17 citations · 65 across the 51 of their papers we have counts for
9 papers · 1 filter
Regime-aware financial volatility forecasting via in-context learning
Saba Asaad, Shayan Mohajer Hamidi, Ali Bereyhi
This work introduces a regime-aware in-context learning framework that leverages large language models (LLMs) for financial volatility forecasting under nonstationary market condit…
Revisiting GAN with Bayes-Optimal Discrimination
Mohammadreza Tavasoli Naeini, Ali Bereyhi, Morteza Noshad +2
We approach generative adversarial network (GAN) training from its original real-versus-fake classification viewpoint, replacing the cross-entropy discriminator objective with an u…
Coding for Computation: Efficient Compression of Neural Networks for Reconfigurable Hardware
Hans Rosenberger, Rodrigo Fischer, Johanna S. Fröhlich +2
As state of the art neural networks (NNs) continue to grow in size, their resource-efficient implementation becomes ever more important. In this paper, we introduce a compression s…
BOLT: Universal Classifier Training via a Bayes-Error Upper Bound
Mohammadreza Tavasoli Naeini, Ali Bereyhi, Morteza Noshad +2
This work introduces a novel approach to compute a universal upper bound on the Bayes error rate (BER) from classifier-output samples, which serves as a Bayes-error-related perform…
Over-the-Air Fair Federated Learning via Multi-Objective Optimization
Shayan Mohajer Hamidi, Ali Bereyhi, Saba Asaad +1
In federated learning (FL), heterogeneity among the local dataset distributions of clients can result in unsatisfactory performance for some, leading to an unfair model. To address…
Regularized Top-: A Bayesian Framework for Gradient Sparsification
Ali Bereyhi, Ben Liang, Gary Boudreau +1
Error accumulation is effective for gradient sparsification in distributed settings: initially-unselected gradient entries are eventually selected as their accumulated error exceed…
GP-FL: Model-Based Hessian Estimation for Second-Order Over-the-Air Federated Learning
Shayan Mohajer Hamidi, Ali Bereyhi, Saba Asaad +1
Second-order methods are widely adopted to improve the convergence rate of learning algorithms. In federated learning (FL), these methods require the clients to share their local H…
Novel Gradient Sparsification Algorithm via Bayesian Inference
Ali Bereyhi, Ben Liang, Gary Boudreau +1
Error accumulation is an essential component of the Top- sparsification method in distributed gradient descent. It implicitly scales the learning rate and prevents the slow-down…
Rate-Constrained Quantization for Communication-Efficient Federated Learning
Shayan Mohajer Hamidi, Ali Bereyhi
Quantization is a common approach to mitigate the communication cost of federated learning (FL). In practice, the quantized local parameters are further encoded via an entropy codi…