Genie optimizes search marketplaces by estimating policy impacts without risky experiments.
problem Optimizing search marketplaces with frequent policy changes and limited randomized experiments.
method Genie uses an open box simulation engine and click calibration model to estimate KPI impacts.
result Genie outperforms existing approaches in optimizing Bing Ads Marketplace.
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.
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.
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.
Survey connects XAI and surrogate modeling for better understanding of complex systems.
problem Opaque surrogate models hide input-output relationships, hindering decision-making.
method Synthesizes XAI techniques with surrogate modeling workflows.
result Surrogate models can be made more interpretable using XAI methods.
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.
Novel algorithm for optimal control of nonlinear systems.
problem Optimal control of nonlinear stochastic dynamical systems with unknown dynamics.
method Decoupled data-based approach combining open-loop and closed-loop control.
result Performance of D2C algorithm is approximately optimal and significantly reduces training time.
NUBO simplifies Bayesian optimization for researchers.
problem Optimizing expensive functions like experiments and simulations.
method Bayesian optimization using Gaussian processes and acquisition functions.
result Transparency and user-friendly design for easy access.
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.
End-to-end autonomous driving framework using guided auxiliary supervision.
problem Learning to drive in highly stochastic urban settings.
method Multi-task Learning from Demonstration (MT-LfD) framework with end-to-end trainable network and supervised auxiliary tasks.
result Joint learning and supervised guidance facilitate faster and better driving performance.
OpenViewer tackles multi-view learning challenges with interpretability and generalization.
problem Lack of interpretability and insufficient generalization in multi-view learning models.
method OpenViewer introduces a Pseudo-Unknown Sample Generation Mechanism, Expression-Enhanced Deep Unfolding Network, and Perception-Augmented Open-Set Training Regime.
result OpenViewer effectively addresses openness challenges and enhances recognition performance for both known and unknown samples.
Unity offers a flexible platform for AI research with rich simulations.
problem Limited configurability and complexity in existing simulation environments.
method Proposed a taxonomy of simulation platforms and demonstrated Unity's suitability.
result Unity enables rich, complex simulations for AI research.
AxNN improves model interpretability without sacrificing predictive power.
problem Black-box nature of machine learning models hinders interpretation and explanation.
method AxNN combines ensembles of generalized additive model networks and additive index models.
result AxNN achieves both good predictive performance and model interpretability.
New framework tests AVs as a black box, prioritizing rare failure modes.
problem Lack of rigorous and scalable testing methods for AVs.
method Developed a simulation testing framework that learns to identify and rank failure scenarios via adaptive importance-sampling methods.
result First independent evaluation of a full-stack commercial AV system (Comma AI's OpenPilot).
Researchers dissect Neural ODEs to understand their dynamics.
problem Understanding the inner workings of Neural ODEs.
method Developing continuous-depth formulation to clarify design choices.
result Clarified the influence of design choices on Neural ODE dynamics.
New method optimizes sensor placement for stochastic systems efficiently.
problem Optimizing sensor placements for black-box stochastic systems with computational constraints.
method Trains a joint energy-based model on simulation data to learn parameter and solution distributions, allowing efficient sensor placement.
result Demonstrates lower computational cost and more informative sensor locations compared to conventional approaches.
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.
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.
Divide-and-conquer framework speeds up black-box inference for large data.
problem Computational intractability of uncertainty quantification for expensive data simulation.
method Divide data into partitions, train on a subset, bootstrap on partitions, combine results.
result Feasibility of estimating max-stable process parameters with tens of thousands of locations.
Analyze and predict complex 3D shape deformations using LSTM autoencoders and oriented bounding boxes.
problem Detecting and predicting patterns in sequences of deforming 3D shapes.
method Use LSTM autoencoders to create low-dimensional representations of 3D shapes, incorporating oriented bounding boxes for structural components.
result The method detects patterns in plastic deformation and predicts future states of 3D shapes with improved accuracy.
New methods improve Bayesian inference for complex economic models.
problem Difficulty in parameter estimation for simulation models, especially in economics.
method Neural network-based black-box approximate Bayesian inference methods.
result Neural network methods provide state-of-the-art parameter inference for economic simulation models.
WS-KDE provides robust confidence bounds for stochastic functions.
problem Optimizing time-consuming black-box functions with stochastic outputs.
method Wilson Score Kernel Density Estimation (WS-KDE) for Bayesian optimization.
result WS-KDE provides reliable confidence bounds for any stochastic function.
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.
Differentially private ensemble classifiers adapt to data streams while protecting privacy.
problem Adapting to evolving data characteristics while protecting private information.
method Unbounded ensemble updates, model agnostic approach.
result Outperforms competitors on various privacy, drift, and distribution settings.
