7 accepted papers
The holy grail of machine learning is to enable Continual Federated Learning (CFL) to enhance the efficiency, privacy, and scalability of AI systems while learning from streaming data. The primary challenge of a CFL system is to overcome global catastrophic forgetting, wherein the accuracy of the gl…
The integration of Artificial Intelligence into wireless networks is rapidly advancing, with Federated Learning (FL) at the forefront as the preserving privacy strategy that enables learning on edge devices. Over-The-Air FL (OTA-FL) harnesses Multiple Access Channels (MACs) for efficient, low-latenc…
The use of AI has led to the era of pervasive intelligence, marked by a proliferation of smart devices in our daily lives. Federated Learning (FL) enables machine learning at the edge without having to share user-specific private data with an untrusted third party. Conventional FL techniques are sup…
Clinical decision making and diagnosis in healthcare is enabled by learning causal relationships among entities. In order to answer the question, ’Will changing the treatment regimen lead to quicker recovery of a patient?’ requires predicting counterfactuals using survival analysis. We seek to relia…
There is a growing interest in the learning-to-learn paradigm, also known as meta-learning, where models infer on new tasks using a few training examples. Recently, meta-learning based methods have been widely used in few-shot classification, regression, reinforcement learning, and domain adaptation…
The holy grail in deep neural network research is porting the memory- and computation-intensive network models on embedded platforms with a minimal compromise in model accuracy. To this end, we propose Variational Student where we reap the benefits of compressibility of the knowledge distillation fr…
In this work, we address the recovery of sparse and compressible vectors in the presence of colored noise possibly with a rank-deficient noise covariance matrix, from overcomplete noisy linear measurements. We exploit the structure of the noise covariance matrix in a Bayesian framework. In particula…