arXiv research

Bayesian optimization tackles constrained high-dimensional problems with penalties and trust regions.

Region-specific linear models are widely used in practical applications because of their non-linear but highly interpretable model representations. One of the key challenges in their use is non-convexity in simultaneous optimization of regions and region-specific models. This paper proposes novel convex region-specific…

Two multifidelity trust-region methods use low-fidelity models for efficient optimization.

We prove that the optimal way to enclose and separate four planar regions with equal area using the less possible perimeter requires all regions to be connected. Moreover, the topology of such optimal clusters is uniquely determined.

Novel BSG method for efficient stochastic optimization.

AdaScale-TuRBO improves high-dimensional Bayesian optimization by dynamically scaling the GP lengthscale.

TREGO improves EGO for global optimization of high-dimensional problems.

TRSVR combines SVRG with trust-region for faster optimization.

Bayesian optimization algorithm reduces regret with efficient region pruning.

Proposes CPO framework for robust decision-making with explainable uncertainty regions.

Construction of tight confidence regions and intervals is central to statistical inference and decision making. This paper develops new theory showing minimum average volume confidence regions for categorical data. More precisely, consider an empirical distribution $\widehat{\boldsymbol{p}}$ generated from $n$ iid real…

Proposes a new algorithm for solving optimization problems with stochastic objectives and equality constraints.

This paper analyzes optimal stopping regions for American options with Poisson exercise opportunities.

Novel method for high-dimensional BO using CMA to define local regions.

Optimizes ellipsoids for uncertainty regions in parameter estimation.

We propose a trust region method for policy optimization that employs Quasi-Newton approximation for the Hessian, called Quasi-Newton Trust Region Policy Optimization QNTRPO. Gradient descent is the de facto algorithm for reinforcement learning tasks with continuous controls. The algorithm has achieved state-of-the-art…

A deep neural network (DNN) with piecewise linear activations can partition the input space into numerous small linear regions, where different linear functions are fitted. It is believed that the number of these regions represents the expressivity of the DNN. This paper provides a novel and meticulous perspective to l…

DO-IQS recovers optimal stopping region from expert trajectories, addressing specific challenges.

BALLET filters a high-confidence region of interest for Bayesian optimization.

Investigates consumption and investment strategies with preference for liquid assets.

Proposes a novel approach for cluster-aware matching using Laplacian Optimal Transport.

Combines global and local search for efficient global optimization with Gaussian processes.

Develops ODRPO to improve RL algorithms with better performance and stability.

PARADE generates provably robust adversarial examples for deep networks.

RYU framework constructs safe regions for optimization problems.

The paper analyzes and improves a deep learning optimization technique using matrix gradient orthogonality.

Recently dictionary screening has been proposed as an effective way to improve the computational efficiency of solving the lasso problem, which is one of the most commonly used method for learning sparse representations. To address today's ever increasing large dataset, effective screening relies on a tight region boun…

Inter-subject registration of cortical areas is necessary in functional imaging (fMRI) studies for making inferences about equivalent brain function across a population. However, many high-level visual brain areas are defined as peaks of functional contrasts whose cortical position is highly variable. As such, most ali…

Study on optimal portfolio selection with varying borrowing and saving rates in continuous-time markets.

A leveraged exchange traded fund (LETF) is an exchange traded fund that uses financial derivatives to amplify the price changes of a basket of goods. In this paper, we consider the robust hedging of European options on a LETF, finding model-free bounds on the price of these options. To obtain an upper bound, we establi…

New sampling strategy improves TR algorithms for stochastic optimization.

End-to-end pipeline for data-driven decision making in mixed-integer optimization.

The SPS method constructs confidence regions for true parameters with optimal sample complexity.

Proximal policy optimization (PPO) is one of the most popular deep reinforcement learning (RL) methods, achieving state-of-the-art performance across a wide range of challenging tasks. However, as a model-free RL method, the success of PPO relies heavily on the effectiveness of its exploratory policy search. In this pa…

MF-TRPO optimizes MFGs with finite sample guarantees.

TROLL improves RL for LLMs by replacing clipping with a trust region projection.

Adaptive replication improves stochastic function optimization.

BO limits search to lower dimensions with LGPR, improving efficiency and accuracy.

New algorithm improves deep learning stability with limited data.

The aim of black-box optimization is to optimize an objective function within the constraints of a given evaluation budget. In this problem, it is generally assumed that the computational cost for evaluating a point is large; thus, it is important to search efficiently with as low budget as possible. Bayesian optimizat…

Method constructs confidence regions for linear models with arbitrary predictors.

SLS optimizes minimum-volume regions for conditional quantiles, bypassing density estimation.

We propose to improve trust region policy search with normalizing flows policy. We illustrate that when the trust region is constructed by KL divergence constraints, normalizing flows policy generates samples far from the 'center' of the previous policy iterate, which potentially enables better exploration and helps av…

AntBO optimizes antibody design using Bayesian optimization for efficient and effective CDRH3 sequence generation.

SDG uses optimal control to improve classifier guidance in low-density regions.

The paper develops methods for constructing confidence regions for regression functions in binary classification.

Paper develops SKPD framework for signal region detection in image regression.

The study estimates the expressiveness of GCNs with bounds on the number of linear regions.