976 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Centre National de la Recherche ScientifiqueFR49 papers
- Technical University of DenmarkDK38 papers
- Aarhus UniversityDK29 papers
- University of CopenhagenDK23 papers
- Tokyo Institute of TechnologyJP19 papers
- University of ManchesterGB18 papers
- École Polytechnique Fédérale de LausanneCH17 papers
- University of OxfordGB16 papers
- Centre de Physique ThéoriqueFR14 papers
- East China Normal UniversityCN14 papers
- IMAR - Institutul de Matematică „Simion Stoilow” al Academiei RomâneRO14 papers
- Imperial College LondonGB14 papers
70 papers · 1 filter
Adaptive Probabilistic Shielding by Learning MDPs for Safe Reinforcement Learning
Astrid Horn Brorholt, Maris F. L. Galesloot, Nils Jansen +2
Probabilistic shielding is a technique for safe reinforcement learning (RL). Typically, a static observer -- called the shield -- constrains the learning agent's actions to those f…
TiWeaver: Unified Temporal Dynamics Modeling via Contextual Patching
Zhe Li, Jindong Tian, Hao Miao +3
Multivariate time series forecasting plays a critical role in real-world applications, including weather prediction, stock analysis, and health monitoring. Due to the diversity of…
TCRL: Temporal-Coupled Adversarial Training for Robust Constrained Reinforcement Learning in Worst-Case Scenarios
Wentao Xu, Zhongming Yao, Weihao Li +4
Constrained Reinforcement Learning (CRL) aims to optimize decision-making policies under constraint conditions, making it highly applicable to safety-critical domains such as auton…
SRLR: Symbolic Regression based Logic Recovery to Counter Programmable Logic Controller Attacks
Hao Zhou, Suman Sourav, Binbin Chen +1
Programmable Logic Controllers (PLCs) are critical components in Industrial Control Systems (ICSs). Their potential exposure to external world makes them susceptible to cyber-attac…
STDiff: A State Transition Diffusion Framework for Time Series Imputation in Industrial Systems
Gary Simethy, Daniel Ortiz-Arroyo, Petar Durdevic
Incomplete sensor data is a major obstacle in industrial time-series analytics. In wastewater treatment plants (WWTPs), key sensors show long, irregular gaps caused by fouling, mai…
Finding Counterfactual Evidences for Node Classification
Dazhuo Qiu, Jinwen Chen, Arijit Khan +2
Counterfactual learning is emerging as an important paradigm, rooted in causality, which promises to alleviate common issues of graph neural networks (GNNs), such as fairness and i…
An approach to melodic segmentation and classification based on filtering with the Haar-wavelet
Gissel Velarde, Tillman Weyde, David Meredith
We present a novel method of classification and segmentation of melodies in symbolic representation. The method is based on filtering pitch as a signal over time with the Haar-wave…
Humanity's Last Exam
Long Phan, Alice Gatti, Ziwen Han +1168
Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achi…
TEAM: Topological Evolution-aware Framework for Traffic Forecasting--Extended Version
Duc Kieu, Tung Kieu, Peng Han +3
Due to the global trend towards urbanization, people increasingly move to and live in cities that then continue to grow. Traffic forecasting plays an important role in the intellig…
Revisiting K-mer Profile for Effective and Scalable Genome Representation Learning
Abdulkadir Celikkanat, Andres R. Masegosa, Thomas D. Nielsen
Obtaining effective representations of DNA sequences is crucial for genome analysis. Metagenomic binning, for instance, relies on genome representations to cluster complex mixtures…
Federated Neural Nonparametric Point Processes
Hui Chen, Xuhui Fan, Hengyu Liu +5
Temporal point processes (TPPs) are effective for modeling event occurrences over time, but they struggle with sparse and uncertain events in federated systems, where privacy is a…
AIDE: Antithetical, Intent-based, and Diverse Example-Based Explanations
Ikhtiyor Nematov, Dimitris Sacharidis, Tomer Sagi +1
For many use-cases, it is often important to explain the prediction of a black-box model by identifying the most influential training data samples. Existing approaches lack customi…
The Reachability Problem for Neural-Network Control Systems
Christian Schilling, Martin Zimmermann
A control system consists of a plant component and a controller which periodically computes a control input for the plant. We consider systems where the controller is implemented b…
BIOSCAN-5M: A Multimodal Dataset for Insect Biodiversity
Zahra Gharaee, Scott C. Lowe, ZeMing Gong +10
As part of an ongoing worldwide effort to comprehend and monitor insect biodiversity, this paper presents the BIOSCAN-5M Insect dataset to the machine learning community and establ…
Interpretable Network Visualizations: A Human-in-the-Loop Approach for Post-hoc Explainability of CNN-based Image Classification
Matteo Bianchi, Antonio De Santis, Andrea Tocchetti +1
Transparency and explainability in image classification are essential for establishing trust in machine learning models and detecting biases and errors. State-of-the-art explainabi…
FedSI: Federated Subnetwork Inference for Efficient Uncertainty Quantification
Hui Chen, Hengyu Liu, Zhangkai Wu +2
While deep neural networks (DNNs) based personalized federated learning (PFL) is demanding for addressing data heterogeneity and shows promising performance, existing methods for f…
QCore: Data-Efficient, On-Device Continual Calibration for Quantized Models -- Extended Version
David Campos, Bin Yang, Tung Kieu +3
We are witnessing an increasing availability of streaming data that may contain valuable information on the underlying processes. It is thus attractive to be able to deploy machine…
Pathformer: Multi-scale Transformers with Adaptive Pathways for Time Series Forecasting
Peng Chen, Yingying Zhang, Yunyao Cheng +5
Transformers for time series forecasting mainly model time series from limited or fixed scales, making it challenging to capture different characteristics spanning various scales.…
Domain Adaptation for Time series Transformers using One-step fine-tuning
Subina Khanal, Seshu Tirupathi, Giulio Zizzo +2
The recent breakthrough of Transformers in deep learning has drawn significant attention of the time series community due to their ability to capture long-range dependencies. Howev…
View-based Explanations for Graph Neural Networks
Tingyang Chen, Dazhuo Qiu, Yinghui Wu +3
Generating explanations for graph neural networks (GNNs) has been studied to understand their behavior in analytical tasks such as graph classification. Existing approaches aim to…
PAC-Bayes Generalisation Bounds for Dynamical Systems Including Stable RNNs
Deividas Eringis, John Leth, Zheng-Hua Tan +2
In this paper, we derive a PAC-Bayes bound on the generalisation gap, in a supervised time-series setting for a special class of discrete-time non-linear dynamical systems. This cl…
Greedy Shapley Client Selection for Communication-Efficient Federated Learning
Pranava Singhal, Shashi Raj Pandey, Petar Popovski
The standard client selection algorithms for Federated Learning (FL) are often unbiased and involve uniform random sampling of clients. This has been proven sub-optimal for fast co…
Privacy-Aware Data Acquisition under Data Similarity in Regression Markets
Shashi Raj Pandey, Pierre Pinson, Petar Popovski
Data markets facilitate decentralized data exchange for applications such as prediction, learning, or inference. The design of these markets is challenged by varying privacy prefer…
An Efficient Federated Learning Framework for Training Semantic Communication System
Loc X. Nguyen, Huy Q. Le, Ye Lin Tun +4
Semantic communication has emerged as a pillar for the next generation of communication systems due to its capabilities in alleviating data redundancy. Most semantic communication…