Unified method for estimating properties of large domain distributions efficiently.
problem Estimating properties of distributions over large domains efficiently.
method Piecewise-polynomial approximation technique for constructing sample- and time-efficient estimators.
result Near-linear-time computable estimators with optimal and highly-concentrated approximation values.
CR method improves convergence for nonconvex optimization under KL property.
problem Improving convergence rate for nonconvex optimization problems.
method Cubic-regularized Newton's method exploiting Kurdyka-Lojasiewicz (KL) property.
result Asymptotic convergence rates of various optimality measures are fully characterized.
Mol-CycleGAN generates optimized molecules with similar structure.
problem Designing molecules with desired properties is challenging.
method CycleGAN-based model that generates optimized compounds with high structural similarity.
result Significantly outperforms previous results in optimizing penalized logP of drug-like molecules.
BOAT optimizes multiple antibody properties efficiently.
problem Balancing multiple drug-like properties in antibody design.
method Bayesian optimization framework coupling surrogate modeling and genetic algorithm.
result Competitive performance with state-of-the-art multi-objective protein optimization methods.
The paper examines properties of GW optimal transport plans, showing they can be sparse and permutation-supported.
problem Properties of Gromov-Wasserstein optimal transport plans.
method Exploration of sparsity, permutation support, and cyclical monotonicity properties.
result GW optimal plans can be sparse and permutation-supported under certain conditions.
We consider an optimal control problem of a property insurance company with proportional reinsurance strategy. The insurance business brings in catastrophe risk, such as earthquake and flood. The catastrophe risk could be partly reduced by reinsurance. The management of the company controls the reinsurance rate and div…
Enhances BO with expert preferences about abstract properties.
problem Lack of expert knowledge in BO for black-box experimental design.
method Human-AI collaboration to incorporate expert preferences into surrogate modeling.
result Superior performance compared to baselines in synthetic and real-world datasets.
Formulates Markov property for risk-sensitive dynamic optimisation.
problem Risk-sensitive dynamic optimisation problems in discrete time.
method Formulates probabilistic Markov property under dynamic risk framework.
result Property holds for standard risk measures and has multiple equivalent versions.
New method solves doubly-nonconvex composite optimization problems.
problem Solving composite optimization problems with both functions nonconvex.
method Stochastic gradient descent with quasiconvex penalty function.
result Convergence properties for doubly-nonconvex composite optimization.
GCPN uses reinforcement learning to generate molecules optimizing desired properties.
problem Generating novel molecules with desired properties while obeying physical laws.
method Graph Convolutional Policy Network (GCPN) trained with reinforcement learning.
result GCPN achieves significant improvements in molecule optimization tasks.
Unified plug-in approach for estimating symmetric properties of distributions efficiently.
problem Estimating symmetric properties of distributions with high accuracy and efficiency.
method Profile-maximum-likelihood (PML) based estimator.
result Achieves theoretical limit for universal symmetric property estimation.
Bayesian model ranks treatments in multi-response experiments.
problem Identifying the best treatment among competing ideal properties.
method Bayesian approach with Markov Chain Monte Carlo algorithm.
result Reliable inference of treatment ranks in practice.
PML estimator optimally solves three statistical learning problems.
problem Distribution estimation, property estimation, and property testing.
method Profile Maximum Likelihood (PML) estimator.
result PML achieves optimal sample complexity for various learning tasks.
PPGD solves nonconvex nonsmooth optimization problems without KL property.
problem Nonconvex and nonsmooth optimization problems in statistics and machine learning.
method Projective Proximal Gradient Descent (PPGD) for solving a class of nonconvex and nonsmooth problems.
result PPGD achieves a fast convergence rate of O(1/k^2) for k ≥ k_0.
Meta-learning symbolic default hyperparameters from dataset properties.
problem Empirical hyperparameter optimization is slow and requires manual configuration.
method Evolutionary algorithm to learn symbolic hyperparameter formulas from dataset properties.
result Meta-learning finds viable symbolic defaults for ML algorithms.
Developed neural network for predicting mechanical properties of composite materials.
problem Predicting and optimizing mechanical properties of composite materials.
method Convolutional neural network model integrated with a genetic algorithm optimizer.
result Highly accurate predictions and optimal microstructural designs identified.
