23 citations · 58 across the 52 of their papers we have counts for
12 papers · 1 filter
Contribution-Aware Bandwidth Allocation for Multimodal Split Learning
Iason Ofeidis, Leandros Tassiulas
Multimodal models are increasingly the default option for perception at the network edge, yet they are trained almost entirely in the datacenter, because a client holding several s…
Reasoning through Verifiable Forecast Actions: Consistency-Grounded RL for Financial LLMs
Jialin Chen, Aosong Feng, Harshit Verma +7
Financial markets are characterized by extreme non-stationarity, low signal-to-noise ratios, and strong dependence on external information such as news, company fundamentals, and m…
Rank Is Not Capacity: Spectral Occupancy for Latent Graph Models
Nikolaos Nakis, Panagiotis Promponas, Konstantinos Tsirkas +4
Graph representation learning has become a standard approach for analyzing networked data, with latent embeddings widely used for link prediction, community detection, and related…
CLAD: A Clustered Label-Agnostic Federated Learning Framework for Joint Anomaly Detection and Attack Classification
Iason Ofeidis, Nikos Papadis, Randeep Bhatia +2
The rapid expansion of the Internet of Things (IoT) and Industrial IoT (IIoT) has created a massive, heterogeneous attack surface that challenges traditional network security mecha…
Hierarchical Federated Learning for Networked AI: From Communication Saving to Architecture-Aware Design
Seyed Mohammad Azimi-Abarghouyi, Mehdi Bennis, Leandros Tassiulas
Federated learning (FL) is fundamentally a distributed optimization problem executed by communicating agents with local data, local computation, and partial system visibility. Once…
Multi-Modal Time Series Prediction via Mixture of Modulated Experts
Lige Zhang, Ali Maatouk, Jialin Chen +3
Real-world time series exhibit complex and evolving dynamics, making accurate forecasting extremely challenging. Recent multi-modal forecasting methods leverage textual information…
TRACE: Grounding Time Series in Context for Multimodal Embedding and Retrieval
Jialin Chen, Ziyu Zhao, Gaukhar Nurbek +5
The ubiquity of dynamic data in domains such as weather, healthcare, and energy underscores a growing need for effective interpretation and retrieval of time-series data. These dat…
HELM: Hyperbolic Large Language Models via Mixture-of-Curvature Experts
Neil He, Rishabh Anand, Hiren Madhu +5
Large language models (LLMs) have shown great success in text modeling tasks across domains. However, natural language exhibits inherent semantic hierarchies and nuanced geometric…
From Similarity to Superiority: Channel Clustering for Time Series Forecasting
Jialin Chen, Jan Eric Lenssen, Aosong Feng +5
Time series forecasting has attracted significant attention in recent decades. Previous studies have demonstrated that the Channel-Independent (CI) strategy improves forecasting pe…
Efficient High-Resolution Time Series Classification via Attention Kronecker Decomposition
Aosong Feng, Jialin Chen, Juan Garza +5
The high-resolution time series classification problem is essential due to the increasing availability of detailed temporal data in various domains. To tackle this challenge effect…
Adaptive Graph Spatial-Temporal Transformer Network for Traffic Flow Forecasting
Aosong Feng, Leandros Tassiulas
Traffic flow forecasting on graphs has real-world applications in many fields, such as transportation system and computer networks. Traffic forecasting can be highly challenging du…
KerGNNs: Interpretable Graph Neural Networks with Graph Kernels
Aosong Feng, Chenyu You, Shiqiang Wang +1
Graph kernels are historically the most widely-used technique for graph classification tasks. However, these methods suffer from limited performance because of the hand-crafted com…