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 …
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YAHPO Gym -- An Efficient Multi-Objective Multi-Fidelity Benchmark for Hyperparameter Optimizationcs.LG
YAHPO Gym introduces a new benchmark for evaluating hyperparameter optimization methods.
problem Evaluating and comparing hyperparameter optimization methods on well-curated benchmark suites.
method Surrogate-based benchmark collection of 14 scenarios, each with multi-fidelity and multi-objective hyperparameter optimization problems.
result Surrogate-based benchmarks produce more faithful results than tabular benchmarks.
The study initiates a theoretical analysis of dynamic benchmarking models.
problem Lack of theoretical foundation and empirical studies in dynamic benchmarks.
method Examined two realizations of dynamic benchmarking: sequential and hierarchical dependency models.
result Sequential dynamic benchmarks show initial performance improvement but can stall after three rounds due to label noise.
This paper evaluates how different imputation methods affect predictive models.
problem The impact of different imputation methods on predictive models' performance.
method Systematic evaluation of various imputation methods for different data sets and machine learning algorithms.
result Recommendation of a general method for empirical benchmarking of imputation methods.
WOODS benchmarks improve understanding of time series OOD generalization.
problem Limited understanding of OOD generalization in time series.
method Presented eight open-source time series benchmarks and revised OOD algorithms.
result Large room for improvement in OOD generalization algorithms for time series.
We introduce Procgen Benchmark, a suite of 16 procedurally generated game-like environments designed to benchmark both sample efficiency and generalization in reinforcement learning. We believe that the community will benefit from increased access to high quality training environments, and we provide detailed experimen…
Optimal benchmark design varies based on costs in financial manipulation.
problem Manipulation of price benchmarks in finance.
method Analyzes empirical pattern and cost structures to determine optimal benchmark design.
result The optimal benchmark depends on the relative sizes of fixed and variable costs.
Unsupervised domain adaptation is a promising way to generalize deep models to novel domains. However, the current literature assumes that the label distribution is domain-invariant and only aligns the feature distributions or vice versa. In this work, we explore the more realistic task of Class-imbalanced Domain Adapt…
Tiny benchmarks reduce LLM evaluation costs by using fewer examples.
problem Expensive evaluation of LLMs with tens of thousands of examples.
method Developed evaluation tools and tiny versions of popular benchmarks.
result Accurately estimate LLM performance with just 100 curated examples.
Excessive reuse of test data has become commonplace in today's machine learning workflows. Popular benchmarks, competitions, industrial scale tuning, among other applications, all involve test data reuse beyond guidance by statistical confidence bounds. Nonetheless, recent replication studies give evidence that popular…
Bayesian optimization improves molecule design by addressing three pitfalls.
problem Bayesian optimization pitfalls cause poor performance in molecule design.
method Identified and addressed three pitfalls: incorrect prior width, over-smoothing, and inadequate acquisition function maximization.
result Basic BO setup achieves highest performance on PMO benchmark.
Study benchmarks 19 survival models on 34 datasets, finding Cox model still best.
problem Quantitative comparison of survival models on low-dimensional data.
method Comprehensive benchmarking of 19 models on 34 datasets, tuning and evaluating using 6 metrics.
result Cox Proportional Hazards model remains best overall for low-dimensional, right-censored data.
Risk, including economic risk, is increasingly a concern for public policy and management. The possibility of dealing effectively with risk is hampered, however, by lack of a sound empirical basis for risk assessment and management. The paper demonstrates the general point for cost and demand risks in urban rail projec…
Variational approaches based on neural networks are showing promise for estimating mutual information (MI) between high dimensional variables. However, they can be difficult to use in practice due to poorly understood bias/variance tradeoffs. We theoretically show that, under some conditions, estimators such as MINE ex…
Introduces AMLB, an open benchmark for AutoML frameworks.
problem Challenges in comparing AutoML frameworks.
method Open benchmark with 9 AutoML frameworks, 71 classification, 33 regression tasks, multi-faceted analysis, Bradley-Terry trees.
result Differences in AutoML frameworks' performance and trade-offs.
We solve the multi-criteria benchmarking problem by formalizing it as a social choice problem and identifying conditions for meaningful rankings.
problem Aggregating multiple metrics into a single ranking for models in benchmarking problems.
method Formalizing multi-criteria benchmarking as a social choice problem and identifying sufficient conditions for meaningful rankings.
result We prove that meaningful multi-criteria benchmarking becomes possible under certain preference conditions (single-peaked, group-separable, distance-restricted).
