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๐Ÿ” avoidance learning ๐Ÿ“‚ Computer Science
Showing 46580 results for "avoidance learning" in Computer Science
Computer Science Preprint PDF DOI

Community Detection with the Canonical Ensemble

Rudy Arthur ยท 2026

Network community detection is usually considered as an unsupervised learning problem. Given a network, the aim is to partition it using some general purpose algorithm. In this paper we instead treat โ€ฆ

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CS3: Efficient Online Capability Synergy for Two-Tower Recommendation

Lixiang Wang, Shaoyun Shi, Peng Wang, Wenjin Wu, Peng Jiang ยท 2026

To balance effectiveness and efficiency in recommender systems, multi-stage pipelines commonly use lightweight two-tower models for large-scale candidate retrieval. However, the isolated two-tower arcโ€ฆ

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Computer Science Preprint PDF DOI

BONSAI: A Mixed-Initiative Workspace for Human-AI Co-Development of Visual Analytics Applications

Thilo Spinner, Matthias Miller, Fabian Sperrle-Roth, Mennatallah El-Assady ยท 2026

Developing Visual Analytics (VA) applications requires integrating complex machine learning models with expressive interactive interfaces. Developers face a stark trade-off: building tightly-coupled mโ€ฆ

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Computer Science Preprint PDF DOI

Sherpa.ai Privacy-Preserving Multi-Party Entity Alignment without Intersection Disclosure for Noisy Identifiers

Daniel M. Jimenez-Gutierrez, Enrique Zuazua, Georgios Kellaris, Joaquin Del Rio, Oleksii Sliusarenko, Xabi Uribe-Etxebarria ยท 2026

Federated Learning (FL) enables collaborative model training among multiple parties without centralizing raw data. There are two main paradigms in FL: Horizontal FL (HFL), where all participants shareโ€ฆ

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Towards More Empathic Programming Environments: An Experimental Empathic AI-Enhanced IDE

Justin Rainier Go, Kurt Christian Andaya, Roemer Gabriel Caliboso, Aaron Daniel Go, Jocelynn Cu ยท 2026

As generative AI becomes integral to software development, the risk of over-reliance and diminished critical thinking grows. This study introduces "Ceci," our Caring Empathic C IDE designed to supportโ€ฆ

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GraphRAG-IRL: Personalized Recommendation with Graph-Grounded Inverse Reinforcement Learning and LLM Re-ranking

Siqi Liang, Xiawei Wang, Yudi Zhang, Jiaying Zhou ยท 2026

Personalized recommendation requires models that capture sequential user preferences while remaining robust to sparse feedback and semantic ambiguity. Recent work has explored large language models (Lโ€ฆ

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DP-FlogTinyLLM: Differentially private federated log anomaly detection using Tiny LLMs

Isaiah Thompson, Tanmay Sen, Ritwik Bhattacharya ยท 2026

Modern distributed systems generate massive volumes of log data that are critical for detecting anomalies and cyber threats. However, in real world settings, these logs are often distributed across muโ€ฆ

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LIVE: Learnable Monotonic Vertex Embedding for Efficient Exact Subgraph Matching (Technical Report)

Yutong Ye, Weilong Ren, Yang Liu, Mengyi Yan, Ruijie Wang, Li Sun, Jianxin Li, Philip S. Yu ยท 2026

Exact subgraph matching is a fundamental graph operator that supports many graph analytics tasks, yet it remains computationally challenging due to its NP-completeness. Recent learning-based approacheโ€ฆ

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Design Rules for Extreme-Edge Scientific Computing on AI Engines

Zhenghua Ma, G Abarajithan, Dimitrios Danopoulos, Olivia Weng, Francesco Restuccia, Ryan Kastner ยท 2026

Extreme-edge scientific applications use machine learning models to analyze sensor data and make real-time decisions. Their stringent latency and throughput requirements demand small batch sizes and rโ€ฆ

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Relational AI in Education: Reciprocity, Participatory Design, and Indigenous Worldviews

