標(biāo)題: Titlebook: Machine Learning and Knowledge Discovery in Databases: Research Track; European Conference, Danai Koutra,Claudia Plant,Francesco Bonchi Con [打印本頁] 作者: MASS 時(shí)間: 2025-3-21 19:28
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書目名稱Machine Learning and Knowledge Discovery in Databases: Research Track讀者反饋學(xué)科排名
作者: habitat 時(shí)間: 2025-3-21 22:41 作者: 任意 時(shí)間: 2025-3-22 01:37
Exploring the?Training Robustness of?Distributional Reinforcement Learning Against Noisy State Obserimal actions or even collapse while training. In this paper, we study the training robustness of distributional Reinforcement Learning?(RL), a class of state-of-the-art methods that estimate the whole distribution, as opposed to only the expectation, of the total return. Firstly, we validate the con作者: 令人心醉 時(shí)間: 2025-3-22 06:57 作者: 自由職業(yè)者 時(shí)間: 2025-3-22 10:51
Label Shift Quantification with?Robustness Guarantees via?Distribution Feature Matchingframework, distribution feature matching (DFM), that recovers as particular instances various estimators introduced in previous literature. We derive a general performance bound for DFM procedures, improving in several key aspects upon previous bounds derived in particular cases. We then extend this作者: Peculate 時(shí)間: 2025-3-22 16:13 作者: Antioxidant 時(shí)間: 2025-3-22 21:04
DualMatch: Robust Semi-supervised Learning with?Dual-Level Interactiong methods typically match model predictions of different data-augmented views in a single-level interaction manner, which highly relies on the quality of pseudo-labels and results in semi-supervised learning not robust. In this paper, we propose a novel SSL method called DualMatch, in which the clas作者: 使閉塞 時(shí)間: 2025-3-22 21:23 作者: Crepitus 時(shí)間: 2025-3-23 02:15
Deep Imbalanced Time-Series Forecasting via?Local Discrepancy Densitypite their scarce occurrences in the training set (., data imbalance), abrupt changes incur loss that significantly contributes to the total loss (., heteroscedasticity). Therefore, they act as noisy training samples and prevent the model from learning generalizable patterns, namely the normal state作者: instate 時(shí)間: 2025-3-23 09:05 作者: Thyroid-Gland 時(shí)間: 2025-3-23 10:01 作者: thwart 時(shí)間: 2025-3-23 17:29
Adacket: ADAptive Convolutional KErnel Transform for?Multivariate Time Series Classificationd also rely on the trial and error design of convolutional kernels, limiting comprehensive design space exploration. This hinders fully exploiting convolutional kernels for feature extraction from multivariate time series (MTS) data. To address this issue, we propose a novel method called Adaptive C作者: MUMP 時(shí)間: 2025-3-23 20:08 作者: 反叛者 時(shí)間: 2025-3-24 00:32
Estimating Dynamic Time Warping Distance Between Time Series with?Missing DataDTW cannot handle missing values, and simple fixes (e.g., dropping missing values, or interpolating) fail when entire intervals are missing, as is often the case with, e.g., temporary sensor or communication failures. There is hardly any research on how to address this problem. In this paper, we pro作者: 匍匐前進(jìn) 時(shí)間: 2025-3-24 06:10 作者: Anthem 時(shí)間: 2025-3-24 08:04
Weighted Multivariate Mean Reversion for?Online Portfolio Selectionlio selection has received increasing attention from both AI and machine learning communities. Mean reversion is an essential property of stock performance. Hence, most state-of-the-art online portfolio strategies have been built based on this. Though they succeed in specific datasets, most of the e作者: 并置 時(shí)間: 2025-3-24 14:09 作者: CHANT 時(shí)間: 2025-3-24 18:35 作者: Accrue 時(shí)間: 2025-3-24 19:34
ey et al., 3D microscopy deconvolution using Richardson-Lucy algorithm with total variation regularization, 2004), positron emission tomography (PET; Vardi et al., J Am Stat Assoc 80:8–20, 1985), or astronomical imaging (Lantéri and Theys, EURASIP J Appl Signal Processing 15:2500–2513, 2005). Here w作者: 微粒 時(shí)間: 2025-3-25 01:15
Cheng Liang,Di Wu,Yi He,Teng Huang,Zhong Chen,Xin Luoatabase design to show how design affects how you approach y.Learn to write SQL queries to select and analyze data, and improve your ability to manipulate data. This book will help you take your existing skills to the next level...Author Mark Simon kicks things off with a quick review of basic SQL k作者: 粗俗人 時(shí)間: 2025-3-25 05:10
Chi Hong,Jiyue Huang,Robert Birke,Lydia Y. Chen to the next level...Author Mark Simon kicks things off with a quick review of basic SQL knowledge, followed by a demonstration of how efficient SQL databases are designed and how to extract just the right data from them. You’ll then learn about each individual table’s structure and how to work with作者: AMPLE 時(shí)間: 2025-3-25 10:26