Due to the high computational demands executing a rigorous comparison between hyperparameter optimization (HPO) methods is often cumbersome. The goal of this paper is to facilitate a better empirical evaluation of HPO methods by providing benchmarks that are cheap to evaluate, but still represent realistic use cases. W…
AI agents improve forecast combination in empirical economics.
problem Hidden researcher degrees of freedom in AI-generated code.
method Adapted agent-loop architecture to empirical economics, added holdout evaluation.
result Independent agent searches find better forecast methods than benchmarks.
New meta-score EPP interprets model performance differences.
problem Lack of interpretable benchmarks for model performance.
method Elo-based Predictive Power (EPP) meta-score, logistic regression.
result EPP scores have probabilistic interpretation and can be compared between data sets.
QuantBench benchmarks AI methods for quantitative investment.
problem Lack of a standardized benchmark for AI in quantitative investment.
method Developed an industrial-grade benchmark platform with standardization, flexibility, and full-pipeline coverage.
result Revealed critical research directions in AI for quantitative investment.
Simpler GNNs with low-rank non-parametric aggregators perform well on graph benchmarks.
problem Over-engineering in GNN architectures for common semi-supervised node classification datasets.
method Replacing feature aggregation with a non-parametric learner to streamline GNN design.
result Non-parametric regression is effective for semi-supervised learning on sparse, directed networks.
Recent studies have shown that online portfolio selection strategies that exploit the mean reversion property can achieve excess return from equity markets. This paper empirically investigates the performance of state-of-the-art mean reversion strategies on real market data. The aims of the study are twofold. The first…
Benchmarking deep time series models for equity portfolios
problem Selecting the best deep time series model for equity portfolios
method Using a CRSP benchmark and multi-criteria acceptability analysis
result No architecture dominates the benchmark, with TransEnc-8 having the highest rank-1 acceptability
Given a set of empirical observations, conditional density estimation aims to capture the statistical relationship between a conditional variable and a dependent variable by modeling their conditional probability . The paper develops best practices for conditional den…
Investigates long-term performance of multi-fidelity Bayesian optimization.
problem Potential long-term under-performance of multi-fidelity Bayesian optimization.
method Simple benchmark study to investigate long-term performance.
result Under-performance of multi-fidelity Bayesian optimization in certain scenarios.
AI agents improve forecast combination but require transparency.
problem AI coding agents increase flexibility in empirical economics, leading to hidden degrees of freedom.
method Adapted open-source agent-loop architecture to empirical economics workflow, adding post-search holdout evaluation.
result Multiple agent runs outperform standard benchmarks in rolling evaluation but not all on post-search holdout.
Benchmarking 18 deep neural network selective classification models on various datasets.
problem Designing a model that can abstain from making predictions when there's high risk of error.
method Evaluation of 18 selective classification models using multiple criteria.
result No single model is the clear winner; best model depends on objectives.
We report a series of robust empirical observations, demonstrating that deep Neural Networks learn the examples in both the training and test sets in a similar order. This phenomenon is observed in all the commonly used benchmarks we evaluated, including many image classification benchmarks, and one text classification…
This work introduces a new benchmark to compare neural network training algorithms.
problem Lack of reliable benchmarks to compare training algorithms effectively.
method Developed a new benchmark called AlgoPerf: Training Algorithms benchmark.
result Demonstrated the feasibility of the benchmark and set a provisional state-of-the-art.
New model reveals significant impact of data and parameter variations on machine learning benchmarks.
problem Variance in machine learning benchmarks due to data sampling, initialization, and hyperparameters.
method Modeling the benchmarking process, analyzing comparison methods, and studying error rates.
result Adding more sources of variation reduces compute cost while improving performance.
Copula Discrepancy benchmarks sample dependence structure against known families.
problem Benchmarking sample dependence structure against known families.
method Copula Discrepancy (CD) statistic comparing target Kendall's tau with fitted parameter.
result CD reliably separates on-target and off-target copulas.
This paper introduces a scalable benchmark for evaluating local posterior sampling in neural networks.
problem Degeneracy in neural network loss landscapes and its impact on SGMCMC algorithms.
method Development of a scalable benchmark for local posterior sampling.
result RMSProp-preconditioned SGLD is most effective at representing the local geometry of the posterior distribution.
Sloth predicts LLM performance using latent skills across families.
problem Variations in benchmark performance due to differences in training configurations and data processing across model families.
method Sloth uses publicly available benchmark data and assumes LLM performance is driven by latent skills influenced by model size and training tokens. It exploits correlations across benchmarks to provide accurate predictions.
result Sloth predicts LLM performance accurately and offers insights into scaling behaviors for complex tasks.
