GenAI4ELab/papercli-papers huggingface dataset:
Random Smoothing Regularization in Kernel Gradient Descent Learning
Authors: Liang Ding; Tianyang Hu; Jiahang Jiang; Donghao Li; Wenjia Wang; Yuan Yao
Venue: JMLR 2024 (main)

Abstract: Random smoothing data augmentation is a unique form of regularization that can prevent overfitting by introducing noise to the input data, encouraging the model to learn more generalized features. Despite its success in various applications, there has been a lack of systematic study on the regularization ability of random smoothing. In this paper, we aim to bridge this gap by presenting a framework for random smoothing regularization that can adaptively and effectively learn a wide range of ground truth functions belonging to the classical Sobolev spaces. Specifically, we investigate two underlying function spaces: the Sobolev space of low intrinsic dimension, which includes the Sobolev space in D-dimensional Euclidean space or low-dimensional sub-manifolds as special cases, and the mixed smooth Sobolev space with a tensor structure. By using random smoothing regularization as novel convolution-based smoothing kernels, we can attain optimal convergence rates in these cases using a kernel gradient descent algorithm, either with early stopping or weight decay. It is noteworthy that our estimator can adapt to the structural assumptions of the underlying data and avoid the curse of dimensionality. This is achieved through various choices of injected noise distributions such as Gaussian, Laplace, or general polynomial noises, allowing for broad adaptation to the aforementioned structural assumptions of the underlying data. The convergence rate depends only on the effective dimension, which may be significantly smaller than the actual data dimension. We conduct numerical experiments on simulated data to validate our theoretical results.
OmniSafe: An Infrastructure for Accelerating Safe Reinforcement Learning Research
Authors: Jiaming Ji; Jiayi Zhou; Borong Zhang; Juntao Dai; Xuehai Pan; Ruiyang Sun; Weidong Huang; Yiran Geng; Mickel Liu; Yaodong Yang
Venue: JMLR 2024 (main)

Abstract: AI systems empowered by reinforcement learning (RL) algorithms harbor the immense potential to catalyze societal advancement, yet their deployment is often impeded by significant safety concerns. Particularly in safety-critical applications, researchers have raised concerns about unintended harms or unsafe behaviors of unaligned RL agents. The philosophy of safe reinforcement learning (SafeRL) is to align RL agents with harmless intentions and safe behavioral patterns. In SafeRL, agents learn to develop optimal policies by receiving feedback from the environment, while also fulfilling the requirement of minimizing the risk of unintended harm or unsafe behavior. However, due to the intricate nature of SafeRL algorithm implementation, combining methodologies across various domains presents a formidable challenge. This had led to an absence of a cohesive and efficacious learning framework within the contemporary SafeRL research milieu. In this work, we introduce a foundational framework designed to expedite SafeRL research endeavors. Our comprehensive framework encompasses an array of algorithms spanning different RL domains and places heavy emphasis on safety elements. Our efforts are to make the SafeRL-related research process more streamlined and efficient, therefore facilitating further research in AI safety.
Measuring Sample Quality in Algorithms for Intractable Normalizing Function Problems
Authors: Bokgyeong Kang; John Hughes; Murali Haran
Venue: JMLR 2024 (main)

Abstract: Models with intractable normalizing functions have numerous applications. Because the normalizing constants are functions of the parameters of interest, standard Markov chain Monte Carlo cannot be used for Bayesian inference for these models. A number of algorithms have been developed for such models. Some have the posterior distribution as their asymptotic distribution. Other "asymptotically inexact" algorithms do not possess this property. There is limited guidance for evaluating approximations based on these algorithms. Hence it is very hard to tune them. We propose two new diagnostics that address these problems for intractable normalizing function models. Our first diagnostic, inspired by the second Bartlett identity, is in principle broadly applicable to Monte Carlo approximations beyond the normalizing function problem. We develop an approximate version of this diagnostic that is applicable to intractable normalizing function problems. Our second diagnostic is a Monte Carlo approximation to a kernel Stein discrepancy-based diagnostic introduced by Gorham and Mackey (2017). We provide theoretical justification for our methods and apply them to several algorithms in challenging simulated and real data examples including an Ising model, an exponential random graph model, and a Conway-Maxwell-Poisson regression model, obtaining interesting insights about the algorithms in these contexts.
Contamination-source based K-sample clustering
Authors: Xavier Milhaud; Denys Pommeret; Yahia Salhi; Pierre Vandekerkhove
Venue: JMLR 2024 (main)

