Evaluates change point detection algorithms on real-world data.
problem Insufficient evaluation of change point detection algorithms on real-world time series.
method Developed a data set of 37 time series from various domains, annotated by human experts, and evaluated 14 algorithms using consistency metrics.
result Demonstrates the need for better evaluation methods in change point detection.
Study introduces a benchmark suite for evaluating neural MI estimators on real-world unstructured datasets.
problem Lack of comprehensive evaluation methods for neural MI estimators on real-world unstructured datasets.
method Developed a benchmark suite using same-class sampling and a binary symmetric channel trick.
result Showed accurate manipulation of true MI values of real-world datasets.
Evaluates transfer learning methods in dynamic data availability scenarios.
problem Real-world data availability varies over time, leading to unrealistic TL method evaluations.
method Proposes a data manipulation framework to simulate varying data availability and domain transformations.
result Demonstrates the usefulness of the framework on proprietary and publicly available datasets.
New R package for NMF evaluated on real-world data.
problem Limited comprehensive evaluations of NMF packages under real-world conditions.
method Systematic performance comparison of three NMF packages using real-world data.
result New package outperforms existing ones in computational efficiency and reconstruction accuracy.
Study reveals gaps between simulated and real-world treatment effect evaluation metrics.
problem Evaluation of treatment effect estimation models differs between academic and practical settings.
method Comprehensive empirical study comparing semi-simulated benchmarks and real-world datasets.
result Counterfactual metrics do not reliably predict observable metrics, and rankings from simulated benchmarks do not generalize to real-world data.
New model detects crying in real-world settings with improved accuracy.
problem Generalization of cry detection models to real-world environments.
method Evaluated machine learning approaches on a novel dataset of real-world infant crying.
result Improved F1 score of 0.613 for crying event recognition in real-world settings.
Paper addresses challenges in benchmarking stream learning algorithms with real-world data.
problem Lack of publicly available non-stationary real-world datasets for evaluating stream algorithms.
method Proposes a new public data repository for benchmarking stream algorithms with real-world data.
result Mitigates problems related to dataset choice in experimental evaluation of stream classifiers and drift detectors.
XAI-Bench releases synthetic datasets for evaluating feature attribution methods.
problem Evaluating and comparing feature attribution methods is challenging.
method Released synthetic datasets and benchmarking library.
result Efficiently evaluates feature attribution methods across various metrics.
Suitability filter detects model performance degradation in real-world deployment.
problem Ensuring model reliability in safety-critical domains without access to ground truth labels.
method Uses suitability signals to evaluate classifier performance on unlabeled user data.
result The suitability filter reliably detects performance deviations due to covariate shift.
Discovery of causal relations from observational data is essential for many disciplines of science and real-world applications. However, unlike other machine learning algorithms, whose development has been greatly fostered by a large amount of available benchmark datasets, causal discovery algorithms are notoriously di…
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.
LiveTradeBench evaluates LLMs in live trading environments.
problem Static benchmarks fail to assess real-world trading ability.
method Live data streaming, portfolio management abstraction, multi-market evaluation.
result LLMs show distinct portfolio styles and adapt to live signals.
The paper critiques ε-fairness, showing it can lead to unfair outcomes and proposes a utility-based approach.
problem The limitations of probabilistic fairness metrics in real-world contexts.
method Utility-based approach to measure fairness, addressing the issue of unavailable data on false negatives.
result A utility-based approach uncovers necessary actions to achieve true fairness, contrasting with traditional probability-based evaluations.
Modern CATE models often fail to outperform a trivial zero-effect predictor, highlighting significant challenges.
problem Lack of robustness in CATE models when applied to real-world data.
method Large-scale benchmark study using diverse observational sampling strategies and novel statistics.
result 62% of CATE estimates have higher MSE than a trivial zero-effect predictor, indicating poor performance.
PRUDEX-Compass evaluates FinRL methods on 6 axes for financial market investments.
problem Insufficient evaluation of FinRL methods in financial markets.
method Introduces PRUDEX-Compass with 6 axes and 17 measures for evaluation.
result Demonstrates the effectiveness of PRUDEX-Compass on 4 real-world datasets.
Study evaluates scalability and real-world impact of disentangled representations.
problem Scalability and real-world impact of disentangled representations.
method New high-resolution dataset and architectures for disentangled representation learning.
result Disentanglement predicts out-of-distribution task performance.
