Extracts weighted automata from black box models for sequential data.
problem Global interpretability of black box models for symbolic sequential data.
method Spectral algorithm for extracting weighted automata from black boxes without access to inner representation.
result Approximation of black box models using inferred weighted automata is of high quality.
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.
Simple method for black-box adversarial attacks with low query efficiency.
problem Efficient construction of adversarial images in the black-box setting.
method Randomly sampling from an orthonormal basis and adjusting images.
result Demonstrated unprecedented query efficiency in both untargeted and targeted attacks.
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.
New symmetries improve meta-reinforcement learning's generalization.
problem Black-box meta reinforcement learning struggles with generalization.
method Introduce symmetries to a black-box meta RL system.
result Incorporating symmetries improves generalization to new environments.
New analysis improves black-box k-PCA algorithms, reducing parameter loss.
problem Designing efficient k-PCA algorithms with black-box access to a 1-PCA oracle. method Black-box deflation methods, analyzing ePCA and cPCA approximations.
result Deflation methods suffer no asymptotic parameter loss for k-cPCA in feasible regimes. 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.
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.
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.
New approach generates black-box attacks on neural networks using swarm evolutionary algorithm.
problem Adversarial samples attack neural networks.
method Swarm evolutionary algorithm for black-box attacks.
result 100% success rate on MNIST and CIFAR-10 images.
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.
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.
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.
Paper introduces DNTs to clone black-box models efficiently.
problem Cloning functionality of black-box models.
method Deep Neural Trees (DNTs) trained with active learning.
result Trained DNT can clone task-specific behavior of black-box models.
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.
New action poisoning attacks improve LinUCB's performance by changing action signals.
problem Improving understanding of adversarial attacks on contextual bandit algorithms.
method Proposed action poisoning attacks in white-box and black-box settings.
result Action poisoning attacks can force LinUCB to pull a target arm frequently with low cost.
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.
Hybrid model combines interpretable and black-box models for better transparency and performance.
problem Balancing interpretability and predictive performance in machine learning models.
method Proposes a Hybrid Predictive Model (HPM) integrating an interpretable model with a black-box model, using principled objective functions and customized training algorithms.
result Hybrid models achieve an efficient trade-off between transparency and predictive performance.
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.
Stabilizing black-box algorithms through task-oriented randomization
problem Ensuring stability of black-box models
method Task-oriented randomization
result Established rigorous theoretical foundations and demonstrated effectiveness through simulations and real-world applications
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.
New algorithm sets record for model evasion with minimal queries.
problem Evasion of machine learning models in black-box settings.
method Sign-based gradient estimation for binary black-box optimization.
result Achieves highest evasion rates and lowest query efficiency.
Paper proposes a new black-box attack method for deep neural networks.
problem Understanding and defending against adversarial attacks on deep neural networks.
method The algorithm finds a probability distribution over a region around the input, making a sample likely an adversarial example.
result The method outperforms state-of-the-art black-box or white-box attack methods for most test cases.
Efficiently controls unknown linear systems with black-box interactions.
problem Controlling an unknown linear dynamical system from black-box interactions.
method First efficient algorithm with sublinear regret, using robust system identification.
result Resolves open problem on stochastic LQR and black-box LQR control.
Black-box adversarial examples improve ASR system accuracy.
problem Improving ASR system accuracy through targeted adversarial examples.
method Combining genetic algorithms and gradient estimation for black-box attacks.
result Achieved 89.25% targeted attack similarity with 94.6% audio file similarity.
Paper presents strong black-box attacks on unsupervised clustering models.
problem Adversarial attacks on unsupervised machine learning models.
method Iterative black-box attack on clustering algorithms.
result Adversarial samples can fool clustering algorithms irrespective of the algorithm used.
New black-box attack method improves GNN defense without needing training data.
problem Vulnerability of Graph Neural Networks to adversarial attacks.
method Developed a gradient-based black-box attack algorithm, BBGA, which does not require access to training data.
result BBGA achieves stable attack performance without accessing training sets, and is effective against various defenses.
Improves black-box adversarial attacks with a transfer-based prior.
problem Low attack success rates and poor query efficiency in black-box adversarial attacks.
method P-RGF method that integrates a transfer-based prior and query information.
result Significantly reduces the number of queries needed for successful attacks.