Roberto Martinez-Maldonado, Vanessa Echeverria, Jenna Hawes, YJ Kim, Zara Maddigan, Mikaela Milesi, Todd Nelson, Yi-Shan Tsai ยท 2026

Education is not merely the transmission of information or the optimisation of individual performance; it is a fundamentally social, constructive, and relational practice. However, recent advances in โ€ฆ

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Last-Iterate Guarantees for Learning in Co-coercive Games

Siddharth Chandak, Ramanan Tamizholi, Nicholas Bambos ยท 2026

We establish finite-time last-iterate guarantees for vanilla stochastic gradient descent in co-coercive games under noisy feedback. This is a broad class of games that is more general than strongly moโ€ฆ

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CHRONOS: A Hardware-Assisted Phase-Decoupled Framework for Secure Federated Learning in IoT

Hung Dang ยท 2026

We propose CHRONOS, a hardware-assisted framework that decouples the cryptographic setup required for private gradient aggregation from the active training phase. CHRONOS executes a once-per-epoch serโ€ฆ

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Analysis of AWW (Anganwadi Workers) Training Content, ILA (Incremental Learning Approach) Modules Following CDT (Component Display Theory)

Arka Majhi, Satish B. Agnihotri ยท 2026

POSHAN Abhiyan envisages capacity building of AWWs or frontline health workers through 21 training modules of ILA (Incremental Learning Approach), modularising the net learning content into smaller leโ€ฆ

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Security Is Relative: Training-Free Vulnerability Detection via Multi-Agent Behavioral Contract Synthesis

Yongchao Wang, Zhiqiu Huang ยท 2026

Deep learning for vulnerability detection has shown promising results on early benchmarks, but recent evaluations reveal catastrophic degradation: models achieving F1 > 0.68 on legacy datasets collapsโ€ฆ

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Ocean: Fast Estimation-Based Sparse General Matrix-Matrix Multiplication on GPU

Yifan Li, Giulia Guidi ยท 2026

In computational science and data analytics, many workloads involve irregular and sparse computations that are inherently difficult to optimize for modern hardware. A key kernel is Sparse General Matrโ€ฆ

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Physical and Augmented Reality based Playful Activities for Refresher Training of ASHA Workers in India

Arka Majhi, Satish B. Agnihotri, Aparajita Mondal ยท 2026

Recent health surveys in India highlight the alarming child malnutrition levels and lower rates of complete child immunization in many parts of India. Previous researches report that the conventional โ€ฆ

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Writing Blog Posts Helps Students Connect Experiential Learning to the Workplace

Utsab Saha, Lola Egherman, Ramiz Rahman, Mohd Toukir Khan, Kevin Wang, Tyler Menezes ยท 2026

Undergraduates in work-based learning experiences often produce meaningful contributions as viewed by their supervisors, yet report a negative perception of their contributions because they struggled โ€ฆ

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From Particles to Perils: SVGD-Based Hazardous Scenario Generation for Autonomous Driving Systems Testing

Linfeng Liang, Xiao Cheng, Tsong Yueh Chen, Xi Zheng ยท 2026

Simulation-based testing of autonomous driving systems (ADS) must uncover realistic and diverse failures in dense, heterogeneous traffic. However, existing search-based seeding methods (e.g., genetic โ€ฆ

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Spatiotemporal Link Formation Prediction in Social Learning Networks Using Graph Neural Networks

Ali Mohammadiasl, Bita Akram, Seyyedali Hosseinalipour, Rajeev Sahay ยท 2026

Social learning networks (SLNs) are graphical representations that capture student interactions within educational settings (e.g., a classroom), with nodes representing students and edges denoting intโ€ฆ

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Human-Machine Co-Boosted Bug Report Identification with Mutualistic Neural Active Learning

Guoming Long, Shihai Wang, Hui Fang, Tao Chen ยท 2026

Bug reports, encompassing a wide range of bug types, are crucial for maintaining software quality. However, the increasing complexity and volume of bug reports pose a significant challenge in sole manโ€ฆ

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