CSTS benchmarks time series clustering by evaluating correlation structures.
problem Lack of validated ground truth for objectively assessing clustering quality.
method Synthetic benchmark CSTS for evaluating correlation structures in multivariate time series data.
result CSTS enables precise diagnosis of methodological limitations in correlation-based time series clustering.
We present an online approach to portfolio selection. The motivation is within the context of algorithmic trading, which demands fast and recursive updates of portfolio allocations, as new data arrives. In particular, we look at two online algorithms: Robust-Exponentially Weighted Least Squares (R-EWRLS) and a regulari…
Are Neural Architecture Search Benchmarks Well Designed? A Deeper Look Into Operation Importancecs.LG
This study analyzes NAS benchmarks and finds that only a subset of operations is crucial for generating high-performing architectures.
problem NAS benchmarks lack generability and provide skewed performance distributions, leading to unreliable comparisons.
method Empirical analysis of widely used NAS benchmarks (101, 201, TransNAS-Bench-101) focusing on operation importance and generability.
result Only a subset of operations is necessary to generate architectures close to the upper-bound performance range, and convolution layers have the highest impact.
Implicit Kernel Learningstat.ML
Kernels are powerful and versatile tools in machine learning and statistics. Although the notion of universal kernels and characteristic kernels has been studied, kernel selection still greatly influences the empirical performance. While learning the kernel in a data driven way has been investigated, in this paper we e…
Develops a flexible batched experimentation framework for limited adaptivity.
problem Challenges of continual reallocation in bandit algorithms with delayed feedback.
method Computational framework leveraging Gaussian sequential experiment and dynamic programming.
result Improves statistical power over standard methods, even compared to Bayesian bandit algorithms.
In recent years, there has been significant progress in applying deep reinforcement learning (RL) for solving challenging problems across a wide variety of domains. Nevertheless, convergence of various methods has been shown to suffer from inconsistencies, due to algorithmic instability and variance, as well as stochas…
A new sequential method estimates Poisson means in streaming data, achieving optimality and efficiency.
problem Estimating Poisson means in a streaming, or online, framework.
method A quasi-Bayesian approach based on Newton's algorithm for a sequential estimate.
result Established frequentist guarantees including consistency and asymptotic optimality.
Paper introduces MCSD, a method for uncertainty estimation in deep learning.
problem Need for reliable uncertainty quantification in deep neural networks.
method Theoretical connection to variational inference and empirical benchmarking of MCSD.
result MCSD achieves competitive predictive accuracy and improves uncertainty ranking.
This work explores maximum likelihood optimization of neural networks through hypernetworks. A hypernetwork initializes the weights of another network, which in turn can be employed for typical functional tasks such as regression and classification. We optimize hypernetworks to directly maximize the conditional likelih…
Unified Python package N benchmarks NN-based matrix completion methods.
problem Matrix completion with robust NN methods across diverse applications.
method Unified Python package N consolidates NN-based methods.
result NN-based techniques outperform classical methods in real-world settings.
Explaining how overparametrized neural networks simultaneously achieve low risk and zero empirical risk on benchmark datasets is an open problem. PAC-Bayes bounds optimized using variational inference (VI) have been recently proposed as a promising direction in obtaining non-vacuous bounds. We show empirically that thi…
Compensation methods correct overestimation of adversarial robustness in neural networks.
problem Overestimation of adversarial robustness using first-order attack methods.
method Proposed compensation methods address inaccurate gradient computation and reduce backpropagations.
result Empirical evaluation of adversarial robustness is improved with these methods.
Current OOD benchmarks overestimate model robustness to spurious correlations.
problem Spurious correlations degrade OOD performance, but benchmarks show the opposite.
method Analyze OOD datasets for spurious correlations and derive conditions for robustness.
result Current OOD benchmarks are misspecified and overestimate model robustness.
Benchpress streamlines benchmarking structure learning algorithms for probabilistic models.
problem Benchmarking structure learning algorithms for probabilistic graphical models.
method Snakemake workflow for scalable, reproducible benchmarks.
result Demonstrates applicability in five typical data scenarios.
Generates synthetic manufacturing data for causal discovery benchmarking.
problem Lack of suitable real data for validating causal discovery algorithms.
method Distributional random forests for estimating conditional distributions.
result Semisynthetic manufacturing data adheres to a causal model.