Unified approach for sequence design combining likelihood-free inference and black-box optimization.
problem Designing biological sequences efficiently and accurately.
method Unified probabilistic framework integrating likelihood-free inference and black-box optimization.
result Previous optimization methods can be adapted and new algorithms proposed within this framework.
Bayesian optimization outperformed random search in machine learning hyperparameter tuning challenge.
problem Optimizing hyperparameters of machine learning models using derivative-free methods.
method Bayesian optimization vs. random search on real datasets.
result Bayesian optimization significantly outperformed random search in held-out objective functions.
New method improves black-box adversarial attacks with gradient priors.
problem Generating adversarial examples in a black-box setting.
method Bandit optimization-based algorithm with gradient priors.
result Improves black-box attacks by using gradient priors, reducing queries and failures.
A new method for optimizing black-box problems with constraints.
problem Optimizing black-box systems with multiple performance criteria and constraints.
method Developed a novel constrained Bayesian optimization approach based on the knowledge gradient method.
result A new acquisition function that balances optimality and feasibility.
ODS improves adversarial attacks by maximizing output diversity.
problem Efficiency and effectiveness of adversarial attacks, especially black-box attacks.
method Output Diversified Sampling (ODS) that maximizes diversity in model outputs.
result ODS reduces the number of queries needed for black-box attacks on ImageNet by a factor of two.
A new black-box optimizer using implicit natural gradient.
problem Efficient optimization for complex, computationally intensive problems.
method Stochastic update with implicit natural gradient of an exponential-family distribution.
result Theoretical convergence rate for convex functions and continuous non-differentiable functions.
RNN-Opt learns neural network optimizers for black-box functions.
problem Optimizing black-box functions with limited information.
method Training RNNs with a discounted regret loss function to optimize real-parameter functions.
result RNN-Opt outperforms existing methods on various black-box optimization tasks.
MALC combines interpretable linear models with black-box models for better predictions and transparency.
problem Combining interpretability with black-box models for better predictions.
method Formulates MALC as a convex optimization problem and uses accelerated proximal gradient method for training.
result MALC provides an efficient frontier balancing prediction accuracy and transparency.
New method optimizes BO by learning search spaces from past evaluations.
problem Optimizing expensive black-box functions efficiently.
method Automatically designs BO search space from historical data.
result Significant boost in BO performance by reducing search space size.
DiBO uses diffusion models to optimize high-dimensional black-box functions efficiently.
problem Optimizing high-dimensional and complex black-box functions efficiently.
method DiBO iterates two stages: training a diffusion model and casting candidate selection as posterior inference.
result DiBO outperforms state-of-the-art baselines across synthetic and real-world tasks.
We address challenges in collaborative black-box optimization through three frameworks.
problem Challenges in distributed experimentation, heterogeneity, and privacy in black-box optimization.
method Three unifying frameworks: global, local, and predictive.
result Shift from descriptive/predictive to prescriptive federated learning in black-box optimization.
Method optimizes diffusion model generation to meet user preferences.
problem Optimizing diffusion model generation with only black-box target scores.
method Covariance-adaptive sequential optimization algorithm for black-box optimization.
result Proves superior performance in achieving better target scores.
Paper advances black-box VI using flows and Monte-Carlo methods.
problem Improving automatic posterior inference in black-box VI.
method Combines normalizing flows, Monte-Carlo methods, and optimization considerations.
result Significant improvement in state-of-the-art variational inference.
New method optimizes black-box functions using generative models and Wasserstein distance.
problem Optimizing black-box functions with stochastic responses in high dimensions.
method Deep generative surrogate models and Wasserstein distance for uncertainty estimation.
result Method outperforms state-of-the-art methods in robustness to function shape and stochasticity.
P-BO reduces black-box adversarial attacks by 10x with Bayesian optimization and function prior.
problem Efficiently generating adversarial examples against black-box models.
method Prior-guided Bayesian Optimization (P-BO) with a function prior initialized from a surrogate model.
result Significantly reduces the number of queries needed for adversarial attacks.
LA-MCTS learns search space partition for black-box optimization using Monte Carlo Tree Search.
problem High-dimensional black-box optimization challenges.
method LA-MCTS recursively splits search space into regions with high/low function values, learns nonlinear partition and local models online.
result LA-MCTS achieves strong performance in black-box optimization and reinforcement learning benchmarks, especially for high-dimensional problems.
