6 accepted papers
In-context learning (ICL) is a new paradigm for natural language processing that utilizes Generative Pre-trained Transformer (GPT)-like models. This approach uses prompts that include in-context demonstrations to generate the corresponding output for a new query input. However, applying ICL in real…
We present an algorithm to estimate the trace of symmetric matrices that are available only via Matrix-Vector multiplication. The proposed algorithm constructs a series of trace estimates by applying the probing technique with an increasing number of vectors. These estimates are then treated as a co…
In this paper, we present FLASH, a framework which addresses for the first time the central AutoML problem of Combined Algorithm Selection and HyperParameter (HP) Optimization (CASH) in the context of Federated Learning (FL). To limit training cost, FLASH incrementally adapts the set of algorithms t…
We propose Quantum Graph Transformers (QGT), a novel approach for realizing the Transformer architecture for graph learning with quantum processors. QGT is built on top of the Graph Trans-former (GT) architecture and addresses the main challenge of mapping GT basic functions such as node encodings,…
In this paper we introduce a metalearning-based methodology for predicting the training runtime of various machine learning algorithms. This prediction is important for automated machine learning (AutoML) systems because they search by training and evaluating a large number of machine learning model…
We address the problem of hyper-parameter optimization (HPO) for federated learning (FL-HPO). We introduce Federated Loss SuRface Aggregation (FLoRA), a general FL-HPO solution framework that can address use cases of tabular data and any Machine Learning (ML) model including gradient boosting traini…