Abstract: In this work, we investigate the $K$-sample clustering of populations subject to contamination phenomena. A contamination model is a two-component mixture model where one component is known (standard behaviour) and the second component, modeling a departure from the standard behaviour, is unknown.When $K$ populations from such a model are observed we propose a semiparametric clustering methodology to detect which populations are impacted by the same type of contamination, with the aim of faciliting coordinated diagnosis and best practices sharing. We prove the consistency of our approach under the assumption of the existence of true clusters and demonstrate the performances of our methodology through an extensive Monte Carlo study. Finally, we apply our methodology, implemented in the R admix package, to a European countries COVID-19 excess of mortality dataset, aiming to cluster countries similarly impacted by the pandemic across different age groups.
Compressed and distributed least-squares regression: convergence rates with applications to federated learning
Authors: Constantin Philippenko; Aymeric Dieuleveut
Venue: JMLR 2024 (main)

Abstract: In this paper, we investigate the impact of compression on stochastic gradient algorithms for machine learning, a technique widely used in distributed and federated learning. We underline differences in terms of convergence rates between several unbiased compression operators, that all satisfy the same condition on their variance, thus going beyond the classical worst-case analysis. To do so, we focus on the case of least-squares regression (LSR) and analyze a general stochastic approximation algorithm for minimizing quadratic functions relying on a random field. We consider weak assumptions on the random field, tailored to the analysis (specifically, expected HÃ¶lder regularity), and on the noise covariance, enabling the analysis of various randomizing mechanisms, including compression. We then extend our results to the case of federated learning. More formally, we highlight the impact on the convergence of the covariance $\mathfrak{C}_{\mathrm{ania}}$ of the additive noise induced by the algorithm. We demonstrate despite the non-regularity of the stochastic field, that the limit variance term scales with $\mathrm{Tr}(\mathfrak{C}_{\mathrm{ania}} H^{-1})/K$ (where $H$ is the Hessian of the optimization problem and $K$ the number of iterations) generalizing the rate for the vanilla LSR case where it is $\sigma^2 \mathrm{Tr}(H H^{-1}) / K = \sigma^2 d / K$ (Bach and Moulines, 2013). Then, we analyze the dependency of $\mathfrak{C}_{\mathrm{ania}}$ on the compression strategy and ultimately its impact on convergence, first in the centralized case, then in two heterogeneous FL frameworks.
aeon: a Python Toolkit for Learning from Time Series
Authors: Matthew Middlehurst; Ali Ismail-Fawaz; Antoine Guillaume; Christopher Holder; David Guijo-Rubio; Guzal Bulatova; Leonidas Tsaprounis; Lukasz Mentel; Martin Walter; Patrick SchÃ¤fer; Anthony Bagnall
Venue: JMLR 2024 (main)

Abstract: aeon is a unified Python 3 library for all machine learning tasks involving time series. The package contains modules for time series forecasting, classification, extrinsic regression and clustering, as well as a variety of utilities, transformations and distance measures designed for time series data. aeon also has a number of experimental modules for tasks such as anomaly detection, similarity search and segmentation. aeon follows the scikit-learn API as much as possible to help new users and enable easy integration of aeon estimators with useful tools such as model selection and pipelines. It provides a broad library of time series algorithms, including efficient implementations of the very latest advances in research. Using a system of optional dependencies, aeon integrates a wide variety of packages into a single interface while keeping the core framework with minimal dependencies. The package is distributed under the 3-Clause BSD license and is available at https://github.com/aeon-toolkit/aeon.
skscope: Fast Sparsity-Constrained Optimization in Python
Authors: Zezhi Wang; Junxian Zhu; Xueqin Wang; Jin Zhu; Huiyang Pen; Peng Chen; Anran Wang; Xiaoke Zhang
Venue: JMLR 2024 (main)