Real-world applications often involve domain-specific and task-based performance objectives that are not captured by the standard machine learning losses, but are critical for decision making. A key challenge for direct integration of more meaningful domain and task-based evaluation criteria into an end-to-end gradient…
Proposes a method to evaluate generalizability in causal inference models.
problem Lack of formal procedures to statistically evaluate generalizability in causal inference.
method Frugal parameterization to simulate from causal benchmarks, using mean and distributional regression methods.
result Ensures more realistic evaluations of causal inference models, avoiding over-reliance on conventional metrics.
Optimizes function networks by selectively evaluating parts of the network, reducing costs.
problem Optimizing expensive function networks where full evaluations are costly.
method Knowledge gradient acquisition function for cost-aware partial evaluations.
result Outperforms existing BOFN methods and benchmarks across various problems.
Online knowledge repositories typically rely on their users or dedicated editors to evaluate the reliability of their content. These evaluations can be viewed as noisy measurements of both information reliability and information source trustworthiness. Can we leverage these noisy evaluations, often biased, to distill a…
Study evaluates large language models' ability to understand probabilistic real-world distributions.
problem Understanding how LLMs grasp probabilistic knowledge of real-world distributions.
method Developed a benchmark to test LLMs' ability to learn and represent empirical distributions across various domains.
result LLMs perform poorly in understanding real-world statistics and do not naturally internalize these distributions.
Proposes a stability evaluation criterion for learning models using distributional perturbations.
problem Ensuring reliable deployment of learning models in out-of-sample environments.
method Uses optimal transport discrepancy with moment constraints to quantify minimal perturbation required for model deterioration.
result Validates the practical utility of the stability evaluation criterion across various real-world applications.
Many real-world classification problems are significantly class-imbalanced to detriment of the class of interest. The standard set of proper evaluation metrics is well-known but the usual assumption is that the test dataset imbalance equals the real-world imbalance. In practice, this assumption is often broken for vari…
Recommendation systems have been integrated into the majority of large online systems. They tailor those systems to individual users by filtering and ranking information according to user profiles. This adaptation process influences the way users interact with the system and, as a consequence, increases the difficulty …
Concept-driven OPE reduces variance in off-policy decision evaluation.
problem High variance in off-policy decision evaluation due to limited sample sizes.
method Integrating human-explainable concepts into OPE to reduce variance.
result Concept-based OPE estimators remain unbiased and reduce variance when concepts are known and predefined.
Study evaluates methods for improving model robustness to various real-world distribution shifts.
problem Improving model robustness to real-world distribution shifts like geographic changes.
method Introduced new datasets and evaluated existing methods on four types of shifts (style, blurriness, location, camera operation).
result Data augmentations and larger models can improve robustness on real-world distribution shifts, contrary to prior claims.
While recent developments in autonomous vehicle (AV) technology highlight substantial progress, we lack tools for rigorous and scalable testing. Real-world testing, the de facto evaluation environment, places the public in danger, and, due to the rare nature of accidents, will require billions of miles in or…
This study synthesizes stablecoin systems and develops a performance evaluation framework.
problem Fragmented academic research on stablecoins across economics, law, and computer science.
method Multi-method research design including literature synthesis, performance evaluation framework, and case study.
result Unified taxonomy and performance evaluation framework for stablecoin design.
RETINA Benchmark evaluates Bayesian deep learning on diabetic retinopathy detection.
problem Reliable uncertainty quantification for deep learning models in medical applications.
method Design and evaluation of a real-world diabetic retinopathy dataset and tasks.
result Benchmarking of Bayesian deep learning methods on diabetic retinopathy detection tasks.
Reinforcement learning (RL) has proven its worth in a series of artificial domains, and is beginning to show some successes in real-world scenarios. However, much of the research advances in RL are often hard to leverage in real-world systems due to a series of assumptions that are rarely satisfied in practice. We pres…
R-AutoEval+ improves model evaluation efficiency and reliability using adaptive synthetic data.
problem Accurate model selection from AI candidates using real-world data is costly and impractical at scale.
method R-AutoEval+ uses adaptive prediction-powered inference to correct bias in autoevaluators while maintaining or improving sample efficiency.
result R-AutoEval+ provides finite-sample reliability guarantees and enhanced sample efficiency compared to conventional methods.
Proposes a new framework to evaluate causal discovery methods for time series data.
problem Lack of ground truth for causal discovery in time series data.
method Flexible framework for generating synthetic time series data.
result Demonstrates degradation in performance when assumptions are violated.