Moussa Kassem Sbeyti,Michelle Karg,Christian Wirth,Azarm Nowzad,Sahin Albayrakical economy and spatial disparities. Drawing on a complex systems framing, the book pulls together a range of evidence to provide insights about the agenda from macro, meso and micro levels of analyses, including utilising qualitative data from a small scoping study with Directors of Regeneration a作者: 離開可分裂 時(shí)間: 2025-3-25 15:04 作者: 冷漠 時(shí)間: 2025-3-25 16:24 作者: 量被毀壞 時(shí)間: 2025-3-25 21:21
Cong Wang,Xiaofeng Cao,Lanzhe Guo,Zenglin Shirstanding of the research area both approaches, qualitative and quantitative, are needed. This view is supported by Kaplan (1964, p. 207) who states that ‘Quantities are . qualities, and a measured quality . just the magnitude expressed in its measure.’ Miles and Huberman (1994, pp. 40ff.) see the d作者: transdermal 時(shí)間: 2025-3-26 00:38
Laurens Devos,Lorenzo Perini,Wannes Meert,Jesse Davisrstanding of the research area both approaches, qualitative and quantitative, are needed. This view is supported by Kaplan (1964, p. 207) who states that ‘Quantities are . qualities, and a measured quality . just the magnitude expressed in its measure.’ Miles and Huberman (1994, pp. 40ff.) see the d作者: Defraud 時(shí)間: 2025-3-26 06:07
n, vertical vergence, and cyclovergence. These couplings serve to guide involuntary motor responses during voluntary shifts of distance and direction of gaze without feedback from retinal image disparity. They function to optimize the disparity stimulus for stereoscopic depth perception, and they ca作者: COLIC 時(shí)間: 2025-3-26 11:04 作者: insightful 時(shí)間: 2025-3-26 13:09
Amal Saadallah,Matthias Jakobsn, vertical vergence, and cyclovergence. These couplings serve to guide involuntary motor responses during voluntary shifts of distance and direction of gaze without feedback from retinal image disparity. They function to optimize the disparity stimulus for stereoscopic depth perception, and they ca作者: Peculate 時(shí)間: 2025-3-26 19:45 作者: AUGER 時(shí)間: 2025-3-27 00:19 作者: reception 時(shí)間: 2025-3-27 03:12
Hongyang Su,Xiaolong Wang,Qingcai Chen,Yang Qin attempted to classify their positions. This has not been easy because most early authors and some modern authors are inexplicit on the crucial issues. There is not much evidence to help us decide between the different descriptions of the paradox. It is clear that classical SDI does not hold, and in作者: GUILT 時(shí)間: 2025-3-27 05:23
Aras Yurtman,Jonas Soenen,Wannes Meert,Hendrik Blockeeln, vertical vergence, and cyclovergence. These couplings serve to guide involuntary motor responses during voluntary shifts of distance and direction of gaze without feedback from retinal image disparity. They function to optimize the disparity stimulus for stereoscopic depth perception, and they ca作者: innate 時(shí)間: 2025-3-27 13:05 作者: dissent 時(shí)間: 2025-3-27 15:34 作者: PACT 時(shí)間: 2025-3-27 19:22 作者: Femine 時(shí)間: 2025-3-28 01:35 作者: 沒有貧窮 時(shí)間: 2025-3-28 06:05
Detecting Evasion Attacks in?Deployed Tree Ensemblesy additive tree ensemble and does not require training a separate model. We evaluate our approach on three different tree ensemble learners. We empirically show that our method is currently the best adversarial detection method for tree ensembles.作者: 合乎習(xí)俗 時(shí)間: 2025-3-28 09:34 作者: 不理會(huì) 時(shí)間: 2025-3-28 13:46
Robust Classification of?High-Dimensional Data Using Data-Adaptive Energy Distancearative performance of the proposed classifiers is also investigated. Our theoretical results are supported by extensive simulation studies and real data analysis, which demonstrate promising advantages of the proposed classification techniques over several widely recognized methods.作者: 剛毅 時(shí)間: 2025-3-28 18:05
0302-9743 ge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, in September 2023..The 196 papers were selected from the 829 submissions for the Research Track, and 58 papers were selected from the 239 submissions for the Applied Data Science Track.?.The volumes are organized in topical作者: Afflict 時(shí)間: 2025-3-28 20:39 作者: PACK 時(shí)間: 2025-3-29 00:20 作者: 背信 時(shí)間: 2025-3-29 04:20
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/m/image/620554.jpg作者: 偉大 時(shí)間: 2025-3-29 09:32
978-3-031-43423-5The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: LATE 時(shí)間: 2025-3-29 14:48 作者: 加花粗鄙人 時(shí)間: 2025-3-29 16:34