Abstract: Applying iterative solvers on sparsity-constrained optimization (SCO) requires tedious mathematical deduction and careful programming/debugging that hinders these solvers' broad impact. In the paper, the library skscope is introduced to overcome such an obstacle. With skscope, users can solve the SCO by just programming the objective function. The convenience of skscope is demonstrated through two examples in the paper, where sparse linear regression and trend filtering are addressed with just four lines of code. More importantly, skscope's efficient implementation allows state-of-the-art solvers to quickly attain the sparse solution regardless of the high dimensionality of parameter space. Numerical experiments reveal the available solvers in skscope can achieve up to 80x speedup on the competing relaxation solutions obtained via the benchmarked convex solver. skscope is published on the Python Package Index (PyPI) and Conda, and its source code is available at: https://github.com/abess-team/skscope.
Studying the Interplay between Information Loss and Operation Loss in Representations for Classification
Authors: Jorge F. Silva; Felipe Tobar; Mario VicuÃ±a; Felipe Cordova
Venue: JMLR 2024 (main)

Abstract: Information-theoretic measures have been widely adopted for machine learning (ML) feature design. Inspired by this, we look at the relationship between information loss in the Shannon sense and the operation loss in the minimum probability of error (MPE) sense when considering a family of lossy representations. Our first result offers a lower bound on a weak form of information loss as a function of its respective operation loss when adopting a discrete encoder. When considering a general family of lossy continuous representations, we show that a form of vanishing information loss (a weak informational sufficiency (WIS)) implies a vanishing MPE loss. Our findings support the observation that selecting/designing representations that capture informational sufficiency is appropriate for learning. However, this selection is rather conservative if the intended goal is achieving MPE in classification. Supporting this, we show that it is possible to adopt an alternative notion of informational sufficiency (strictly weaker than pure sufficiency in the mutual information sense) to achieve operational sufficiency in learning. Furthermore, our new WIS condition is used to demonstrate the expressive power of digital encoders and the capacity of two existing compression-based algorithms to achieve lossless prediction in ML.
Non-splitting Neyman-Pearson Classifiers
Authors: Jingming Wang; Lucy Xia; Zhigang Bao; Xin Tong
Venue: JMLR 2024 (main)

Abstract: The Neyman-Pearson (NP) binary classification paradigm constrains the more severe type of error (e.g., the type I error) under a preferred level while minimizing the other (e.g., the type II error). This paradigm is suitable for applications such as severe disease diagnosis, fraud detection, among others. A series of NP classifiers have been developed to guarantee the type I error control with high probability.  However, these existing classifiers involve a sample splitting step: a mixture of class 0 and class 1 observations to construct a scoring function and some left-out class 0 observations to construct a threshold. This splitting enables classifier threshold construction built upon independence, but it amounts to insufficient use of data for training and a potentially higher type II error.  Leveraging a canonical linear discriminant analysis (LDA) model, we derive a quantitative CLT for a certain functional of quadratic forms of the inverse of sample and population covariance matrices,  and based on this result,  develop for the first time NP classifiers without splitting the training sample.  Numerical experiments have confirmed the advantages of our new non-splitting parametric strategy.
Learning and scoring Gaussian latent variable causal models with unknown additive interventions
Authors: Armeen Taeb; Juan L. Gamella; Christina Heinze-Deml; Peter BÃ¼hlmann
Venue: JMLR 2024 (main)

