Develops significance tests for neural networks without strong assumptions or excessive computation.
problem Addressing the black-box nature of deep neural networks for feature relevance testing.
method Derives one-split and two-split tests relaxing assumptions and computational complexity.
result Establishes asymptotic null distributions and consistency in Type II error.
Paper proposes a black-box technique to generate adversarial samples.
problem Robustness of Deep Neural Networks (DNNs) to adversarial samples.
method Black-box Momentum Iterative Fast Gradient Sign Method (BMI-FGSM) using Differential Evolution to approximate gradients.
result Achieves high success rates in generating adversarial samples and misclassification.
Black-box risk scoring models permeate our lives, yet are typically proprietary or opaque. We propose Distill-and-Compare, a model distillation and comparison approach to audit such models. To gain insight into black-box models, we treat them as teachers, training transparent student models to mimic the risk scores ass…
Black-box optimization is primarily important for many compute-intensive applications, including reinforcement learning (RL), robot control, etc. This paper presents a novel theoretical framework for black-box optimization, in which our method performs stochastic update with the implicit natural gradient of an exponent…
New approach tackles non-stationary multi-agent games with black-box methods.
problem Challenges in learning equilibria in non-stationary multi-agent systems.
method Versatile black-box approach applicable to various games, including general-sum, potential, and Markov games.
result Achieves optimal regret bounds for non-stationary games, with or without knowledge of total variation.
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.
Faced with distribution shift between training and test set, we wish to detect and quantify the shift, and to correct our classifiers without test set labels. Motivated by medical diagnosis, where diseases (targets) cause symptoms (observations), we focus on label shift, where the label marginal p(y) changes but the …
Deep-PrAE improves rare-event simulation for black-box systems.
problem Evaluating rare safety-critical events in learning-based systems.
method Combines deep neural networks with IS to create statistically guaranteed estimations.
result Deep-PrAE provides accurate bounds on safety-critical event probabilities.
Interpretable machine learning has become a strong competitor for traditional black-box models. However, the possible loss of the predictive performance for gaining interpretability is often inevitable, putting practitioners in a dilemma of choosing between high accuracy (black-box models) and interpretability (interpr…
REST improves robustness of black-box models to geometric transformations.
problem Overconfident incorrect predictions on out-of-distribution samples.
method REinforcement Spatial Transform learner (REST) that transforms input data into in-distribution samples.
result Improves robustness to geometric transformations and sample efficiency.
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.
Powerful adversarial attack methods are vital for understanding how to construct robust deep neural networks (DNNs) and for thoroughly testing defense techniques. In this paper, we propose a black-box adversarial attack algorithm that can defeat both vanilla DNNs and those generated by various defense techniques develo…
Hybrid neural network infers states from black-box systems.
problem Inference of state models in black-box systems without source code instrumentation.
method Convolutional and recurrent layers learn correlations between time series signals.
result Improves state change point detection by up to 102%.
Analyzing large-scale, multi-experiment studies requires scientists to test each experimental outcome for statistical significance and then assess the results as a whole. We present Black Box FDR (BB-FDR), an empirical-Bayes method for analyzing multi-experiment studies when many covariates are gathered per experiment.…
Deep neural networks (DNNs) are known for their vulnerability to adversarial examples. These are examples that have undergone small, carefully crafted perturbations, and which can easily fool a DNN into making misclassifications at test time. Thus far, the field of adversarial research has mainly focused on image model…
Develops causal explanations for black-box classifiers.
problem Creating understandable explanations for complex models.
method Generative model with information-theoretic measures of causal influence.
result Learned latent factors influence classifier outputs.
Survey of algorithms for testing AI-driven CPS safety.
problem Testing AI-driven CPS for safety in complex environments.
method Survey of applied algorithms for safety validation.
result Survey of existing tools and techniques for safety validation.
This paper explores how the generalization of substitute classifiers affects the success of black-box adversarial attacks.
problem Understanding the factors driving the transferability of black-box adversarial examples.
method Max-min adversarial example game framework and theoretical generalization bounds.
result Substitute NN with better generalization behavior results in more transferable adversarial examples.
Proposes a method to ensure accurate estimation of rare events in AI systems.
problem Lack of efficiency guarantees in black-box systems for rare-event simulation.
method Integrates deep learning with importance sampling to create a statistically guaranteed estimator.
result Demonstrates effective estimation of rare-event probabilities in AI systems.
Trieste optimizes black-box functions using TensorFlow for efficient decision-making.
problem Optimizing complex, black-box functions efficiently.
method Bayesian optimization and active learning with TensorFlow integration.
result Trieste enables efficient exploration of black-box functions.
In science and medicine, model interpretations may be reported as discoveries of natural phenomena or used to guide patient treatments. In such high-stakes tasks, false discoveries may lead investigators astray. These applications would therefore benefit from control over the finite-sample error rate of interpretations…
Develops a method to continuously audit black-box conditional quantile forecasts.
problem Continuous monitoring of black-box forecasts under changing data streams and regimes.
method Distribution-free and game-theoretic testing framework for non-i.i.d. losses.
result Derives finite-time detection guarantees for miscalibrated forecasts based on features.
