DONE algorithm optimizes unknown functions with noisy measurements.
problem Online optimization of unknown functions with costly and noisy measurements.
method Uses a random Fourier expansion to maintain a surrogate function and iteratively update it with new measurements.
result DONE algorithm is significantly faster than Bayesian optimization while achieving similar or better performance.
Regulations impose idiosyncratic capital and funding costs for holding derivatives. Capital requirements are costly because derivatives desks are risky businesses; funding is costly in part because regulations increase the minimum funding tenor. Idiosyncratic costs mean no single measure makes derivatives martingales f…
Chameleon optimizes neural network compilation for faster execution and shorter time.
problem Faster execution and shorter compilation time for neural networks.
method Adaptive code optimization using reinforcement learning and adaptive sampling.
result 4.45x speed up in optimization time over AutoTVM, 5.6% improvement in inference time.
Financial markets are not random, but hard to predict due to hidden causes and strategic use.
problem Hard to predict financial markets
method Disciplined thesis on the distinction between no-arbitrage, informational efficiency, and net exploitability
result Discovers that markets are hard to predict due to hidden causes and strategic use
A method extracts binary features directly from CS measurements for compressive image classification.
problem Efficiently classify images using compressive sensing without reconstruction.
method DCT-based approach for binary feature extraction from CS measurements, feature fusion with CNN features.
result Fused features outperform state-of-the-art methods in image classification.
New algorithms reduce costly feature collection in bandits.
problem Costly feature collection in contextual bandits.
method Proposes algorithms avoiding unnecessary feature collection.
result Strong regret guarantees maintained with reduced feature collection.
Efficiently computes optimal policies for Entropic Risk Measures.
problem Optimizing risk-sensitive metrics in MDPs is computationally expensive.
method Uses Entropic Risk Measures and novel structural analysis for efficient computation.
result Achieves strong performance in various decision-making scenarios.
Active learning optimizes correlation clustering by querying the most informative pairwise comparisons.
problem Efficiently clustering data with limited pairwise similarity information.
method Developed principled active learning approach using information-theoretic acquisition functions.
result Significantly outperforms existing baselines in clustering accuracy and query efficiency.
Agent decides when to measure latent states in RL to improve efficiency.
problem Costly state measurement in RL negatively affects future outcomes.
method Introduces AOMDP with measurement action, uses online RL and sequential Monte Carlo.
result Reduced uncertainty improves sample efficiency and policy value.
Optimal sampling strategy improves prediction accuracy with surrogate variables under measurement constraints.
problem Measurement-constrained datasets and lack of labeled data.
method A-optimality criterion for optimal sampling, leveraging surrogate variables.
result Achieves lower asymptotic variance and reduced empirical mean squared error.
The paper tackles online decision making with costly information acquisition.
problem Collecting useful information is costly and requires active decision-making.
method Proposes two algorithms, Sim-OOS and Seq-OOS, for simultaneous and sequential observation making.
result Both algorithms achieve sublinear regret in time.
Improves signal recovery speed and performance via meta-learning.
problem Slow and costly signal recovery in compressed sensing.
method Jointly trains a generator and optimisation process for reconstruction via meta-learning.
result Significantly improved both performance and speed of signal recovery.
ConvMMD improves inference in noisy data.
problem Inference degradation due to measurement error in noisy data.
method Convolutional Maximum Mean Discrepancy (convMMD) for inference with noisy, heteroscedastic observations.
result Established consistency and asymptotic normality of the convMMD-based estimator.
Develops a method to find costly high-confidence errors in black box models.
problem Finding rare high-confidence errors missed by random sampling.
method Adversarial perturbation-guided search technique to find errors at rates greater than expected given model confidence.
result Our Adversarial Distance search discovers high-confidence errors at a rate greater than expected given model confidence.
This paper develops efficient bounds on the Wasserstein metric for discrete measures.
problem Computing the exact Wasserstein metric is computationally expensive.
method Formulates and solves a Kantorovich problem on a coarse grid using quantized measures and cost matrices, followed by upscaling and correction.
result Achieves a 10x-100x speedup while maintaining low approximation error.
Optimizes data labeling for causal effect estimation with missing outcomes.
problem Estimating causal effects with missing outcome data and budget constraints.
method Optimizes batch sampling probability to minimize variance of causal inference estimator.
result Achieves lower mean-squared error with fewer labeled data points.