Abstract: With observational data alone, causal structure learning is a challenging problem. The task becomes easier when having access to data collected from perturbations of the underlying system, even when the nature of these is unknown. Existing methods either do not allow for the presence of latent variables or assume that these remain unperturbed. However, these assumptions are hard to justify if the nature of the perturbations is unknown. We provide results that enable scoring causal structures in the setting with additive, but unknown interventions. Specifically, we propose a maximum-likelihood estimator in a structural equation model that exploits system-wide invariances to output an equivalence class of causal structures from perturbation data. Furthermore, under certain structural assumptions on the population model, we provide a simple graphical characterization of all the DAGs in the interventional equivalence class. We illustrate the utility of our framework on synthetic data as well as real data involving California reservoirs and protein expressions. The software implementation is available as the Python package utlvce.
An Asymptotic Study of Discriminant and Vote-Averaging Schemes for Randomly-Projected Linear Discriminants
Authors: Lama B. Niyazi; Abla Kammoun; Hayssam Dahrouj; Mohamed-Slim Alouini; Tareq Y. Al-Naffouri
Venue: JMLR 2024 (main)

Abstract: Modern technology has contributed to the rise of high-dimensional data in various domains such as bio-informatics, chemometrics, and face recognition. In the recent literature, random projections and, in particular, randomly-projected ensembles based on the classical Linear Discriminant Analysis (LDA), have been proposed for classification problems involving such high-dimensional data. In this work, we study the two main classes of randomly-projected LDA ensemble classifiers, namely discriminant averaging and vote averaging. Through asymptotic analysis in a growth regime where the problem dimensions are assumed to grow at constant rates to each other for a fixed ensemble size, we determine the exact mechanism through which the ensemble size affects the classification performance. Furthermore, we investigate whether projection selection truly matters in an ensemble setting, and, ultimately, derive the optimal form of the randomly-projected LDA ensemble. Motivated by these findings, we propose a framework for efficient tuning of the optimal classifier's ensemble size and projection dimension based on an estimator of the classifier probability of misclassification which is consistent under the assumed growth regime. The proposed framework is shown to outperform the existing rule-of-thumb, as well as other methods for parameter tuning, on both real and synthetic data.
Evidence Estimation in Gaussian Graphical Models Using a Telescoping Block Decomposition of the Precision Matrix
Authors: Anindya Bhadra; Ksheera Sagar; David Rowe; Sayantan Banerjee; Jyotishka Datta
Venue: JMLR 2024 (main)

Abstract: Marginal likelihood, also known as model evidence, is a fundamental quantity in Bayesian statistics. It is used for model selection using Bayes factors or for empirical Bayes tuning of prior hyper-parameters. Yet, the calculation of evidence has remained a longstanding open problem in Gaussian graphical models. Currently, the only feasible solutions that exist are for special cases such as the Wishart or G-Wishart, in moderate dimensions. We develop an approach based on a novel telescoping block decomposition of the precision matrix that allows the estimation of evidence by application of Chib's technique under a very broad class of priors under mild requirements. Specifically, the requirements are: (a) the priors on the diagonal terms on the precision matrix can be written as gamma or scale mixtures of gamma random variables and (b) those on the off-diagonal terms can be represented as normal or scale mixtures of normal. This includes structured priors such as the Wishart or G-Wishart, and more recently introduced element-wise priors, such as the Bayesian graphical lasso and the graphical horseshoe. Among these, the true marginal is known in an analytically closed form for Wishart, providing a useful validation of our approach. For the general setting of the other three, and several more priors satisfying conditions (a) and (b) above, the calculation of evidence has remained an open question that this article resolves under a unifying framework.
PyPop7: A Pure-Python Library for Population-Based Black-Box Optimization
Authors: Qiqi Duan; Guochen Zhou; Chang Shao; Zhuowei Wang; Mingyang Feng; Yuwei Huang; Yajing Tan; Yijun Yang; Qi Zhao; Yuhui Shi
Venue: JMLR 2024 (main)

