Neyman's framework evaluates personalized treatment rules using experiments.
problem Evaluating the efficacy of individualized treatment rules derived by machine learning.
method Neyman's repeated sampling framework applied to cross-fitted ITRs.
result Ex-post evaluation of ITRs can be more efficient than random assignment.
Combines experimental and historical data for robust policy evaluation.
problem Policy evaluation with mixed data sources, especially experimental vs historical.
method Linear integration of estimators from experimental and historical data, optimized for MSE minimization.
result Proposed estimators outperform traditional methods in ridesharing company data.
Evaluation of deep reinforcement learning (RL) is inherently challenging. In particular, learned policies are largely opaque, and hypotheses about the behavior of deep RL agents are difficult to test in black-box environments. Considerable effort has gone into addressing opacity, but almost no effort has been devoted t…
We approximate differential entropy for efficient Bayesian experimental design.
problem Efficiently estimating expected information gain in large-scale inference problems.
method Approximate differential entropy using Monte Carlo or quasi-Monte Carlo surrogates.
result Our approach achieves comparable or better convergence rates than state-of-the-art methods.
Novel neural architecture improves Bayesian experimental design efficiency.
problem Intractable evaluation of expected information gain (EIG) in Bayesian optimal experimental design.
method Develops a neural architecture that optimizes a single variational model for estimating EIG across many designs, using a lower bound for computational efficiency.
result Significantly improves accuracy in Bayesian experimental design with better sample efficiency.
Defines explanations for classifier outcomes using causal concepts.
problem Understanding classifier outcomes in a causal context.
method Proposes a new definition of explanation based on causality, compares it with existing notions, and evaluates it experimentally.
result Experimental evaluation shows the new definition's effectiveness on financial datasets.
Like all sub-fields of machine learning Bayesian Deep Learning is driven by empirical validation of its theoretical proposals. Given the many aspects of an experiment it is always possible that minor or even major experimental flaws can slip by both authors and reviewers. One of the most popular experiments used to eva…
We study the effectiveness of non-uniform randomized feature selection in decision tree classification. We experimentally evaluate two feature selection methodologies, based on information extracted from the provided dataset: (i) \emph{leverage scores-based} and (ii) \emph{norm-based} feature selection. Experimenta…
Aims to improve personalized treatment decisions through Bayesian experimental design.
problem Evaluating and improving personalized treatment decisions in contexts like customer service.
method Model-agnostic Bayesian Experimental Design to efficiently gather data and avoid highly sub-optimal treatments.
result Our method achieves superior performance in evaluating and improving treatment decisions compared to traditional approaches.
A framework for designing and evaluating new GCN variants.
problem Designing and evaluating new graph convolutional network (GCN) variants.
method Propose a framework to compose networks using building blocks of GCN.
result Several newly composed variants are useful alternatives and competitive with original GCNs.
Develops a method to evaluate OPE robustness to hyperparameters and policies.
problem Difficulty in selecting and tuning OPE estimators due to limited experimental evaluations.
method Introduces IEOE (Interpretable Evaluation for Offline Evaluation) to assess robustness.
result Demonstrates improved evaluation of OPE estimators' reliability.
Presents SPEED, an algorithm for optimal policy evaluation in linear bandits with heteroscedastic noise.
problem Optimal data collection for policy evaluation in linear bandits with heteroscedastic reward noise.
method Formulated an optimal design for weighted least squares estimates, derived the optimal sample allocation, introduced SPEED algorithm, and derived regret bounds.
result SPEED leads to policy evaluation with MSE comparable to oracle strategy and significantly lower than random policy execution.
Study examines fairness in machine learning for credit scoring.
problem Bias in machine learning models for credit scoring.
method Comprehensive experimental study of fairness-aware machine learning models.
result Fairness-aware models improve fairness while maintaining accuracy.
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.
In this paper, we focus on developing efficient sensitivity analysis methods for a computationally expensive objective function f(x) in the case that the minimization of it has just been performed. Here "computationally expensive" means that each of its evaluation takes significant amount of time, and therefore our m…
This study uses OPE methods to quickly assess auction policies.
problem Rapid decision-making in dynamic auction environments.
method Off-Policy Evaluation and counterfactual methods.
result Improved policy selection and optimization.
