Classifies privacy policy segments for better user understanding.
problem Difficulty in understanding privacy policies due to legal jargon.
method Uses machine learning and deep learning techniques to classify privacy policy segments.
result Identifies data practices in privacy policies for better user comprehension.
Study highlights how lazy data practices in fair ML research can unfairly impact minority groups.
problem Lazy data practices in fair ML research can unfairly impact minority groups.
method Systematic study of 280 experiments across 142 publications on 142 datasets.
result Unreflective data practices lead to biased findings and unfair treatment of minorities.
A practical guide to Variational Bayes methods.
problem Bayesian inference for data analysis practitioners.
method Explains and demonstrates commonly used Variational Bayes methods.
result Quick derivation and implementation of VB algorithms.
Semi-supervised wrapper methods are concerned with building effective supervised classifiers from partially labeled data. Though previous works have succeeded in some fields, it is still difficult to apply semi-supervised wrapper methods to practice because the assumptions those methods rely on tend to be unrealistic i…
Aggregation defenses improve deep learning models' robustness against data poisoning attacks.
problem Data poisoning attacks manipulate deep learning models with malicious training samples.
method Deep Partition Aggregation, efficiency improvements, data-to-complexity ratio, poisoning overfitting phenomenon.
result Aggregation defenses boost poisoning robustness through the poisoning overfitting phenomenon.
Topological Data Analysis is a recent and fast growing field providing a set of new topological and geometric tools to infer relevant features for possibly complex data. This paper is a brief introduction, through a few selected topics, to basic fundamental and practical aspects of \tda\ for non experts.
Clinical AI models fail to transfer between sites due to site-specific practices.
problem Clinical AI models perform poorly at new sites.
method Identify and isolate site-specific clinical practices affecting data distribution.
result A potential solution to improve model transferability.
Data Science is currently a popular field of science attracting expertise from very diverse backgrounds. Current learning practices need to acknowledge this and adapt to it. This paper summarises some experiences relating to such learning approaches from teaching a postgraduate Data Science module, and draws some learn…
Addresses theoretical and practical aspects of Gaussian differential privacy.
problem Theoretical and practical challenges in privacy-preserving data analysis.
method Discussion of f-differential privacy and Gaussian differential privacy.
result Gaussian differential privacy can enhance privacy in various applications.
Machine learning suffers from poor design, data, and evaluation practices.
problem Ad hoc design, poor data hygiene, and lack of statistical rigor in model evaluation.
method Examines the entire machine learning process from design to evaluation, highlighting common pitfalls and providing recommendations.
result Common pitfalls in machine learning research and development are identified and actionable recommendations are provided.
Differentially private learning on real-world data poses challenges for standard machine learning practice: privacy guarantees are difficult to interpret, hyperparameter tuning on private data reduces the privacy budget, and ad-hoc privacy attacks are often required to test model privacy. We introduce three tools to ma…
New algorithm ensures global convergence in deep neural networks beyond NTK regime.
problem Existing global convergence guarantees do not apply to practical deep networks.
method Proposes an algorithm with global convergence guarantees under the expressivity condition.
result Algorithm ensures global convergence in practical settings beyond NTK regime.
Bayesian inference has great promise for the privacy-preserving analysis of sensitive data, as posterior sampling automatically preserves differential privacy, an algorithmic notion of data privacy, under certain conditions (Dimitrakakis et al., 2014; Wang et al., 2015). While this one posterior sample (OPS) approach e…
New ITID assumption improves generalization in practical tasks.
problem Generalization theories fail to interpret many generalization phenomena and guide practical learning tasks due to the IID assumption.
method Proposes ITID assumption to consider task properties, derives a new generalization bound, and introduces an invariance enhancement algorithm.
result Demonstrates the effectiveness of the new generalization theory in improving practical generalization performance.
The study compares machine learning models for depression detection and highlights the importance of feature selection.
problem The choice of features in machine learning models for depression detection is crucial.
method Comparison of seven machine learning models on depression detection tasks.
result Optimal feature selection is essential for accurate and clinically acceptable classification solutions.
Improves PU learning for imbalanced data with practical AUL estimation and new training method.
problem Training binary classifiers on datasets with only positive and unlabeled samples.
method Asymptotic unbiased AUL estimation and ProbTagging for imbalanced data.
result ProbTagging increases AUC by up to 10% on industrial and artificial data sets.