Study generalizes property elicitation to imprecise probabilities.
problem Minimizing risk over imprecise probability distributions.
method Maximin risk minimization over a set of imprecise probabilities.
result Conditions for elicitability of IP-properties.
Study uses property elicitation to understand how fairness regularizers affect optimal decisions.
problem Understanding how fairness regularizers change the optimal decision in predictive algorithms.
method Property elicitation to analyze the relationship between loss, regularization, and optimal decision.
result Necessary and sufficient condition for when a property changes with the addition of a regularizer.
The paper analyzes ℓq optimization methods for high-dimensional linear regression.
problem Estimating sparse parameters from noisy observations in high-dimensional settings.
method Introduces and analyzes ℓq optimization methods for sparse estimation. result Shows stable recovery properties and bounds for ℓq minimization and regularization methods. ChemBO optimizes small organic molecules for synthesis and desired properties.
problem Designing and optimizing new organic molecules for specific properties.
method Bayesian optimization framework that considers synthesizability constraints.
result ChemBO generates synthesizable candidates efficiently and effectively.
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.
This paper analyzes statistical properties of the Robust Satisficing model.
problem Lack of statistical theory for the Robust Satisficing model.
method Comprehensive analysis of statistical properties, including confidence intervals and generalization error bounds.
result Established two-sided confidence intervals and finite-sample generalization error bounds for the RS optimizer.
New dynamical approach defines symmedian as hyperbolic barycenter.
problem Understanding symmedian properties in hyperbolic geometry.
method Developed a new dynamical coordinatization.
result Symmedian point acts as hyperbolic barycenter.
Proximal methods avoid local minima in weakly convex problems.
problem Weakly convex optimization problems with strict saddle properties.
method Proximal methods on nonsmooth functions with strict saddle guarantees.
result Proximal methods converge to local minimizers only, when initialized randomly.
Optimal transport with scalar martingales defined over multiple periods.
problem Finding optimal transport plans with specific properties over multiple time periods.
method Introducing left-monotone transports and characterizing them through various properties.
result Left-monotone transports are unique under certain conditions and have specific order properties.
The paper proposes a method to test properties of the optimal assortment in multinomial logit models.
problem Uncertainty quantification for the optimal assortment in multinomial logit models.
method The paper proposes a novel inferential framework to test properties of the optimal assortment in multinomial logit models, reducing the problem to detecting the sign change point of marginal revenue gaps.
result The asymptotic normality of the marginal revenue gap estimator and the construction of a maximum statistic to detect the sign change point.
The study examines the properties of linear regions in DNNs and how optimization techniques affect them.
problem Understanding the expressivity of deep neural networks through their linear regions.
method Empirical analysis of local properties of linear regions, including inspheres, hyperplane directions, decision boundaries, and surrounding regions.
result Different optimization techniques lead to distinct linear regions, even with similar classification accuracy.
This paper optimizes performative risk by focusing on convex properties and developing efficient algorithms.
problem Performative risk, the loss experienced by decision makers, is not optimized by stable models.
method Identifying convex properties of loss function and model-induced distribution shift, developing algorithms for optimization.
result Optimization of performative risk with better sample efficiency than generic methods.
Investment and consumption models show a threshold for optimal policies that converge to a steady state.
problem Optimal investment and consumption policies in financial models.
method Analytical and numerical methods to find and validate the turnpike property and convergence rate.
result Threshold value determines the turnpike property for investment policies, independent of specific utility functions.
A new method for stochastic optimization using virtual gradients.
problem Stochastic optimization challenges in computational efficiency and memory usage.
method Inspired by dynamic programming, SVGD uses a computational graph and automatic differentiation for efficient optimization.
result Experimental results show SVGD outperforms other methods on multiple datasets and network models.
PropEn uses matching to create a larger dataset for efficient design optimization.
problem Limited data and complex landscapes in scientific applications.
method PropEn uses a matching approach to implicitly guide design without a discriminator.
result PropEn efficiently approximates the gradient of property improvement within the data distribution.
The study addresses biases in evaluating molecular optimization methods and proposes methods to reduce these biases.
problem Biases in in silico evaluation of molecular optimization methods.
method Discussion and empirical investigation of bias reduction methods for predictor misspecification and sample reuse.
result Empirical investigation of bias reduction methods for predictor misspecification and sample reuse.