New algorithms for model selection in off-policy evaluation of reinforcement learning.
problem Hyperparameter tuning for off-policy evaluation methods in reinforcement learning.
method Developed new model-free and model-based selectors with theoretical guarantees and a new experimental protocol.
result New model-free selector, LSTD-Tournament, demonstrates promising empirical performance.
New method uses approximate KLD for intractable likelihood models.
problem Designing experiments for models with intractable likelihoods.
method Derive a lower bound of KLD utility, express it in terms of entropies, and evaluate efficiently.
result Demonstrated the performance of the proposed method through numerical examples.
Framework tests CATE homogeneity across trials and evaluates confounding.
problem Assessing treatment effect consistency across randomized and observational studies.
method Leverages multiple randomized trials to test CATE homogeneity and compares with observational data.
result Identifies potential confounding and effect heterogeneity in treatment effects.
We show how a multi-agent simulator can support two important but distinct methods for assessing a trading strategy: Market Replay and Interactive Agent-Based Simulation (IABS). Our solution is important because each method offers strengths and weaknesses that expose or conceal flaws in the subject strategy. A key weak…
Data quality issues have attracted widespread attention due to the negative impacts of dirty data on data mining and machine learning results. The relationship between data quality and the accuracy of results could be applied on the selection of the appropriate algorithm with the consideration of data quality and the d…
GeneDisco benchmarks experimental design for drug discovery.
problem Vast experimental design space in drug discovery.
method Machine learning for optimal experimental design.
result Standardised benchmark suite for active learning.
Active learning (AL) repeatedly trains the classifier with the minimum labeling budget to improve the current classification model. The training process is usually supervised by an uncertainty evaluation strategy. However, the uncertainty evaluation always suffers from performance degeneration when the initial labeled …
A computational theory reduces agent evaluation errors and speeds up processes.
problem Efficient evaluation of mini agents at reduced cost.
method Developed a computational theory and a meta-learner to handle heterogeneous agents.
result Reduced evaluation errors by 24.1% to 99.0% across various scenarios.
Gemini uses inexpensive measurements to correct biases in expensive property evaluations.
problem Accurate estimation of materials properties using expensive measurements is hindered in scientific discovery campaigns.
method Gemini is a data-driven model that corrects systematic biases between property evaluation methods using inexpensive measurements.
result Gemini reduces the number of expensive evaluations needed for Bayesian optimization in materials discovery.
BOED improves SBI by optimizing experimental designs and inference functions.
problem Efficiently use experimental resources for better inference on complex models.
method Link mutual information bounds between SBI and BOED, optimizing both design and inference.
result BOED improves inference in real-world simulators in epidemiology and biology.
The paper evaluates samplers on multi-modal targets, focusing on mode separation and recovery.
problem Handling multi-modality in sampling.
method Synthetic experimental setting focusing on mode relative importance recovery.
result Illustrates the challenges and potential of samplers in multi-modality.
We offer an experimental benchmark and empirical study for off-policy policy evaluation (OPE) in reinforcement learning, which is a key problem in many safety critical applications. Given the increasing interest in deploying learning-based methods, there has been a flurry of recent proposals for OPE method, leading to …
The increasing availability of individual-level data has led to numerous applications of individualized (or personalized) treatment rules (ITRs). Policy makers often wish to empirically evaluate ITRs and compare their relative performance before implementing them in a target population. We propose a new evaluation metr…
Bayesian DOE accelerates experimental design with improved efficiency.
problem Enhancing experimental design efficiency and reliability.
method Bayesian framework, conditional density estimation, informative data selection.
result Significantly improved computational efficiency of experimental design.
Efficiently designs experiments without integrating posterior distributions.
problem Computational inefficiency in Bayesian experimental design for PDE-based models.
method Likelihood-free approach using ANN to approximate conditional expectation.
result Significant reduction in observation model evaluations.
Study evaluates reinforcement learning algorithms for sequential experimental design.
problem Lack of generalization in reinforcement learning for experimental design.
method Investigated several reinforcement learning algorithms for sequential experimental design.
result Certain algorithms, using dropout or ensemble approaches, show attractive generalization properties.