New method predicts model performance under selection bias in healthcare.
problem Selection bias limits model generalizability in healthcare.
method Proposes a novel upper bound method for estimating model performance.
result Validates and demonstrates the practical utility of the method.
This paper explores good practices for AI explainability in finance.
problem Complex financial models lack transparency and interpretability.
method Exploring good practices for deploying explainability in AI-based financial systems.
result Developing effective XAI tools for the financial industry.
This work analyzes Fréchet regression using comparison geometry, providing theoretical and practical insights.
problem Analyzing data on complex structures like manifolds and graphs.
method Theoretical analysis through comparison geometry, focusing on existence, uniqueness, and stability of the Fréchet mean.
result Key results on the existence, uniqueness, and stability of the Fréchet mean, along with statistical guarantees for nonparametric regression.
Causal inference is central to many areas of artificial intelligence, including complex reasoning, planning, knowledge-base construction, robotics, explanation, and fairness. An active community of researchers develops and enhances algorithms that learn causal models from data, and this work has produced a series of im…
This chapter focuses on developing datasets for machine learning, addressing data preparation challenges.
problem Challenges in collecting and transforming data for practical machine learning applications.
method Detailed process of dataset development including data collection, transformation, quality evaluation, and distribution.
result Provides insights and practical tools for effective dataset development and management.
Neural networks fit fewer samples than their parameters suggest in practice.
problem Understanding the practical limitations of neural network flexibility.
method Examination of neural network optimization, parameter efficiency, and loss surfaces.
result Neural networks can only fit training sets with significantly fewer samples than their parameters suggest.
Bayesian methods promise to fix many shortcomings of deep learning, but they are impractical and rarely match the performance of standard methods, let alone improve them. In this paper, we demonstrate practical training of deep networks with natural-gradient variational inference. By applying techniques such as batch n…
Article addresses practical challenges in conformal prediction.
problem Challenges in determining, computing, and controlling conformal prediction regions.
method Proposes a quadratic-polynomial non-conformity measure.
result Allows circumventing three challenges in full conformal prediction framework.
This guide clarifies techniques for assessing and comparing model calibration and performance.
problem Assessing and comparing the calibration and performance of predictive models in insurance and actuarial practice.
method Clarifies statistical techniques for assessing model calibration and comparing models, emphasizing the importance of specifying the prediction target functional and choosing the appropriate scoring function.
result Provides guidance for the practical choice of scoring functions and illustrates results with real data case studies.
The paper offers guidelines for validating data-driven models.
problem Ensuring reliable validation of data-driven models.
method A set of general rules for model validation.
result Helps practitioners create reliable validation plans and report results transparently.
New framework provides privacy guarantees for practical federated learning.
problem Inadequate privacy guarantees for federated learning due to restrictive assumptions.
method Fed-α-NormEC, integrating multiple local updates, partial client participation, and standard assumptions. result Provably convergent and differentially private federated learning framework.
Causal discovery algorithms infer causal relations from data based on several assumptions, including notably the absence of measurement error. However, this assumption is most likely violated in practical applications, which may result in erroneous, irreproducible results. In this work we show how to obtain an upper bo…
Breakthroughs in machine learning are rapidly changing science and society, yet our fundamental understanding of this technology has lagged far behind. Indeed, one of the central tenets of the field, the bias-variance trade-off, appears to be at odds with the observed behavior of methods used in the modern machine lear…
Short-and-sparse deconvolution (SaSD) is the problem of extracting localized, recurring motifs in signals with spatial or temporal structure. Variants of this problem arise in applications such as image deblurring, microscopy, neural spike sorting, and more. The problem is challenging in both theory and practice, as na…
Paper shows local SGD outperforms mini-batch SGD under certain conditions.
problem Proving local SGD's superiority in distributed learning with heterogeneous data.
method New lower and upper bounds for local SGD under first-order heterogeneity assumptions.
result Local SGD is min-max optimal under certain conditions, resolving understanding of distributed optimization.
This paper reviews PU learning evaluation methods and provides practical recommendations.
problem Evaluating PU learning methods when only positive and unlabelled data are available.
method Critical review of 51 articles proposing PU classifiers and alternative predictive accuracy measures.
result Practical recommendations for improving PU learning evaluation.
