Machine Learning

Journal: Machine Learning

Abbreviation

Mach Learn

Publisher

Springer

ISSN

1573-0565
0885-6125

Show all metadata

Search Results

Publications1 - 10 of 15

Probabilistic clustering of time-evolving distance data
Vogt, Julia E.; Kloft, Marius; Stark, Stefan; et al. (2015)
Machine Learning
Bayesian optimization with safety constraints: safe and automatic parameter tuning in robotics
Berkenkamp, Felix; Krause, Andreas; Schoellig, Angela P. (2023)
Machine Learning
Selecting the right tuning parameters for algorithms is a pravelent problem in machine learning that can significantly affect the performance of algorithms. Data-efficient optimization algorithms, such as Bayesian optimization, have been used to automate this process. During experiments on real-world systems such as robotic platforms these methods can evaluate unsafe parameters that lead to safety-critical system failures and can destroy the system. Recently, a safe Bayesian optimization algorithm, called SafeOpt, has been developed, which guarantees that the performance of the system never falls below a critical value; that is, safety is defined based on the performance function. However, coupling performance and safety is often not desirable in practice, since they are often opposing objectives. In this paper, we present a generalized algorithm that allows for multiple safety constraints separate from the objective. Given an initial set of safe parameters, the algorithm maximizes performance but only evaluates parameters that satisfy safety for all constraints with high probability. To this end, it carefully explores the parameter space by exploiting regularity assumptions in terms of a Gaussian process prior. Moreover, we show how context variables can be used to safely transfer knowledge to new situations and tasks. We provide a theoretical analysis and demonstrate that the proposed algorithm enables fast, automatic, and safe optimization of tuning parameters in experiments on a quadrotor vehicle.
Weakly supervised change detection using guided anisotropic diffusion
Caye Daudt, Rodrigo; Le Saux, Bertrand; Boulch, Alexandre; et al. (2023)
Machine Learning
Large scale datasets created from crowdsourced labels or openly available data have become crucial to provide training data for large scale learning algorithms. While these datasets are easier to acquire, the data are frequently noisy and unreliable, which is motivating research on weakly supervised learning techniques. In this paper we propose original ideas that help us to leverage such datasets in the context of change detection. First, we propose the guided anisotropic diffusion (GAD) algorithm, which improves semantic segmentation results using the input images as guides to perform edge preserving filtering. We then show its potential in two weakly-supervised learning strategies tailored for change detection. The first strategy is an iterative learning method that combines model optimisation and data cleansing using GAD to extract the useful information from a large scale change detection dataset generated from open vector data. The second one incorporates GAD within a novel spatial attention layer that increases the accuracy of weakly supervised networks trained to perform pixel-level predictions from image-level labels. Improvements with respect to state-of-the-art are demonstrated on 4 different public datasets.
POMDP inference and robust solution via deep reinforcement learning: an application to railway optimal maintenance
Arcieri, Giacomo; Hoelzl, Cyprien; Schwery, Oliver; et al. (2024)
Machine Learning
Partially Observable Markov Decision Processes (POMDPs) can model complex sequential decision-making problems under stochastic and uncertain environments. A main reason hindering their broad adoption in real-world applications is the unavailability of a suitable POMDP model or a simulator thereof. Available solution algorithms, such as Reinforcement Learning (RL), typically benefit from the knowledge of the transition dynamics and the observation generating process, which are often unknown and non-trivial to infer. In this work, we propose a combined framework for inference and robust solution of POMDPs via deep RL. First, all transition and observation model parameters are jointly inferred via Markov Chain Monte Carlo sampling of a hidden Markov model, which is conditioned on actions, in order to recover full posterior distributions from the available data. The POMDP with uncertain parameters is then solved via deep RL techniques with the parameter distributions incorporated into the solution via domain randomization, in order to develop solutions that are robust to model uncertainty. As a further contribution, we compare the use of Transformers and long short-term memory networks, which constitute model-free RL solutions and work directly on the observation space, with an approach termed the belief-input method, which works on the belief space by exploiting the learned POMDP model for belief inference. We apply these methods to the real-world problem of optimal maintenance planning for railway assets and compare the results with the current real-life policy. We show that the RL policy learned by the belief-input method is able to outperform the real-life policy by yielding significantly reduced life-cycle costs.
Pairwise learning to rank by neural networks revisited: reconstruction, theoretical analysis and practical performance
Köppel, Marius; Segner, Alexander; Wagener, Martin; et al. (2025)
Machine Learning
We reevaluate the pairwise learning to rank approach based on neural nets, called RankNet, and present a theoretical analysis of its architecture. We show mathematically that the model can, under certain conditions, learn reflexive, antisymmetric, and transitive relations, enabling simplified training and improved performance. Experimental results on the LETOR MSLR-WEB10K, MQ2007 and MQ2008 datasets show that the model outperforms numerous state-of-the-art methods (including a listwise approach), while being inherently simpler in structure and using a pairwise approach only.
Efficient projection-free online convex optimization using stochastic gradients
Xie, Jiahao; Zhang, Chao; Shen, Zebang; et al. (2025)
Machine Learning