Experimental evaluation is a major research methodology for investigating clustering algorithms and many other machine learning algorithms. For this purpose, a number of benchmark datasets have been widely used in the literature and their quality plays a key role on the value of the research work. However, in most of t…
Learning knowledge representation is an increasingly important technology applicable in many domain-specific machine learning problems. We discuss the effectiveness of traditional Link Prediction or Knowledge Graph Completion evaluation protocol when embedding knowledge representation for categorised multi-relational d…
Unified framework for hybrid learning and optimization via active inference.
problem Sequential decisions in black-box evaluations requiring both task improvement and uncertainty reduction.
method Pragmatic Curiosity (PraC) framework that evaluates queries by balancing information gain and pragmatic value.
result Unified approach reduces decision risk and improves coverage of critical regions without task-specific rules.
System interprets complex treatment effects for personalized policies.
problem Complex, hard-to-understand treatment effect models.
method Scalable, interpretable personalized experimentation system.
result Learned explanations and generated interpretable policies.
A new method for experimental design focuses on predicting downstream quantities of interest.
problem Designs that maximize parameter learning may not maximize downstream quantity prediction.
method Likelihood-free goal-oriented optimal experimental design (LF-GO-OED) using ABC density ratio estimation.
result LF-GO-OED maximizes the expected information gain for downstream quantities.
Experimental design is crucial for inference where limitations in the data collection procedure are present due to cost or other restrictions. Optimal experimental designs determine parameters that in some appropriate sense make the data the most informative possible. In a Bayesian setting this is translated to updatin…
Deep reinforcement learning (RL) has achieved breakthrough results on many tasks, but agents often fail to generalize beyond the environment they were trained in. As a result, deep RL algorithms that promote generalization are receiving increasing attention. However, works in this area use a wide variety of tasks and e…
Study optimizes experimental design for best treatment arm identification.
problem Identifying the best treatment arm given contextual information.
method Adaptive Sampling-Policy Learning (PLAS) strategy for minimax rate optimality.
result PLAS strategy achieves minimax rate optimality in expected simple regret.
Study proposes worst+gap measure for better DG evaluation.
problem Lack of comprehensive exploration of average measure in DG evaluation.
method Introduced worst+gap measure and compared it with average measure.
result Worst+gap measure provides a more accurate approximation of true DG performance.
Online media provides opportunities for marketers through which they can deliver effective brand messages to a wide range of audiences. Advertising technology platforms enable advertisers to reach their target audience by delivering ad impressions to online users in real time. In order to identify the best marketing me…
Efficiently identifies key input variables for expensive functions using active learning.
problem Efficiently identify key input variables for expensive, black-box functions.
method Proposes novel active learning acquisition functions targeting derivative-based global sensitivity measures (DGSMs) under Gaussian process surrogate models.
result Active learning substantially enhances sample efficiency of DGSM estimation, especially with limited evaluation budgets.
When a feed-forward neural network (FNN) is trained for source ranging in an ocean waveguide, it is difficult evaluating the range accuracy of the FNN on unlabeled test data. A fitting-based early stopping (FEAST) method is introduced to evaluate the range error of the FNN on test data where the distance of source is u…
Optimizes expensive experiments by incorporating expert knowledge.
problem Expensive experiments require minimizing the number of trials.
method Bayesian optimization with posterior sampling of expert knowledge.
result Demonstrates significant efficiency gains in experiments and hyperparameter tuning.
The paper introduces new metrics for evaluating generative models of behavior.
problem Lack of quantitative evaluation criteria for unsupervised behavior discovery.
method Proposed and investigated several metrics for generative models of behavior.
result The proposed metrics correspond with biologists' intuitions and allow for model evaluation and bias understanding.
The optimization of expensive-to-evaluate black-box functions over combinatorial structures is an ubiquitous task in machine learning, engineering and the natural sciences. The combinatorial explosion of the search space and costly evaluations pose challenges for current techniques in discrete optimization and machine …
This paper critically examines unsupervised disentangled representation learning, revealing challenges and limitations.
problem The difficulty of unsupervised learning of disentangled representations and the challenges in evaluation metrics.
method Theoretical analysis and a large-scale experimental study covering 8 datasets and 14000 models.
result Well-disentangled models cannot be identified without supervision, and different evaluation metrics disagree on what constitutes disentanglement.