256 citations · 864 across the 92 of their papers we have counts for
13 papers · 1 filter
Safeguarding Privacy: Privacy-Preserving Detection of Mind Wandering and Disengagement Using Federated Learning in Online Education
Anna Bodonhelyi, Mengdi Wang, Efe Bozkir +2
Since the COVID-19 pandemic, online courses have expanded access to education, yet the absence of direct instructor support challenges learners' ability to self-regulate attention…
CycleSL: Server-Client Cyclical Update Driven Scalable Split Learning
Mengdi Wang, Efe Bozkir, Enkelejda Kasneci
Split learning emerges as a promising paradigm for collaborative distributed model training, akin to federated learning, by partitioning neural networks between clients and a serve…
Position: Uncertainty Quantification Needs Reassessment for Large-language Model Agents
Michael Kirchhof, Gjergji Kasneci, Enkelejda Kasneci
Large-language models (LLMs) and chatbot agents are known to provide wrong outputs at times, and it was recently found that this can never be fully prevented. Hence, uncertainty qu…
Enriching Tabular Data with Contextual LLM Embeddings: A Comprehensive Ablation Study for Ensemble Classifiers
Gjergji Kasneci, Enkelejda Kasneci
Feature engineering is crucial for optimizing machine learning model performance, particularly in tabular data classification tasks. Leveraging advancements in natural language pro…
TurboSVM-FL: Boosting Federated Learning through SVM Aggregation for Lazy Clients
Mengdi Wang, Anna Bodonhelyi, Efe Bozkir +1
Federated learning is a distributed collaborative machine learning paradigm that has gained strong momentum in recent years. In federated learning, a central server periodically co…
URL: A Representation Learning Benchmark for Transferable Uncertainty Estimates
Michael Kirchhof, Bálint Mucsányi, Seong Joon Oh +1
Representation learning has significantly driven the field to develop pretrained models that can act as a valuable starting point when transferring to new datasets. With the rising…