We consider Online Convex Optimization (OCO) problems subject to a compact convex set. An important class of projection-free online methods known as Frank–Wolfe-type (FW-type) methods have attracted considerable attention in the machine learning community, as they eschew the expensive projection operation and only require a simple linear minimization oracle in each round. Recently, the stochastic gradient technique has been integrated in FW-type online methods to circumvent the expensive full gradient computation and further reduce the per-round computational cost. However, these methods generally have high regret bounds due to high variance in gradient estimation. Although adopting a large minibatch in stochastic gradients can reduce the variance, it would in turn increase the per-round computational cost. In this paper, we develop efficient FW-type methods that only need stochastic gradients with small minibatch and achieve nearly optimal regret bounds with low per-round costs. We first explore the similarity between gradients of decision variables in consecutive rounds, and construct a lightweight variance-reduced estimator by utilizing historical gradient information. Based on this estimator, we propose a method named OFWRG for smooth problems in the stochastic setting. We prove that OFWRG achieves a nearly optimal regret bound with the lowest O(1) per-round computational cost. OFWRG is the first method with such nearly optimal result in this setting. We further extend OFWRG to OCO problems in other settings, including smooth problems in the adversarial setting and a class of non-smooth problems in the stochastic and adversarial settings. Our theoretical analyses show that these extensions of OFWRG achieve nearly optimal regret bounds and low per-round computational costs under mild conditions. Experimental results demonstrate the efficiency of our methods.
Navigating explanatory multiverse through counterfactual path geometry
Sokol, Kacper; Small, Edward; Xuan, Yueqing (2025)
Machine Learning
Counterfactual explanations are the de facto standard when tasked with interpreting decisions of (opaque) predictive models. Their generation is often subject to technical and domain-specific constraints that aim to maximise their real-life utility. In addition to considering desiderata pertaining to the counterfactual instance itself, guaranteeing existence of a viable path connecting it with the factual data point has recently gained relevance. While current explainability approaches ensure that the steps of such a journey as well as its destination adhere to selected constraints, they neglect the multiplicity of these counterfactual paths. To address this shortcoming we introduce the novel concept of explanatory multiverse that encompasses all the possible counterfactual journeys. We define it using vector spaces, showing how to navigate, reason about and compare the geometry of counterfactual trajectories found within it. To this end, we overview their spatial properties-such as affinity, branching, divergence and possible future convergence-and propose an all-in-one metric, called opportunity potential, to quantify them. Notably, the explanatory process offered by our method grants explainees more agency by allowing them to select counterfactuals not only based on their absolute differences but also according to the properties of their connecting paths. To demonstrate real-life flexibility, benefit and efficacy of explanatory multiverse we propose its graph-based implementation, which we use for qualitative and quantitative evaluation on six tabular and image data sets.
Boosting Poisson regression models with telematics car driving data
Gao, Guangyuan; Wang, He; Wüthrich, Mario V. (2021)
Machine Learning
With the emergence of telematics car driving data, insurance companies have started to boost classical actuarial regression models for claim frequency prediction with telematics car driving information. In this paper, we propose two data-driven neural network approaches that process telematics car driving data to complement classical actuarial pricing with a driving behavior risk factor from telematics data. Our neural networks simultaneously accommodate feature engineering and regression modeling which allows us to integrate telematics car driving data in a one-step approach into the claim frequency regression models. We conclude from our numerical analysis that both classical actuarial risk factors and telematics car driving data are necessary to receive the best predictive models. This emphasizes that these two sources of information interact and complement each other.
Conditional variance penalties and domain shift robustness
Heinze-Deml, Christina; Meinshausen, Nicolai (2021)
Machine Learning
When training a deep neural network for image classification, one can broadly distinguish between two types of latent features of images that will drive the classification. We can divide latent features into (i) ‘core’ or ‘conditionally invariant’ features C whose distribution C| Y, conditional on the class Y, does not change substantially across domains and (ii) ‘style’ features S whose distribution S| Y can change substantially across domains. Examples for style features include position, rotation, image quality or brightness but also more complex ones like hair color, image quality or posture for images of persons. Our goal is to minimize a loss that is robust under changes in the distribution of these style features. In contrast to previous work, we assume that the domain itself is not observed and hence a latent variable. We do assume that we can sometimes observe a typically discrete identifier or “ID variable”. In some applications we know, for example, that two images show the same person, and ID then refers to the identity of the person. The proposed method requires only a small fraction of images to have ID information. We group observations if they share the same class and identifier (Y, ID) = (y, id) and penalize the conditional variance of the prediction or the loss if we condition on (Y, ID). Using a causal framework, this conditional variance regularization (CoRe) is shown to protect asymptotically against shifts in the distribution of the style variables in a partially linear structural equation model. Empirically, we show that the CoRe penalty improves predictive accuracy substantially in settings where domain changes occur in terms of image quality, brightness and color while we also look at more complex changes such as changes in movement and posture.
Time varying undirected graphs
Zhou, Shuheng; Lafferty, John; Wasserman, Larry (2010)
Machine Learning

Publications1 - 10 of 15

Journal: Machine Learning

Abbreviation

Publisher

Journal Volumes

ISSN

Description

Filters

Search Results