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.
Zeroth-order optimization methods lack inherent privacy guarantees.
New method for zeroth-order stochastic gradient algorithms provides confidence intervals.
In this paper, we propose and analyze zeroth-order stochastic approximation algorithms for nonconvex and convex optimization, with a focus on addressing constrained optimization, high-dimensional setting and saddle-point avoiding. To handle constrained optimization, we first propose generalizations of the conditional g…
This paper reviews zeroth-order optimization in signal processing and machine learning.
Proximal gradient method has been playing an important role to solve many machine learning tasks, especially for the nonsmooth problems. However, in some machine learning problems such as the bandit model and the black-box learning problem, proximal gradient method could fail because the explicit gradients of these pro…
Improves zeroth-order optimization for private machine learning with public data.
A new method for MARL with partial observations reduces communication overhead.
Two algorithms solve nonconvex minimax problems with linear constraints, achieving complexity guarantees.
Discretizations of Langevin diffusions provide a powerful method for sampling and Bayesian inference. However, such discretizations require evaluation of the gradient of the potential function. In several real-world scenarios, obtaining gradient evaluations might either be computationally expensive, or simply impossibl…
New method improves zeroth-order stochastic optimization with adaptive sampling.
We consider the problem of optimizing a high-dimensional convex function using stochastic zeroth-order queries. Under sparsity assumptions on the gradients or function values, we present two algorithms: a successive component/feature selection algorithm and a noisy mirror descent algorithm using Lasso gradient estimate…
Book covers tools for zeroth-order convex optimisation.
Paper proposes ZO-NGD for more efficient black-box attacks.
Paper proposes algorithms for solving nonconvex-nonconcave problems with complexity guarantees.
ConMeZO speeds up zeroth-order optimization for large language models.
New algorithms solve complex minimax problems without needing derivatives.
We propose a method for zeroth order stochastic convex optimization that attains the suboptimality rate of after queries for a convex bounded function . The method is based on a random walk (the \emph{Ball Walk}) on the epigraph of the function. Th…
Alternating direction method of multipliers (ADMM) is a popular optimization tool for the composite and constrained problems in machine learning. However, in many machine learning problems such as black-box attacks and bandit feedback, ADMM could fail because the explicit gradients of these problems are difficult or in…
Paper tackles gradient-free minimax optimization with variance reduction for faster convergence.
New algorithm reduces dimensionality in stochastic optimization.
Zeroth-order methods favor flat minima in machine learning.
This paper analyzes and guarantees convergence of prior-guided ZO algorithms.
In this paper, we design and analyze a new zeroth-order online algorithm, namely, the zeroth-order online alternating direction method of multipliers (ZOO-ADMM), which enjoys dual advantages of being gradient-free operation and employing the ADMM to accommodate complex structured regularizers. Compared to the first-ord…
In this paper, we study zeroth-order algorithms for minimax optimization problems that are nonconvex in one variable and strongly-concave in the other variable. Such minimax optimization problems have attracted significant attention lately due to their applications in modern machine learning tasks. We first consider a …
LAZO reduces query complexity and variance in ZO methods.
ZDPG learns model-free policies without critics, improving on PG.
New optimization method improves generalization across various tasks.
ZOSPI improves RL policies with global value function exploitation.
Two types of zeroth-order stochastic algorithms have recently been designed for nonconvex optimization respectively based on the first-order techniques SVRG and SARAH/SPIDER. This paper addresses several important issues that are still open in these methods. First, all existing SVRG-type zeroth-order algorithms suffer …
A new method for optimizing functions without gradients, improving efficiency and convergence.
Sparse perturbations improve convergence in SZO methods for faster training.
New adaptive methods solve weakly convex stochastic optimization problems.
As application demands for zeroth-order (gradient-free) optimization accelerate, the need for variance reduced and faster converging approaches is also intensifying. This paper addresses these challenges by presenting: a) a comprehensive theoretical analysis of variance reduced zeroth-order (ZO) optimization, b) a nove…
LORENZA improves LLM fine-tuning efficiency and generalization.
DPZero fine-tunes large models privately without backpropagation.
Optimal algorithms for Riemannian optimization with reduced complexity.
MAZE attacks a model without data, using synthetic inputs.
Zeroth-order (a.k.a, derivative-free) methods are a class of effective optimization methods for solving complex machine learning problems, where gradients of the objective functions are not available or computationally prohibitive. Recently, although many zeroth-order methods have been developed, these approaches still…
Paper develops methods to optimize policies directly from human feedback without reward inference.
A new hybrid-ordered SGD method reduces communication and complexity for non-convex optimization.
In the learning to learn (L2L) framework, we cast the design of optimization algorithms as a machine learning problem and use deep neural networks to learn the update rules. In this paper, we extend the L2L framework to zeroth-order (ZO) optimization setting, where no explicit gradient information is available. Our lea…
New method estimates Riemannian derivatives from noisy function evaluations.
New analysis for black-box learning without gradients, improving generalization bounds.
Stochastic zeroth-order (SZO), or gradient-free, optimization allows to optimize arbitrary functions by relying only on function evaluations under parameter perturbations, however, the iteration complexity of SZO methods suffers a factor proportional to the dimensionality of the perturbed function. We show that in scen…
Zeroth-order optimization is an important research topic in machine learning. In recent years, it has become a key tool in black-box adversarial attack to neural network based image classifiers. However, existing zeroth-order optimization algorithms rarely extract second-order information of the model function. In this…
We consider the problem of global optimization of an unknown non-convex smooth function with zeroth-order feedback. In this setup, an algorithm is allowed to adaptively query the underlying function at different locations and receives noisy evaluations of function values at the queried points (i.e. the algorithm has ac…
The adaptive momentum method (AdaMM), which uses past gradients to update descent directions and learning rates simultaneously, has become one of the most popular first-order optimization methods for solving machine learning problems. However, AdaMM is not suited for solving black-box optimization problems, where expli…
In this paper, we propose a new technique named \textit{Stochastic Path-Integrated Differential EstimatoR} (SPIDER), which can be used to track many deterministic quantities of interest with significantly reduced computational cost. We apply SPIDER to two tasks, namely the stochastic first-order and zeroth-order method…