A practical one-shot federated learning algorithm for cross-silo setting.
problem Limited applicability of existing one-shot federated learning algorithms due to specific model support and lack of privacy guarantees.
method FedKT, a one-shot federated learning algorithm that supports any classification models and provides differential privacy guarantees.
result FedKT significantly outperforms other state-of-the-art federated learning algorithms with a single communication round.
Histogram binning method proven with guarantees without splitting data.
problem Proving theoretical guarantees for histogram binning without sample splitting.
method Using Markov property of order statistics to prove calibration guarantees for original method.
result Proves histogram binning has strong calibration guarantees without sample splitting.
SAGDA generates synthetic agricultural datasets to improve ML in African farming.
problem Data scarcity in African agriculture limits machine learning innovations.
method SAGDA is an open-source Python toolkit that generates, augments, and validates synthetic agricultural datasets.
result SAGDA enhances ML applications in agriculture, such as yield prediction and fertilizer recommendation.
Topological data analysis is an emerging mathematical concept for characterizing shapes in multi-scale data. In this field, persistence diagrams are widely used as a descriptor of the input data, and can distinguish robust and noisy topological properties. Nowadays, it is highly desired to develop a statistical framewo…
Confounding biases recommender systems, even when data seems fully observed.
problem Unmeasured features influencing both treatment and outcome in recommender systems.
method Illustrations and simulation studies showing how common practices introduce confounding.
result Standard recommender system practices can introduce confounding, reducing performance.
Topic models provide a useful method for dimensionality reduction and exploratory data analysis in large text corpora. Most approaches to topic model inference have been based on a maximum likelihood objective. Efficient algorithms exist that approximate this objective, but they have no provable guarantees. Recently, a…
Faced with massive data, is it possible to trade off (statistical) risk, and (computational) space and time? This challenge lies at the heart of large-scale machine learning. Using k-means clustering as a prototypical unsupervised learning problem, we show how we can strategically summarize the data (control space) in …
Domain adaptation provides a powerful set of model training techniques given domain-specific training data and supplemental data with unknown relevance. The techniques are useful when users need to develop models with data from varying sources, of varying quality, or from different time ranges. We build CrossTrainer, a…
This article provides an original understanding of the behavior of a class of graph-oriented semi-supervised learning algorithms in the limit of large and numerous data. It is demonstrated that the intuition at the root of these methods collapses in this limit and that, as a result, most of them become inconsistent. Co…
Develops methods to improve demand counterfactuals from imperfect proxies.
problem Imperfect proxies in demand models lead to biased counterfactuals and invalid inference.
method Practical toolkit for market-level and individual data, requiring minimal computation.
result Improves substitution prediction and counterfactual performance.
This paper introduces TDA and TSI for better business analytics.
problem Nonlinear, multi-scale business datasets under-represented by traditional tools.
method Topological Data Analysis (TDA) and Topological Stability Index (TSI).
result TSI reveals structural variability in business data.
Gradient Boosting Decision Trees (GBDTs) have become very successful in recent years, with many awards in machine learning and data mining competitions. There have been several recent studies on how to train GBDTs in the federated learning setting. In this paper, we focus on horizontal federated learning, where data sa…
Tree-based variational inference improves PLN model for hierarchical count data.
problem Limited applicability of PLN model in ecosystems due to lack of hierarchical tree structures.
method Introduced PLN-Tree model integrating structured variational inference techniques.
result Enhanced generative improvements and practical interpretability in microbiome modeling.
This paper provides a practical method to extract caplet volatilities from quoted data.
problem Extracting caplet volatilities from quoted data is complex and not straightforward.
method The paper presents a constructive algorithm based on criteria and robust outlier detection. It includes direct interpolation, bootstrap methods, and global search methods.
result The paper introduces methods to extract caplet volatilities that are arbitrage-free and consistent with quoted data.
Study shows data attribution methods are sensitive to hyperparameters, making tuning costly.
problem Hyperparameter sensitivity in data attribution methods makes tuning impractical.
method Theoretical analysis and lightweight procedure for selecting regularization value without retraining.
result Proposes a lightweight procedure for selecting regularization value without model retraining.
Recent research has helped to cultivate growing awareness that machine learning systems fueled by big data can create or exacerbate troubling disparities in society. Much of this research comes from outside of the practicing data science community, leaving its members with little concrete guidance to proactively addres…