New black-box reductions simplify online learning algorithms.
problem Designing adaptive and parameter-free online learning algorithms.
method Introducing black-box reductions to simplify analysis and improve regret guarantees.
result Improved regret bounds for parameter-free learning.
Paper proposes a new Hessian-aware zeroth-order optimization for improving black-box adversarial attacks.
problem Improving black-box adversarial attacks on neural networks.
method Introduces a Hessian-aware zeroth-order optimization algorithm called ZO-HessAware.
result ZO-HessAware achieves improved success rates with lower query complexity.
New approach optimizes expensive black-box systems with uncertain outputs.
problem Optimizing expensive black-box systems with limited data and uncertainty.
method Flexible non-interpolating surrogate model (TK-MARS) and Smart-Replication approach.
result TK-MARS outperforms original MARS and detects important variables.
Paper tackles constrained robust optimization without gradients.
problem Constrained robust (min-max) optimization in a black-box setting.
method Integrates ZO gradient estimator with alternating projected SGD-ascent method.
result Proposed ZO-Min-Max framework converges sub-linearly under mild conditions.
Efficiently distills white-box adversarial attacks into black-box models.
problem Generating efficient adversarial examples for robustness.
method Train a model to emulate white-box attack behavior and distill it into a more efficient black-box model.
result Reduces adversarial example generation time by 19x-39x and transfers to black-box settings.
New guarantees for black-box variational inference methods.
problem Insufficient theoretical guarantees for black-box variational inference.
method Novel convergence guarantees for stochastic optimization of variational inference.
result Provable convergence of proximal and projected stochastic gradient descent for variational inference.
Optimal policy for early stopping improves black-box optimization efficiency.
problem Finding the best algorithm and hyperparameters for complex tasks.
method Bayesian approach to adaptively restart algorithms based on run-so-far features.
result Up to 13x improvement in expected time to reach target accuracy over random search.
Efficiently optimizes boolean functions using multilinear polynomials and exponential weight updates.
problem Optimizing boolean functions over the boolean hypercube with high computational cost.
method Proposes a computationally efficient algorithm using multilinear polynomials and exponential weight updates.
result Improves computational time up to several orders of magnitude compared to state-of-the-art algorithms.
Bayesian optimization tackles non-smooth tuning problems.
problem Optimizing black-box functions with non-smoothness and limited samples.
method Proposed a clustered Gaussian process (cGP) model for non-smooth optimization.
result Improvement of up to 90% in performance for repetitive experiments.
Efficient method for generating adversarial examples with limited query budget.
problem Developing black-box adversarial attacks with limited information.
method Bayesian Optimization in a structured low-dimensional subspace.
result Significantly higher attack success rate with fewer queries.
Optimizes target value in stochastic black box functions.
problem Finding input to minimize expected squared error to target value.
method Derives acquisition functions for expected improvement, probability of improvement, and lower confidence bound, assuming Gaussian aleatoric effects.
result Acquisition functions can outperform classical Bayesian optimization under certain conditions.
Paper finds optimal membership inference strategies for machine learning models.
problem Determining if a sample was part of the training set of a machine learning model.
method Derives optimal strategies for membership inference with assumptions on parameter distribution, showing that black-box attacks are as good as white-box attacks.
result Optimal strategies are not tractable, leading to approximations that outperform existing methods.
We use Bayesian optimization to create efficient adversarial attacks with limited queries.
problem Creating adversarial examples with limited query access.
method Bayesian optimization for query-efficient adversarial attacks.
result Our method reduces query count by up to 80% compared to state-of-the-art methods.
A new Frank-Wolfe framework improves efficiency and effectiveness of adversarial attacks.
problem Develop efficient and effective optimization-based adversarial attack algorithms.
method Proposes a Frank-Wolfe algorithm variant for both white-box and black-box adversarial attacks.
result Demonstrates improved efficiency and effectiveness compared to existing methods.
New method attacks black-box models with fewer queries.
problem Attacking machine learning models without model information.
method Formulated as a real-valued optimization problem, solved with zeroth order optimization.
result Demonstrated to outperform random walk approach on various datasets.
Efficient method reduces black-box adversarial queries for neural networks.
problem Solving for adversarial examples in black-box settings with limited query access.
method Efficient combinatorial optimization surrogate for gradient estimation.
result State-of-the-art black-box attack performance with reduced query count.