Efficient methods estimate concordance probability for big data.
problem Efficiently calculating concordance probability in large datasets.
method Proposes two estimation methods for discrete and continuous settings.
result Estimators are accurate and computationally efficient.
Measures policy-violating content prevalence with ML-assisted sampling and LLM labeling.
problem Accurate measurement of content violations that are often rare and costly to label.
method Design-based measurement system using ML-assisted probability sampling and LLM labeling.
result Produces unbiased prevalence estimates with confidence intervals and dashboard drilldowns.
Bayesian approach improves Shapley value estimation efficiency.
problem Efficiently estimating Shapley values in machine learning models.
method Bayesian experimental design using Gaussian process surrogate and adaptive coalition selection.
result Consistently improves sample efficiency in low-budget settings.
We present new algorithms to compute the mean of a set of empirical probability measures under the optimal transport metric. This mean, known as the Wasserstein barycenter, is the measure that minimizes the sum of its Wasserstein distances to each element in that set. We propose two original algorithms to compute Wasse…
IoT nodes compress measurements into DNN outputs for efficient communication.
problem Efficient communication of high-dimensional IoT data with limited bandwidth.
method Modeling IoT node measurements as DNN intermediate outputs and optimizing model parameters.
result Approximately 96% reduction in transmissions with only 2.5% loss in inference accuracy.
Paper proposes efficient algorithms for bandit problems with costly sampling.
problem Maximizing expectation function over a finite set with high sampling cost.
method Proposes naive and adaptive stochastic bandit algorithms for PAC solution.
result Adaptive algorithm outperforms naive in terms of number of samples.
Bayesian Deep Learning tackles inverse problems with neural networks and approximate computations.
problem Solving inverse problems with indirect measurements and uncertainties.
method Bayesian Deep Learning, using neural networks and approximate computations.
result Effective solutions for inverse problems using Bayesian Deep Learning.
Paper develops a loss function using Wasserstein distance for multi-label prediction.
problem Challenges in learning multi-label outputs with a natural metric.
method Develops a loss function based on Wasserstein distance, regularized for efficiency.
result Wasserstein loss encourages smoothness of predictions with respect to a chosen metric.
New predictive bandit model with noise for better decision making.
problem Optimizing decisions with limited information and noise.
method Introduced predictive bandits, derived regret bounds, and developed algorithms.
result Developed algorithms matching theoretical regret bounds.
The new SMA for operational risk is criticized for not adequately measuring risk or linking management actions.
problem The new SMA for operational risk does not adequately measure risk or link management actions.
method The behavior of the SMA was studied under various conditions, showing its limitations.
result The SMA does not respond appropriately to changes in risk profile or differentiate among risk profiles.
Study proposes an active subsampling method for estimating individualized thresholds in high-dimensional data.
problem Estimating optimal individualized thresholds in high-dimensional data with limited labeled samples.
method Developed a K-step active subsampling algorithm to iteratively select and label the most informative data points.
result Revealed a phase transition phenomenon in the estimation of θ with respect to the smoothness of the conditional density. Simplified equation predicts model sensitivity to data.
problem Understanding model sensitivity to training data is challenging and costly.
method Derived using Bayesian principles, the Memory-Perturbation Equation (MPE) unifies and generalizes existing sensitivity measures.
result Empirical results show sensitivity estimates during training can predict generalization on unseen test data.
Bayesian optimization tackles mixed discrete-continuous problems with Gaussian processes.
problem Optimizing problems with both discrete and continuous variables using costly simulations.
method Relaxing discrete variables into continuous latent variables, using Bayesian optimization, and incorporating compatibility constraints with Lagrangians.
result Comparative analysis of different mixed Bayesian optimization approaches.
This work tackles costly feature acquisition for classification, using RL.
problem Classification with costly feature acquisition.
method Formulated as a sequential decision-making problem (MDP) and solved with deep reinforcement learning.
result Robust performance across various datasets and settings, outperforming prior-art.
Enhances influence functions for deep models without costly Hessian inversion.
problem Challenges in computing Hessian matrix inverse for deep models.
method Substitutes Hessian inverse with identity matrix and extends applications.
result TracIn method performs well in measuring model utility and fairness.