New algorithms improve on consistency and robustness in convex function chasing with black-box advice.
problem Minimizing cost in normed vector space with black-box advice for convex function chasing.
method Two novel algorithms: INTERP and BDINTERP, exploiting convexity to achieve improved consistency and robustness.
result BDINTERP achieves near-optimal consistency-robustness trade-off for α-polyhedral cost functions.
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.
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.
ALFI improves likelihood-free inference for black-box generators.
problem Limitations of likelihood-free inference on black-box generators.
method Adversarial Likelihood-Free Inference (ALFI) to estimate posterior distributions.
result ALFI achieves best parameter estimation accuracy with limited simulation.
Randomizing model outputs confuses black box adversarial attacks.
problem Defending against black box adversarial attacks in deep neural networks.
method Randomization applied to model outputs to confuse attackers.
result Randomization can bound the probability of introducing errors, thwarting black box attacks.
New method improves HPO interpretability without sacrificing performance.
problem Difficulty in understanding HPO algorithms due to black-box nature.
method Coupling Bayesian optimization with Bayesian Algorithm Execution.
result More reliable IML explanations without compromising optimization performance.
HyRS model achieves transparency without sacrificing predictive performance.
problem Achieving interpretability in black-box models with good predictive power.
method HyRS model using decision rules for interpretable subspace, efficient search algorithm for optimal model selection.
result HyRS model obtains effective trade-off between transparency and interpretability.
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.
Develops black-box methods to estimate parameters of complex models.
problem Lack of efficient methods to produce simulations for complex statistical models.
method Pre-training deep neural networks on extensive simulated databases for well-structured likelihoods. Iterative algorithm for other complex dependencies.
result Successfully estimates and quantifies uncertainty of parameters from non-Gaussian models.
New method generates reliable adversarial examples with fewer queries.
problem Neural networks are vulnerable to adversarial examples in black-box settings.
method Natural evolution strategies and targeted attacks in partial information settings.
result Successfully performed targeted adversarial attack on Google Cloud Vision API.
Bayesian Algorithm Execution uses mutual information to infer properties of black-box functions efficiently.
problem Estimating computable properties of expensive black-box functions with limited evaluations.
method Sequentially choosing queries that maximize mutual information with respect to the algorithm's output.
result InfoBAX reduces query counts by up to 500 times compared to the original algorithm.
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.
New framework converts offline to online estimation using black-box offline estimators.
problem Convert offline estimation algorithms to online estimation algorithms.
method Oracle-Efficient Online Estimation (OEOE) framework.
result Achieves near-optimal online estimation error via black-box offline estimators.
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.
New approach turns optimal stationary RL into non-stationary RL without prior knowledge.
problem Optimal RL in non-stationary environments without prior knowledge of non-stationarity.
method Black-box reduction of optimal stationary RL algorithms to non-stationary RL.
result Achieves optimal dynamic regret bounds in various RL settings.
A theory for interpreting black-box models in medical diagnostics.
problem Lack of computational formulation for interpreting black-box models in medical diagnostics.
method Defining interpretation as a finite communication between a known model and a black-box model, deriving an algorithm for diagnostic interpretability.
result Demonstrated the feasibility of interpreting black-box models in synthetic supervised classification scenarios.
Optimum-statistical collaboration improves black-box optimization efficiency.
problem Improving black-box optimization efficiency through better statistical collaboration.
method Introducing optimum-statistical collaboration framework for hierarchical bandits-based optimization.
result Demonstrated improved regret bounds and better performance in experiments.
Fairwashing occurs when machine learning models are made to appear fair through rationalization.
problem Rationalizing unfair black-box models to appear fair.
method LaundryML uses a regularized rule list enumeration algorithm to find fair rule lists approximating an unfair model.
result It is possible to systematically rationalize decisions from unfair black-box models using model and outcome explanations.
AdversarialPSO uses fewer queries to create high-success-rate adversarial examples.
problem Creating imperceptible adversarial examples with high success rates in black-box settings.
method AdversarialPSO, a black-box attack using Particle Swarm Optimization.
result Achieved high success rates (99.6% on CIFAR-10, 96.3% on MNIST, 82.0% on Imagenet) with fewer queries.