IH-GAN models cellular structures accurately and improves structural performance.
problem Optimizing variable-density cellular structures with multiscale design challenges.
method Conditional deep generative model (IH-GAN) for property-to-geometry mapping using implicit function parameterization.
result Generates unit cells with high accuracy and improves structural performance.
Bayesian Algorithm Execution uses mutual information to infer properties of black-box functions efficiently.
problem Estimating computable properties of expensive black-box functions with limited evaluations.
method Sequentially choosing queries that maximize mutual information with respect to the algorithm's output.
result InfoBAX reduces query counts by up to 500 times compared to the original algorithm.
We systematically investigate the problem of representing Markov chains by families of random maps, and which regularity of these maps can be achieved depending on the properties of the probability measures. Our key idea is to use techniques from optimal transport to select optimal such maps. Optimal transport theory a…
A new model designs molecules with desired properties.
problem Finding molecules with optimal chemical or biological properties.
method A latent prompt Transformer model with three components: latent vector, molecule generation, and property prediction.
result The model achieves state-of-the-art performance on molecule design tasks.
BBRT improves molecular properties through iterative translation.
problem Optimizing molecular structures for improved biochemical properties.
method Iterative translation of molecules using a black box approach.
result Improvement in molecular properties with each iteration of the translator.
Paper optimizes material microstructures with limited data using probabilistic methods.
problem Optimizing material properties with uncertain process-structure-property links.
method Flexible probabilistic formulation, data-driven surrogate, active learning.
result Significant improvement in accuracy with small training data.
MHG-VAE achieves 100% valid molecules with simpler architecture.
problem Generating valid molecules and evaluating properties efficiently.
method MHG-VAE uses molecular hypergraph grammar to guide a single VAE.
result 100% validity achieved with simpler architecture.
Study optimal strategies under uncertain market parameters and borrowing costs.
problem Optimal portfolio-consumption strategies in uncertain markets.
method Robust utility maximization framework with explicit solutions.
result Impacts of uncertain parameters, constraints, and borrowing costs quantified.
GeoAdaLer enhances geometric understanding of Adam for stochastic optimization.
problem Understanding geometric principles behind Adam's success in stochastic optimization.
method Introduces GeoAdaLer, an adaptive learning method based on geometric properties.
result Extends interpretability and effectiveness in complex optimization scenarios.
New research extends optimal transport map breakdown properties to general costs.
problem Understanding robustness of optimal transport maps under contamination.
method Analyzing breakdown point of optimal transport maps for general convex costs.
result Breakdown point of optimal transport maps is independent of the cost function.
Flexible framework integrates machine learning and DRO for uncertain parameter prediction.
problem Limited joint observations of uncertain parameters and covariates.
method Wasserstein, sample robust optimization, and phi-divergence-based ambiguity sets.
result Validation of theoretical and practical benefits in limited data scenarios.
We study an optimal execution problem in the presence of market impact where the security price follows a geometric Ornstein-Uhlenbeck process, which implies the mean-reverting property, and show that the optimal strategy is a mixture of initial/terminal block liquidation and gradual intermediate liquidation. The mean-…
This work analyzes the statistical properties of adaptive gradient methods.
problem Lack of understanding of the statistical properties of adaptive gradient methods.
method Theoretical analyses and experiments on the variance of update magnitudes.
result The variance of update magnitudes is an increasing and bounded function of time, not diverging.
This paper explores how to choose scoring rules for estimating properties with parametric assumptions.
problem Indirect elicitation of properties with parametric assumptions.
method Developed a framework for choosing proper scoring rules for indirect elicitation, considering constraints and optimal solutions.
result The optimal estimation of the target property changes monotonically with the increase of each weight, and often setting some weights as zero yields the best configuration.
The rectified flow method is analyzed for its statistical properties.
problem Theoretical support for rectified flow methods is lacking.
method Empirical analysis of rectified flow's statistical properties using regression and density estimation.
result Convergence rates for rectified flow estimators are faster than for nonparametric regression and density estimation.
Investigates maps and properties in spaces with negative dimensions and curvature.
problem Existence of transport maps and local-to-global property in spaces with negative dimensions and bounded Ricci curvature.
method Examines metric measure spaces with negative curvature dimensions and applies reduced curvature-dimension conditions.
result Establishes the existence of transport maps and proves the local-to-global property.