Fair active learning selects data points to balance model accuracy and fairness.
problem Ensuring fairness in machine learning models used in high-stakes applications.
method Designing algorithms for fair active learning that select data points to balance model accuracy and fairness, focusing on demographic parity.
result Demonstrated the effectiveness of the proposed fair active learning approach over benchmark datasets.
Algorithm optimizes measurement sequence to minimize data acquisition.
problem Efficiently measure high-dimensional data with minimal measurements.
method Active sequential inference using variational autoencoder (VAE) latent space.
result Optimal measurement sequences chosen to recover high-dimensional data.
Novel method reduces costly model evaluations in inference problems.
problem Efficiently approximating complex, costly model integrals.
method Compressed Monte Carlo (CMC) scheme for selecting model evaluations.
result Empirical evidence of method's performance in astronomy and remote sensing.
Improved classification with costly features using deep reinforcement learning.
problem Optimizing classification error with limited and costly feature acquisition.
method Revisited Q-learning approach with neural network approximation for sequential feature requests and classification decisions.
result Deep reinforcement learning approach comparable to state-of-the-art algorithms, robust across datasets.
New method estimates MTF from photos without expensive equipment.
problem Costly MTF measurement limits lens performance evaluation.
method Custom grid display for ground truth, CNN for MTF estimation.
result Estimates MTF from natural images, generalizes to unseen lenses.
Active learning selects samples for labeling to build accurate models with minimal labeled data.
problem Costly acquisition of labeled data in supervised learning.
method Adaptive selection of unlabeled data samples for labeling.
result Efficient model building with minimal labeled data.
Validates composite systems using discrepancy propagation.
problem Validation of industrial systems with costly real-world tests.
method Propagates bounds on distributional discrepancy measures through a composite system.
result Derives upper bound on real system failure probability from simulations.
We analyze a simple asset transfer model in which the transfer amount is a fixed fraction f of the giver's wealth. The model is analyzed in a new way by Laplace transforming the master equation, solving it analytically and numerically for the steady-state distribution, and exploring the solutions for various values o…
Paper presents a method to accurately quantify neural network uncertainty without sampling.
problem Uncertainty quantification in neural networks for reliability and robustness.
method Sample-free moment propagation technique for mean vectors and covariance matrices.
result Analytic solution for covariance of nonlinear activation functions.
Survey of Monte Carlo methods for noisy, costly densities in reinforcement learning and ABC.
problem Dealing with intractable, costly, and noisy densities in real-world scenarios.
method Classification and description of Monte Carlo methodologies using surrogate models.
result Unified scheme and numerical comparisons of different methodologies.
Study optimizes data collection from biased, costly sources to minimize risk.
problem Estimating population means and group-conditional means from multiple sources with varying costs and biases.
method Develops a sampling plan that maximizes effective sample size, paired with a post-stratification estimator.
result Achieves budgeted minimax optimal risk for estimating population means and group-conditional means.
Extends deep learning for hierarchical data to improve classification accuracy.
problem Classification with costly features in hierarchical data.
method Extended deep reinforcement learning with hierarchical deep sets and softmax.
result Superior performance on seven datasets, including malicious web domain classification.
Completes missing kernel values across multiple data views.
problem Missing data in kernel matrices across multiple views.
method Models both within-view and between-view relationships to predict missing values.
result Outperforms existing techniques on simulated and real-world data.
Novel approach for estimating conditional expectations using Bayesian quadrature.
problem Estimating conditional expectations with costly evaluations.
method Probabilistic numerical methods incorporating prior smoothness knowledge.
result Fast convergence rate and uncertainty quantification.
Optimal investment strategy with expert opinions in uncertain conditions.
problem Optimizing wealth in a model with unobservable drift and costly expert opinions.
method Embedding into a full information problem, using viscosity solutions and stochastic Perron's method.
result Constructing optimal trading and expert opinion strategies under sufficient regularity conditions.
LLMs help less-resourced researchers access costly data.
problem Unequal access to costly datasets limits research contributions.
method RAG framework with GPT-4o-mini for automated data collection.
result LLMs can collect CEO pay ratios and CAMs from corporate disclosures with high accuracy and low cost.
Optimizes optimal transport distances using low-dimensional embeddings.
problem High computational cost of optimal transport distances in high dimensions.
method Approximate OT distances using 1-Lipschitz maps in a lower-dimensional space.
result Efficiently approximates optimal transport distances with lower computational cost.