make traditional EnKF regularization ineffective.
method Low-rank factorization of the Kalman gain based on the Jacobian spectrum.
result Inference can be performed in a low-dimensional subspace of the state space.
For two oriented simple closed curves on a compact orientable surface with a connected boundary we introduce a simple computation of a value in the first homology group of the surface, which detects in some cases that the geometric intersection number of the curves is greater than zero when their algebraic intersection…
UVU simplifies value uncertainty quantification in RL.
problem Estimating epistemic uncertainty in value functions for reinforcement learning.
method UVU uses squared prediction errors between an online learner and a fixed, randomly initialized target network, incorporating policy-conditional value uncertainty.
result UVU achieves equal performance to large ensembles on challenging offline RL settings, with computational savings.
A new method selects the best ensemble for concept drift detection.
problem Concept drift detection in data streams.
method Dynamic ensemble selection focusing on decisionspace.
result Highest detection precision and lowest false alarms.
Combines model-free Q-ensembles and model-based approaches for improved exploration.
problem Improving exploration strategies in reinforcement learning.
method Integrates model-free Q-ensembles and model-based trajectory memory approaches.
result Model-based trajectory memory combined with Q-ensembles outperforms Q-ensembles alone.
Improved surrogate model for field-valued QoIs using LF and HF simulations.
problem Accurate and efficient modeling of field-valued quantities under uncertain inputs.
method Bifidelity Karhunen-Loève expansion with active learning.
result Consistent improvements in predictive accuracy and sample efficiency.
Ensemble CNNs improve mode classification in smartphone travel surveys.
problem Classifying transportation modes from smartphone travel survey data.
method Developed an ensemble of CNN models with different architectures and hyper-parameters, combined using average voting, majority voting, optimal weights, and a Random Forest meta-learner.
result The ensemble method with Random Forest as meta-learner achieved 91.8% accuracy, surpassing other methods.
Paper examines risk measure expansions under FGM dependence, improving accuracy at extreme levels.
problem Capturing higher-order tail behavior and dependence effects in risk measures.
method Second-order asymptotic expansions using extreme value theory and regular variation theory.
result Second-order approximations reduce approximation errors, especially at extreme confidence levels.
Rainfall ensemble forecasts have to be skillful for both low precipitation and extreme events. We present statistical post-processing methods based on Quantile Regression Forests (QRF) and Gradient Forests (GF) with a parametric extension for heavy-tailed distributions. Our goal is to improve ensemble quality for all t…
Modified ensembling scheme provides Bayesian posterior estimation in neural networks.
problem Lack of principled uncertainty estimation in neural networks.
method Derive and implement a modified ensembling scheme that estimates Bayesian posterior.
result Consistent estimator of Bayesian posterior in wide neural networks.
Study predicts soccer player market values using machine learning and SHAP for interpretability.
problem Predicting accurate market values for professional soccer players.
method Ensemble machine learning models, SHAP for interpretability, Boruta for feature selection.
result GBDT model achieved high predictive accuracy (R-squared 0.901, RMSE 3,221,632.175).
The paper calculates Bachelier option prices using Taylor expansions and applies it as a variance reduction technique.
problem Calculating Bachelier option prices and variance reduction in correlated cases.
method Taylor expansions and classical Itô calculus to derive option prices, uses negative powers of future mean volatility.
result The paper provides a new method to calculate Bachelier option prices and applies it to reduce variance in Monte Carlo simulations.
Finite-width neural networks are approximated by Gaussian processes with finite size corrections.
problem Understanding the behavior of finite-width neural networks as they approach infinite width.
method Analyzing the distribution of outputs at initialization for large, finite neural networks with a single hidden layer.
result The distribution of outputs at initialization is well described by a Gaussian perturbed by the fourth Hermite polynomial, with the perturbation scale inversely proportional to the number of network units.