Abstract: In this paper, we present an open-source pure-Python library called PyPop7 for black-box optimization (BBO). As population-based methods (e.g., evolutionary algorithms, swarm intelligence, and pattern search) become increasingly popular for BBO, the design goal of PyPop7 is to provide a unified API and elegant implementations for them, particularly in challenging high-dimensional scenarios. Since these population-based methods easily suffer from the notorious curse of dimensionality owing to random sampling as one of core operations for most of them, recently various improvements and enhancements have been proposed to alleviate this issue more or less mainly via exploiting possible problem structures: such as, decomposition of search distribution or space, low-memory approximation, low-rank metric learning, variance reduction, ensemble of random subspaces, model self-adaptation, and fitness smoothing. These novel sampling strategies could better exploit different problem structures in high-dimensional search space and therefore they often result in faster rates of convergence and/or better qualities of solution for large-scale BBO. Now PyPop7 has covered many of these important advances on a set of well-established BBO algorithm families and also provided an open-access interface to adding the latest or missed black-box optimizers for further functionality extensions. Its well-designed source code (under GPL-3.0 license) and full-fledged online documents (under CC-BY 4.0 license) have been freely available at https://github.com/Evolutionary-Intelligence/pypop and https://pypop.readthedocs.io, respectively.
Optimistic Search: Change Point Estimation for Large-scale Data via Adaptive Logarithmic Queries
Authors: Solt KovÃ¡cs; Housen Li; Lorenz Haubner; Axel Munk; Peter BÃ¼hlmann
Venue: JMLR 2024 (main)

Abstract: Change point estimation is often formulated as a search for the maximum of a gain function describing improved fits when segmenting the data. Searching one change point through all candidates requires $O(n)$ evaluations of the gain function for an interval with $n$ observations. If each evaluation is computationally demanding (e.g. in high-dimensional models), this can become infeasible. Instead, we propose optimistic search, a methodology that only requires $O(\log n)$ evaluations of the gain function, leading to huge computational gains for massive (large-scale, high-dimensional) data for single and multiple change point estimation. Towards solid understanding of our strategy, we investigate in detail the $p$-dimensional Gaussian changing means setup, including high-dimensional scenarios. For some of our proposals, we prove asymptotic minimax optimality for detecting change points and derive sharp asymptotic rates for localizing change points. Our search strategy generalizes far beyond the theoretically analyzed setup. We illustrate, as an example, massive computational speedup in change point detection for high-dimensional Gaussian graphical models.
Value-Distributional Model-Based Reinforcement Learning
Authors: Carlos E. Luis; Alessandro G. Bottero; Julia Vinogradska; Felix Berkenkamp; Jan Peters
Venue: JMLR 2024 (main)

Abstract: Quantifying uncertainty about a policy's long-term performance is important to solve sequential decision-making tasks. We study the problem from a model-based Bayesian reinforcement learning perspective, where the goal is to learn the posterior distribution over value functions induced by parameter (epistemic) uncertainty of the Markov decision process. Previous work restricts the analysis to a few moments of the distribution over values or imposes a particular distribution shape, e.g., Gaussians. Inspired by distributional reinforcement learning, we introduce a Bellman operator whose fixed-point is the value distribution function. Based on our theory, we propose Epistemic Quantile-Regression (EQR), a model-based algorithm that learns a value distribution function. We combine EQR with soft actor-critic (SAC) for policy optimization with an arbitrary differentiable objective function of the learned value distribution. Evaluation across several continuous-control tasks shows performance benefits with respect to both model-based and model-free algorithms. The code is available at https://github.com/boschresearch/dist-mbrl.
Commutative Scaling of Width and Depth in Deep Neural Networks
Authors: Soufiane Hayou
Venue: JMLR 2024 (main)

Abstract: In this paper, we study the commutativity of infinite width and depth limits in deep neural networks. Our aim is to understand the behavior of neural functions (functions that depend on a neural network model) as width and depth go to infinity (in some sense), and eventually identify settings under which commutativity holds, i.e. the neural function tends to the same limit no matter how width and depth limits are taken. In this paper, we formally introduce and define the commutativity framework, and discuss its implications on neural network design and scaling. We study commutativity for the neural covariance kernel which reflects how network layers separate data. Our findings extend previous results established in Hayou and Yang (2023) by showing that taking the width and depth to infinity in a deep neural network with skip connections, when branches are suitably scaled to avoid exploding behavior, result in the same covariance structure no matter how that limit is taken. This has a number of theoretical and practical implications that we discuss in the paper. The proof techniques in this paper are new and rely on tools that are more accessible to readers who are not familiar with stochastic calculus (used in the proofs of Hayou and Yang (2023)).