Optimize black-box simulators with local generative models.
problem Optimizing non-differentiable, stochastic simulators with intractable likelihoods.
method Differentiable local surrogate models based on deep generative models.
result Local surrogates enable gradient-based optimization, faster than baseline methods.
Adapts example weights to optimize black-box metrics.
problem Optimizing metrics defined by black-box functions.
method Adaptive example weighting and iterative post-shifting.
result Improves classification performance compared to baselines.
End-to-end training of neural networks with black-box functions.
problem Training neural networks for tasks that require precise, non-differentiable functions.
method Approximate black-box functions with differentiable neural networks and integrate them into neural network training.
result Neural network trained to compute inputs for black-box functions, generalizing better and learning more efficiently.
Optimal allocation between explainable and black box models for high performance and explainability.
problem Balancing explainability and performance in model ensembles.
method Optimal allocation of observations between explainable and black box models to maximize ensemble performance and explainability.
result Learned allocations maintain high ensemble performance and explainability, sometimes outperforming individual models.
A new AutoML framework uses ADMM to optimize machine learning pipeline configurations.
problem Optimizing machine learning pipelines with mixed integer and continuous variables.
method Leverages ADMM to decompose and solve the optimization problem.
result Significant gains in comparison to other AutoML frameworks.
Method reformulates constrained optimization as latent space inference.
problem Optimizing black-box functions with hard constraints.
method Posterior inference in latent space using flow-based models and diffusion models.
result Method achieves superior performance across various tasks.
New method improves black-box attacks using pre-trained models.
problem Efficiently attacking black-box models with limited information.
method EigenBA algorithm leveraging pre-trained white-box model's Jacobian matrix.
result Optimal perturbations are related to right singular vectors of Jacobian matrix.
P3BO optimizes biological sequence design by combining multiple methods.
problem Variability in performance of black-box optimization methods for biological sequence design.
method Population-Based Black-Box Optimization (P3BO) that samples sequences from an ensemble of methods, weighting by past performance.
result P3BO outperforms individual methods, proposing higher quality and more diverse sequences.
Adapts model-based advice to stabilize black-box policies for nonlinear control.
problem Stabilizing machine-learned policies for nonlinear control with limited model information.
method Proposes an adaptive λ-confident policy to combine black-box and model-based advice. result Proves the stability of the adaptive λ-confident policy and its competitive ratio. Tree-search algorithm optimizes noisy black-box functions with multi-fidelity queries.
problem Optimizing noisy functions with expensive evaluations and low-cost approximations.
method Tree-like hierarchical partitions and multi-fidelity bandit tree-search algorithm.
result Simple regret bounds for the proposed algorithm.
Unified framework for scalable black-box optimization.
problem Expensive black-box evaluations in scientific and engineering domains.
method Integrates active learning, multi-armed bandits, and distributed computing.
result Consistently outperforms state-of-the-art black-box optimizers.
A simple patch copying method reduces black-box adversarial attack queries by 81%.
problem The effectiveness of black-box adversarial attacks depends on the initialization method.
method Copying small patches from other images as initialization points.
result Reduces the number of queries required for a state-of-the-art Boundary Attack by 81%
TrustVI is a fast second-order algorithm for black-box variational inference.
problem Efficiently optimizing variational distributions in complex models.
method Trust-region optimization with minibatch reparameterization.
result TrustVI converges faster and finds better variational distributions than alternatives.
Enhances SVM interpretability by integrating data priors.
problem Lack of interpretability in black-box models.
method Integrates data-based priors into soft-margin SVM to enhance interpretability.
result Proposes an interpretable SVM optimization model and solves it as a nonlinear quadratic programming problem.
Adapts Bayesian optimization for mixed constraints in aircraft design.
problem Optimizing expensive black box functions with mixed constraints.
method Super efficient global optimization with upper trust bound for constraints, Gaussian process uncertainty, refinement procedure.
result Superior performance on aircraft design problem compared to state-of-the-art solvers.
TVR optimizes black-box simulators by targeting variance reduction over control and noise parameters.
problem Optimizing black-box simulators with uncertain parameters.
method Targeted Variance Reduction (TVR) method that optimizes (x,θ) jointly. result Improved robust optimization performance over state-of-the-art methods.