4 citations · 5 across the 7 of their papers we have counts for
6 papers · 1 filter
C3PO: Optimized Large Language Model Cascades with Probabilistic Cost Constraints for Reasoning
Antonios Valkanas, Soumyasundar Pal, Pavel Rumiantsev +2
Large language models (LLMs) have achieved impressive results on complex reasoning tasks, but their high inference cost remains a major barrier to real-world deployment. A promisin…
SKOLR: Structured Koopman Operator Linear RNN for Time-Series Forecasting
Yitian Zhang, Liheng Ma, Antonios Valkanas +2
Koopman operator theory provides a framework for nonlinear dynamical system analysis and time-series forecasting by mapping dynamics to a space of real-valued measurement functions…
ECGN: A Cluster-Aware Approach to Graph Neural Networks for Imbalanced Classification
Bishal Thapaliya, Anh Nguyen, Yao Lu +7
Classifying nodes in a graph is a common problem. The ideal classifier must adapt to any imbalances in the class distribution. It must also use information in the clustering struct…
MODL: Multilearner Online Deep Learning
Antonios Valkanas, Boris N. Oreshkin, Mark Coates
Online deep learning tackles the challenge of learning from data streams by balancing two competing goals: fast learning and deep learning. However, existing research primarily emp…
Contrastive Learning for Time Series on Dynamic Graphs
Yitian Zhang, Florence Regol, Antonios Valkanas +1
There have been several recent efforts towards developing representations for multivariate time-series in an unsupervised learning framework. Such representations can prove benefic…
Bag Graph: Multiple Instance Learning using Bayesian Graph Neural Networks
Soumyasundar Pal, Antonios Valkanas, Florence Regol +1
Multiple Instance Learning (MIL) is a weakly supervised learning problem where the aim is to assign labels to sets or bags of instances, as opposed to traditional supervised learni…