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.
This paper develops efficient surrogate models for optimization of complex dynamical systems.
Optimal sampling strategy improves prediction accuracy with surrogate variables under measurement constraints.
New bounds show polyhedral surrogates are optimal for generalization.
Optimizes hard-to-optimize metrics using adaptive surrogates.
We study the rates of convergence from empirical surrogate risk minimizers to the Bayes optimal classifier. Specifically, we introduce the notion of \emph{consistency intensity} to characterize a surrogate loss function and exploit this notion to obtain the rate of convergence from an empirical surrogate risk minimizer…
Optimizes UUV hull design with a two-orders-of-magnitude speedup.
A new sampling strategy improves reliability and robustness optimization for complex designs.
A new beta-VAE based regression model accelerates oilfield optimization studies.
We establish linear regret bounds for convex smooth losses using Fenchel-Young losses.
SBBO optimizes complex spaces using sampling-based models.
Develops a numerical algorithm for stochastic impulse control using regression surrogates.
This paper investigates the control of an ML component within the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) devoted to black-box optimization. The known CMA-ES weakness is its sample complexity, the number of evaluations of the objective function needed to approximate the global optimum. This weakness is…
This paper describes Plumbing for Optimization with Asynchronous Parallelism (POAP) and the Python Surrogate Optimization Toolbox (pySOT). POAP is an event-driven framework for building and combining asynchronous optimization strategies, designed for global optimization of expensive functions where concurrent function …
Conformal Candidate Certification advances offline MBO by certifying candidate designs with statistical guarantees.
A new method for incorporating preferences in multi-objective Bayesian optimization.
The central task in modeling complex dynamical systems is parameter estimation. This task involves numerous evaluations of a computationally expensive objective function. Surrogate-based optimization introduces a computationally efficient predictive model that approximates the value of the objective function. The stand…
Area under ROC (AUC) is an important metric for binary classification and bipartite ranking problems. However, it is difficult to directly optimizing AUC as a learning objective, so most existing algorithms are based on optimizing a surrogate loss to AUC. One significant drawback of these surrogate losses is that they …
Optimize black-box simulators with local generative models.
Gradient-informed BNNs improve Bayesian optimization performance.
Optimizing expensive black-box systems with limited data is an extremely challenging problem. As a resolution, we present a new surrogate optimization approach by addressing two gaps in prior research -- unimportant input variables and inefficient treatment of uncertainty associated with the black-box output. We first …
Bayesian optimization uses BNNs as efficient surrogate models for expensive function evaluations.
Study proposes a differentiable surrogate loss function for optimizing score in binary classification with imbalanced data.
Hyperboost uses gradient boosting for hyperparameter optimization, outperforming state-of-the-art methods.
Automatically searching for optimal hyperparameter configurations is of crucial importance for applying deep learning algorithms in practice. Recently, Bayesian optimization has been proposed for optimizing hyperparameters of various machine learning algorithms. Those methods adopt probabilistic surrogate models like G…
This paper develops convex surrogates for optimizing the multi-label F-measure.
We learn a compact surrogate model for optimization problems to reduce training and inference time.
New method integrates real and synthetic data to improve machine learning models.
Many applications in machine learning require optimizing a function whose true gradient is unknown, but where surrogate gradient information (directions that may be correlated with, but not necessarily identical to, the true gradient) is available instead. This arises when an approximate gradient is easier to compute t…
Paper extends SMM to weakly convex and multi-convex surrogates for non-convex optimization.
Differentiable pipeline replaces non-differentiable CAE components for shape optimization.
Unified approach for federated learning using MM optimization.
HASSO improves SO algorithms by dynamically tuning hyperparameters.
Paper bounds convergence rate of adversarial surrogate risk.
This research analyzes the consistency of convex and nonconvex surrogate losses for adversarially robust classification.
We propose a robust adversarial prediction framework for general multiclass classification. Our method seeks predictive distributions that robustly optimize non-convex and non-continuous multiclass loss metrics against the worst-case conditional label distributions (the adversarial distributions) that (approximately) m…
We provide novel theoretical insights on structured prediction in the context of efficient convex surrogate loss minimization with consistency guarantees. For any task loss, we construct a convex surrogate that can be optimized via stochastic gradient descent and we prove tight bounds on the so-called "calibration func…
New PG losses improve decision optimization in misspecified models.
The minimization of loss functions is the heart and soul of Machine Learning. In this paper, we propose an off-the-shelf optimization approach that can minimize virtually any non-differentiable and non-decomposable loss function (e.g. Miss-classification Rate, AUC, F1, Jaccard Index, Mathew Correlation Coefficient, etc…
The performance of deep neural networks crucially depends on good hyperparameter configurations. Bayesian optimization is a powerful framework for optimizing the hyperparameters of DNNs. These methods need sufficient evaluation data to approximate and minimize the validation error function of hyperparameters. However, …
Deep Gaussian Processes improve likelihood-free inference for complex distributions.
When a black-box optimization objective can only be evaluated with costly or noisy measurements, most standard optimization algorithms are unsuited to find the optimal solution. Specialized algorithms that deal with exactly this situation make use of surrogate models. These models are usually continuous and smooth, whi…
Bayes-consistent disagreement discrepancy loss improves model robustness.
MF BO combines MFO and BO to optimize expensive problems.
This paper proposes a mechanism to produce equivalent Lipschitz surrogates for zero-norm and rank optimization problems by means of the global exact penalty for their equivalent mathematical programs with an equilibrium constraint (MPECs). Specifically, we reformulate these combinatorial problems as equivalent MPECs by…
Parallel surrogate optimization algorithms have proven to be efficient methods for solving expensive noisy optimization problems. In this work we develop a new parallel surrogate optimization algorithm (ProSRS), using a novel tree-based "zoom strategy" to improve the efficiency of the algorithm. We prove that if ProSRS…
Global optimization problems whose objective function is expensive to evaluate can be solved effectively by recursively fitting a surrogate function to function samples and minimizing an acquisition function to generate new samples. The acquisition step trades off between seeking for a new optimization vector where the…
We study consistency properties of machine learning methods based on minimizing convex surrogates. We extend the recent framework of Osokin et al. (2017) for the quantitative analysis of consistency properties to the case of inconsistent surrogates. Our key technical contribution consists in a new lower bound on the ca…
Proposes MamBO for efficient high-dimensional large-scale optimization.