This paper compares and evaluates methods for evaluating statistical models using benchmarking data and simulations.
problem Choosing between benchmarking data sets and simulation studies for method comparison studies.
method Borrowing ideas from mixed methods research and Clinical Scenario Evaluation, the paper investigates and develops new approaches to evaluate methods.
result Develops new approaches to evaluate methods by combining the strengths of benchmarking data sets and simulation studies.
CausalRivers benchmarks causal discovery methods on real-world river discharge data.
problem Lack of in-the-wild evaluation of causal discovery methods on complex, real-world data.
method Introduces CausalRivers, a large-scale dataset of river discharge data for benchmarking.
result Demonstrates the utility of CausalRivers in evaluating causal discovery methods.
Retail company uses Prophet algorithm for accurate sales forecasting.
problem Accurate sales forecasting in the retail industry.
method Facebook's Prophet algorithm and backtesting strategy.
result Framework demonstrates real-world use case capabilities.
The paper tackles sampling bias in credit scoring models and proposes methods to improve their training and evaluation.
problem Sampling bias in credit scoring models leads to an incomplete representation of the borrower population.
method Bias-aware self-learning framework and Bayesian evaluation method to correct for bias.
result Bayesian evaluation outperforms standard accuracy measures in predicting future performance.
A framework for evaluating and benchmarking concept drift detection methods
problem Data stream mining challenged by concept drift
method A novel benchmarking framework
result Fair comparisons of drift detection methods
New metrics needed for streaming ML due to delayed labels.
problem Streaming ML evaluation fails to identify unexpected performance.
method Recommend additional metrics for streaming ML performance.
result New metrics are needed for streaming ML due to delayed labels.
This work evaluates PDA methods without target labels, revealing significant accuracy drops.
problem Evaluating PDA methods without target labels and inconsistent experimental settings.
method Realistic evaluation of 7 PDA methods with 7 model selection strategies on 2 datasets.
result Accuracy drops up to 30 percentage points without target labels, only one method performs well.
Deep Reinforcement Learning has shown great success in a variety of control tasks. However, it is unclear how close we are to the vision of putting Deep RL into practice to solve real world problems. In particular, common practice in the field is to train policies on largely deterministic simulators and to evaluate alg…
SurvHTE-Bench benchmarks HTE estimation in survival analysis with diverse datasets.
problem Challenges in estimating HTEs from right-censored survival data.
method Modular synthetic datasets, semi-synthetic datasets, and real-world datasets.
result First rigorous comparison of survival HTE methods under diverse conditions.
Machine learning models deployed in real-world applications are often evaluated with precision-based metrics such as F1-score or AUC-PR (Area Under the Curve of Precision Recall). Heavily dependent on the class prior, such metrics make it difficult to interpret the variation of a model's performance over different subp…
The study evaluates GRL approaches and finds limitations in their applicability.
problem Challenges in applying GRL approaches to real-world graphs with varying structural differences.
method Empirical data-driven framework and theoretical analysis of GRL approaches.
result Existing GRL approaches are insufficient for real-world graphs with diverse structural patterns.
We propose practical extensions to Bayesian optimization for solving dynamic problems. We model dynamic objective functions using spatiotemporal Gaussian process priors which capture all the instances of the functions over time. Our extensions to Bayesian optimization use the information learnt from this model to guide…
In this work, we consider the problem of model selection for deep reinforcement learning (RL) in real-world environments. Typically, the performance of deep RL algorithms is evaluated via on-policy interactions with the target environment. However, comparing models in a real-world environment for the purposes of early …
Causal inference analysis is the estimation of the effects of actions on outcomes. In the context of healthcare data this means estimating the outcome of counter-factual treatments (i.e. including treatments that were not observed) on a patient's outcome. Compared to classic machine learning methods, evaluation and val…
iMOCA optimizes multiple objectives with continuous approximations for resource efficiency.
problem Optimizing multiple objectives with continuous function approximations that balance accuracy and evaluation cost.
method Information-Theoretic Multi-Objective Bayesian Optimization with Continuous Approximations (iMOCA) selects input and function approximations to maximize information gain per unit cost.
result iMOCA significantly improves over existing single-fidelity methods in approximating the optimal Pareto set.
When learning behavior, training data is often generated by the learner itself; this can result in unstable training dynamics, and this problem has particularly important applications in safety-sensitive real-world control tasks such as robotics. In this work, we propose a principled and model-agnostic approach to miti…