gradient-free optimization

Reliable Gradient-free and Likelihood-free Prompt Tuning EACL 2023 Survival of the Most Influential Prompts: Efficient Black-Box Prompt Search via Clustering and Pruning EMNLP 2023 Approaching Quartic Convergence Rates for Quasi-Stochastic Approximation with Application to Gradient-Free Optimization NeurIPS 2022 BBTv2: Towards a Gradient-Free Future with Large Language Models EMNLP 2022 Gradient-Free Methods for Deterministic and Stochastic Nonsmooth Nonconvex Optimization NeurIPS 2022 Learning Discrete Structured Variational Auto-Encoder using Natural Evolution Strategies ICLR 2022 TTOpt: A Maximum Volume Quantized Tensor Train-based Optimization and its Application to Reinforcement Learning NeurIPS 2022 ZARTS: On Zero-order Optimization for Neural Architecture Search NeurIPS 2022 Black-Box Detection of Backdoor Attacks With Limited Information and Data ICCV 2021 Learning a Gradient-free Riemannian Optimizer on Tangent Spaces AAAI 2021 Min-Max Optimization without Gradients: Convergence and Applications to Black-Box Evasion and Poisoning Attacks ICML 2020 Fast Efficient Hyperparameter Tuning for Policy Gradient Methods NeurIPS 2019 Faster Gradient-Free Proximal Stochastic Methods for Nonconvex Nonsmooth Optimization AAAI 2019 Improving Black-box Adversarial Attacks with a Transfer-based Prior NeurIPS 2019 Monte Carlo Tree Search for Policy Optimization IJCAI 2019 ZO-AdaMM: Zeroth-Order Adaptive Momentum Method for Black-Box Optimization NeurIPS 2019 Adversarial Risk and the Dangers of Evaluating Against Weak Attacks ICML 2018 Efficient Gradient-Free Variational Inference using Policy Search ICML 2018 Genetic-Gated Networks for Deep Reinforcement Learning NeurIPS 2018 Stein Variational Gradient Descent Without Gradient ICML 2018 Zeroth-Order Stochastic Variance Reduction for Nonconvex Optimization NeurIPS 2018 Gradient-free Policy Architecture Search and Adaptation CoRL 2017 Learning Supervised PageRank with Gradient-Based and Gradient-Free Optimization Methods NeurIPS 2016