Scout-Nd optimizes parameters of stochastic simulators efficiently.
problem Optimizing parameters of stochastic, computationally expensive simulators.
method Scout-Nd algorithm, reducing gradient noise, multi-fidelity schemes.
result Demonstrates better performance compared to existing methods.
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.
Enhanced Elastic-Net with box-constraint improves support recovery in noisy measurements.
problem Support recovery of sparse signals from noisy measurements.
method Box-Elastic Net (Box-EN) method with mean squared error and probability of support recovery analysis.
result The Box-Elastic Net outperforms the standard Elastic-Net in support recovery.
New method optimizes expensive simulations for complex systems.
problem Optimizing complex systems with limited expensive experiments.
method Black-box Optimization via Marginal Means (BOMM) approach.
result BOMM improves optimization performance in high dimensions.
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.
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.
Study tackles discretization problem in crafting adversarial examples for discrete integer domains.
problem Discretization problem in crafting adversarial examples for discrete integer domains.
method Proposes a black-box method to reduce adversarial example searching to a derivative-free optimization problem.
result Significantly higher success rate in crafting adversarial images in discrete integer domain compared to black-box methods.
Reinforcement learning controls complex, black-boxed systems like greenhouses.
problem Controlling nonlinear, complex, and black-boxed systems.
method Actor-critic reinforcement learning approach.
result Successfully maintained greenhouse conditions for 20 times longer than other methods.
Bayesian optimisation tackles expensive black-box functions with constraints.
problem Optimizing constrained black-box functions in machine learning and simulation.
method Proposes a new Knowledge Gradient acquisition function for constrained Bayesian optimisation.
result Demonstrates superior performance over four state-of-the-art constrained Bayesian optimisation algorithms.
Paper proposes an efficient method for bounding box annotation in object detection.
problem Manual annotation of bounding boxes is tedious and resource-intensive.
method Iterative training of object detector on small batches of labeled images, with human annotator correcting errors.
result Significant reduction in human annotation effort, up to 75%.
New method interprets recurrent neural networks via excitable network attractors.
problem Understanding the inner workings of recurrent neural networks.
method Excitable network attractors for mechanistic interpretation.
result Recurrent neural networks' behavior can be interpreted using excitable network attractors.
In this work, we explore how probabilistic programs can be used to represent policies in sequential decision problems. In this formulation, a probabilistic program is a black-box stochastic simulator for both the problem domain and the agent. We relate classic policy gradient techniques to recently introduced black-box…
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.
Study volume growth and asymptotic cones of nonnegative Ricci curvature manifolds.
problem Whether the volume growth order of manifolds is greater than or equal to the dimension of their asymptotic cones.
method Analyzing asymptotic cones and volume growth conditions, extending Sormani's results.
result Existence of asymptotic cones with upper box dimension at most equal to the volume growth order.
New attacks reduce bad queries in black-box classifiers, improving effectiveness.
problem Evasion attacks often issue many bad queries, which are costly.
method Design new attacks that minimize bad queries while increasing non-bad queries.
result Reduces the number of bad queries by 1.5-7.3 times, improving attack effectiveness.
A tutorial on optimizing complex functions with partial knowledge.
problem Optimizing functions with limited or partial information.
method Grey-box Bayesian optimization, blending black-box and white-box approaches.
result Improves optimization performance by leveraging internal information.
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.
We prove that the group D^r(R) of C^r diffeomorphisms of the real line, endowed with the compact-open and Whitney C^r topologies, is bihomeomorphic to the group H(R) of homeomorphisms of the real line endowed with the compact-open and Whitney topologies. This implies that the diffeomorphism group D^r(R) endowed with th…
This work defines observation-specific explanations for black-box models.
problem Assigning importance to data points in black-box model predictions.
method Surrogate model construction using scattered data approximation and orthogonal matching pursuit.
result Validated approach on simulated and real-world datasets.
PRETZEL optimizes machine learning prediction serving systems for better performance.
problem Low latency, high throughput, and graceful performance degradation under heavy load in prediction serving systems.
method Introducing a novel white box architecture enabling both end-to-end and multi-model optimizations.
result Average 5.5x reduction in 99th percentile latency, 25x reduction in memory footprint, and 4.7x increase in throughput compared to state-of-the-art approaches.
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.
TreeHFD algorithm explains tree ensemble models through hierarchical orthogonality.
problem Difficulty in explaining black-box tree ensemble models.
method TreeHFD algorithm using hierarchical orthogonality constraints.
result TreeHFD estimates Hoeffding decomposition from data samples.
This paper explores causal analysis in machine learning for better interpretability.
problem The lack of causality in traditional interpretable machine learning models.
method An overview of causal approaches for interpretable machine learning.
result Causal analysis improves the interpretability of machine learning models.
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