Recently, neural networks trained as optimizers under the "learning to learn" or meta-learning framework have been shown to be effective for a broad range of optimization tasks including derivative-free black-box function optimization. Recurrent neural networks (RNNs) trained to optimize a diverse set of synthetic non-…
Spanning attack improves black-box attacks with unlabeled data.
problem Query inefficiency in black-box attacks due to high input space dimensionality.
method Proposes spanning attack by constraining adversarial perturbations in a low-dimensional subspace via an auxiliary unlabeled dataset.
result Significantly improves query efficiency of black-box attacks.
New research shows many recent defenses against adversarial examples are ineffective against black-box attacks.
problem The robustness of recent defenses against adversarial examples is insufficient, especially against black-box attacks.
method Evaluation of nine defenses on two black-box adversarial models and six attacks on CIFAR-10 and Fashion-MNIST datasets.
result Most recent defenses provide only marginal improvements in security (<25%) compared to undefended networks. Model proposes how regulators should oversee complex algorithms in high-stakes applications.
problem Regulating complex algorithms used in high-stakes applications like lending, testing, and hiring.
method Proposes a model where regulators are limited in learning about complex algorithms with misaligned preferences, and explores different regulatory approaches.
result Complex algorithms can improve welfare, but regulation should focus on the source of incentive misalignment for optimal results.
Machine Learning systems are vulnerable to adversarial attacks and will highly likely produce incorrect outputs under these attacks. There are white-box and black-box attacks regarding to adversary's access level to the victim learning algorithm. To defend the learning systems from these attacks, existing methods in th…
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.
The paper tests semantic importance in opaque models using betting.
problem Precise statistical guarantees for semantic concepts in black-box models.
method Formalizes global and local statistical importance via conditional independence and SKIT.
result Shows effectiveness and flexibility of the framework on various models.
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.
NP-Attack reduces query counts for black-box adversarial attacks.
problem Efficiency of black-box adversarial attacks is low.
method Uses Neural Process to characterize image structure and find adversarial examples.
result NP-Attack significantly decreases query counts.
MPHD transfers knowledge across different domains for Bayesian optimization.
problem Optimizing functions with unknown or diverse domains.
method MPHD uses neural nets to map domain-specific contexts to GP specifications, enabling transfer learning across heterogeneous search spaces.
result MPHD improves black-box function optimization performance on diverse domains.
Black box machine learning models are currently being used for high stakes decision-making throughout society, causing problems throughout healthcare, criminal justice, and in other domains. People have hoped that creating methods for explaining these black box models will alleviate some of these problems, but trying t…
Optimizing expensive black-box systems with limited data is an extremely challenging problem. As a resolution, we present a new surrogate optimization approach by addressing two gaps in prior research -- unimportant input variables and inefficient treatment of uncertainty associated with the black-box output. We first …
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.
Interpretable companion model for black-box classifiers.
problem Dilemma between interpretable and black-box models.
method Trains a companion model from data and black-box model predictions, optimizing a combination of accuracy and complexity.
result Companion model provides interpretable predictions with a slight accuracy loss for user choice.
Understanding how a learned black box works is of crucial interest for the future of Machine Learning. In this paper, we pioneer the question of the global interpretability of learned black box models that assign numerical values to symbolic sequential data. To tackle that task, we propose a spectral algorithm for the …
Most of the work on interpretable machine learning has focused on designing either inherently interpretable models, which typically trade-off accuracy for interpretability, or post-hoc explanation systems, which lack guarantees about their explanation quality. We propose an alternative to these approaches by directly r…
Paper proposes a new black-box attack approach to minimize visual distortion.
problem Constructing adversarial examples that minimize visual distortion in a black-box threat model.
method Learning the noise distribution of adversarial examples to approximate the gradient of a non-differentiable loss function.
result The proposed attack results in much lower visual distortion compared to state-of-the-art black-box attacks.
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. Deep neural networks work well at approximating complicated functions when provided with data and trained by gradient descent methods. At the same time, there is a vast amount of existing functions that programmatically solve different tasks in a precise manner eliminating the need for training. In many cases, it is po…
While autonomous vehicle (AV) technology has shown substantial progress, we still lack tools for rigorous and scalable testing. Real-world testing, the de-facto evaluation method, is dangerous to the public. Moreover, due to the rare nature of failures, billions of miles of driving are needed to statisticall…
New framework for variational coresets simplifies Bayesian inference for complex models.
problem Efficient Bayesian inference for complex models like neural networks.
method Black-box variational inference for coresets that handle intractable posterior distributions.
result Principled application of variational coresets to Bayesian neural networks.
PyBADS optimizes complex functions quickly and reliably.
problem Optimizing rough, noisy, and expensive functions with unknown gradients.
method Bayesian Adaptive Direct Search (BADS) algorithm.
result PyBADS performs well on both artificial and real-world problems.
Sparse perturbations improve convergence in SZO methods for faster training.
problem Dependency of SZO methods on function dimensionality limits their convergence speed.
method Sparse perturbations reduce the effective dimensionality of the optimization problem.
result Sparse SZO optimization leads to faster convergence in training loss and test accuracy.
Many deployed learned models are black boxes: given input, returns output. Internal information about the model, such as the architecture, optimisation procedure, or training data, is not disclosed explicitly as it might contain proprietary information or make the system more vulnerable. This work shows that such attri…
Bayesian optimisation generates saliency maps for black-box models.
problem Generating saliency maps for models without access to parameters.
method Bayesian optimisation sampling method to find global salient regions.
result Approach outperforms grid-based methods and performs similarly to gradient-based methods.
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.