A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Study improves statistical power for detecting algorithmic bias in educational data.
Machine learning forecasts show bias at long horizons, contrary to standard tests.
Unified framework for mean testing under truncation bias.
Adversarial training leads to large generalization gap, decomposed into bias and variance.
Study evaluates bias mitigation methods in deep learning, finds they often exploit hidden biases.
Proposes tests to control confounding bias in predictive models.
Estimates peeking effects in p-values to correct bias.
The bias-variance tradeoff tells us that as model complexity increases, bias falls and variances increases, leading to a U-shaped test error curve. However, recent empirical results with over-parameterized neural networks are marked by a striking absence of the classic U-shaped test error curve: test error keeps decrea…
New framework minimizes interference and selection bias in network A/B testing.
Active testing reduces label costs for efficient model evaluation.
In real-world classification problems, the class balance in the training dataset does not necessarily reflect that of the test dataset, which can cause significant estimation bias. If the class ratio of the test dataset is known, instance re-weighting or resampling allows systematical bias correction. However, learning…
The paper develops a theory explaining how machine learning models can amplify biases.
Study shows different trajectory prediction models generalize better under OoD conditions.
In classification problems, sampling bias between training data and testing data is critical to the ranking performance of classification scores. Such bias can be both unintentionally introduced by data collection and intentionally introduced by the algorithm, such as under-sampling or weighting techniques applied to i…
Deep neural networks can generalize by reducing high-frequency noise over time, not always following a monotonic learning bias.
Propensity score matching improves fairness in machine learning models.
New tools for assessing and correcting bias in AI algorithms.
Synthetic control method improves policy evaluation in high-dimensional settings.
For many causal effect parameters of interest, doubly robust machine learning (DRML) estimators are the state-of-the-art, incorporating the good prediction performance of machine learning; the decreased bias of doubly robust estimators; and the analytic tractability and bias reduction of sample splitting wi…
Performance of investment managers are evaluated in comparison with benchmarks, such as financial indices. Due to the operational constraint that most professional databases do not track the change of constitution of benchmark portfolios, standard tests of performance suffer from the "look-ahead benchmark bias," when t…
Framework tests group fairness in machine learning models.
With recent advances in high throughput technology, researchers often find themselves running a large number of hypothesis tests (thousands+) and esti- mating a large number of effect-sizes. Generally there is particular interest in those effects estimated to be most extreme. Unfortunately naive estimates of these effe…
Deep networks generalize well even when they fit training data perfectly, thanks to overparametrization.
This paper examines the use of a residual bootstrap for bias correction in machine learning regression methods. Accounting for bias is an important obstacle in recent efforts to develop statistical inference for machine learning methods. We demonstrate empirically that the proposed bootstrap bias correction can lead to…
We detect lookahead bias in LLM forecasts using a novel statistical method.
New insights into bias and variance in over-parameterized models.
Securely trains fair models using homomorphic encryption.
Corrects bias in LLM-as-a-judge evaluations using adaptive calibration.
Develops tools to audit ML models for bias and unfairness.
The paper detects and identifies bias in data using a counterfactual approach.
Proposes a neural network model to improve predictions in biased datasets.
Most of previous machine learning algorithms are proposed based on the i.i.d. hypothesis. However, this ideal assumption is often violated in real applications, where selection bias may arise between training and testing process. Moreover, in many scenarios, the testing data is not even available during the training pr…
Recent developments in Neural Relation Extraction (NRE) have made significant strides towards Automated Knowledge Base Construction (AKBC). While much attention has been dedicated towards improvements in accuracy, there have been no attempts in the literature to our knowledge to evaluate social biases in NRE systems. W…
The accuracy of deep neural networks is significantly affected by how well mini-batches are constructed during the training step. In this paper, we propose a novel adaptive batch selection algorithm called Recency Bias that exploits the uncertain samples predicted inconsistently in recent iterations. The historical lab…
The paper introduces Relative Bias to quantify LLM bias systematically.
DCEM algorithm reduces bias in machine learning models trained on selective labels.
Unintended bias in Machine Learning can manifest as systemic differences in performance for different demographic groups, potentially compounding existing challenges to fairness in society at large. In this paper, we introduce a suite of threshold-agnostic metrics that provide a nuanced view of this unintended bias, by…
Paper addresses selection bias in online advertising auctions.
Excessive reuse of holdout data can lead to overfitting. However, there is little concrete evidence of significant overfitting due to holdout reuse in popular multiclass benchmarks today. Known results show that, in the worst-case, revealing the accuracy of adaptively chosen classifiers on a data set of size al…
Data that is gathered adaptively --- via bandit algorithms, for example --- exhibits bias. This is true both when gathering simple numeric valued data --- the empirical means kept track of by stochastic bandit algorithms are biased downwards --- and when gathering more complicated data --- running hypothesis tests on c…
Standard methods in supervised learning separate training and prediction: the model is fit independently of any test points it may encounter. However, can knowledge of the next test point be exploited to improve prediction accuracy? We address this question in the context of linear prediction, show…
From scientific experiments to online A/B testing, the previously observed data often affects how future experiments are performed, which in turn affects which data will be collected. Such adaptivity introduces complex correlations between the data and the collection procedure. In this paper, we prove that when the dat…
Bayesian method corrects bias in imbalanced datasets.
Social bias in machine learning has drawn significant attention, with work ranging from demonstrations of bias in a multitude of applications, curating definitions of fairness for different contexts, to developing algorithms to mitigate bias. In natural language processing, gender bias has been shown to exist in contex…
Publication bias skews asset pricing research findings.
Proposes BSSP to stabilize predictions in biased data.
Algorithm recovers causal graphs in presence of latent confounders and selection bias.
Can we make Bayesian posterior MCMC sampling more efficient when faced with very large datasets? We argue that computing the likelihood for N datapoints in the Metropolis-Hastings (MH) test to reach a single binary decision is computationally inefficient. We introduce an approximate MH rule based on a sequential hypoth…