MMA: Multi-Metric-Autoencoder for?Analyzing High-Dimensional and?Incomplete Dataa better representation from a set of dispersed metric spaces. Extensive experiments on four real-world datasets demonstrate that our MMA significantly outperforms seven state-of-the-art models. Our code is available at the link 作者: maroon 時(shí)間: 2025-3-29 22:15
Exploring and?Exploiting Data-Free Model Stealing (i) substitute model which imitates the target model through synthetic queries and their inferred labels; and (ii) a tandem generator consisting of two networks, . and ., which first explores the synthetic data space via . and then exploits high-quality examples via . to maximize the knowledge tran作者: 蓋他為秘密 時(shí)間: 2025-3-30 01:15
Exploring the?Training Robustness of?Distributional Reinforcement Learning Against Noisy State Obser distributional RL loss based on the categorical parameterization equipped with the Kullback-Leibler?(KL) divergence. The resulting stable gradients while the optimization in distributional RL accounts for its better training robustness against state observation noises. Finally, extensive experiment作者: 和平主義 時(shí)間: 2025-3-30 07:40 作者: ELUDE 時(shí)間: 2025-3-30 11:11
Deep Imbalanced Time-Series Forecasting via?Local Discrepancy Density the temporal changes appear in the training set based on LD (., estimated LD density). Our reweighting framework is applicable to existing time-series forecasting models regardless of the architectures. Through extensive experiments on 12 time-series forecasting models over eight datasets with vari作者: 衰老 時(shí)間: 2025-3-30 14:11 作者: Malfunction 時(shí)間: 2025-3-30 19:54
Sparse Transformer Hawkes Process for?Long Event Sequences applied to the time series of aggregated event counts, primarily targeting the extraction of long-term periodic dependencies. Both components complement each other and are fused together to model the conditional intensity function of a point process for future event forecasting. Experiments on real作者: chandel 時(shí)間: 2025-3-30 22:57 作者: 有其法作用 時(shí)間: 2025-3-31 01:38
Efficient Adaptive Spatial-Temporal Attention Network for?Traffic Flow Forecastingically demonstrate the validity of the extension. Furthermore, we design an adaptive spatial-temporal fusion embedding scheme to generate heterogeneous and synchronous traffic states without pre-defined graph structures. We further propose an Efficient Adaptive Spatial-Temporal Attention Network (EA作者: 滔滔不絕地說 時(shí)間: 2025-3-31 07:32
Estimating Dynamic Time Warping Distance Between Time Series with?Missing Datam the same population). We show that, on multiple datasets, the proposed techniques outperform existing techniques in estimating pairwise DTW distances as well as in classification and clustering tasks based on these distances. The proposed techniques can enable many machine learning algorithms to m作者: glisten 時(shí)間: 2025-3-31 11:29
Uncovering Multivariate Structural Dependency for?Analyzing Irregularly Sampled Time Seriesh network that coherently captures structural interactions, learns time-aware dependencies, and handles challenging characteristics of IS-MTS data. Specifically, we first develop a multivariate interaction module that handles the frequent missing values and adaptively extracts graph structural relat作者: Nostalgia 時(shí)間: 2025-3-31 14:00
Weighted Multivariate Mean Reversion for?Online Portfolio SelectionMultivariate Mean Reversion” (WMMR) (Code is available at: .).. Empirical studies on various datasets show that WMMR has the ability to overcome the limitations of existing mean reversion algorithms and achieve superior results.作者: 俗艷 時(shí)間: 2025-3-31 20:15
H,-Nets: Hyper-hodge Convolutional Neural Networks for?Time-Series Forecastingces and, as a result, simultaneously extracts latent higher-order spatio-temporal dependencies. We provide theoretical foundations behind the proposed hyper-simplex-graph representation learning and validate our new Hodge-style Hyper-simplex-graph Neural Networks (H.-Nets) on 7 real world spatio-tem作者: 灌溉 時(shí)間: 2025-3-31 22:17
Conference proceedings 2023ptimization; Recommender Systems; Reinforcement Learning;?Representation Learning..Part V:.??Robustness; Time Series; Transfer and Multitask Learning..Part VI:.??Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare & Bioinformatics; Human-Computer Intera作者: peak-flow 時(shí)間: 2025-4-1 04:53
; Jonsson et al., Total variation regularization in positron emission tomography, 1998; Panin et al., IEEE Trans Nucl Sci 46(6):2202–2210, 1999). However, most of these algorithms for regularizations like TV lead to convergence problems for large regularization parameters, cannot guarantee positivit作者: abysmal 時(shí)間: 2025-4-1 09:11
