181 citations · 221 across the 11 of their papers we have counts for
13 papers
Data Reuse in Non-Stationary Learning
Tomer Gafni, Garud Iyengar, Assaf Zeevi
We consider online learning in non-stationary environments, where the goal is to track an unknown parameter that switches abruptly between a finite set of recurring values. Recurre…
The Cost of Learning Under Multiple Change Points
Tomer Gafni, Garud Iyengar, Assaf Zeevi
We consider an online learning problem in environments with multiple change points. In contrast to the single change point problem that is widely studied using classical "high conf…
Dual Precision Quantization for Efficient and Accurate Deep Neural Networks Inference
Tomer Gafni, Asaf Karnieli, Yair Hanani
Deep neural networks have achieved state-of-the-art results in a wide range of applications, from natural language processing and computer vision to speech recognition. However, as…
Asymptotically Optimal Search for a Change Point Anomaly under a Composite Hypothesis Model
Liad Lea Didi, Tomer Gafni, Kobi Cohen
We address the problem of searching for a change point in an anomalous process among a finite set of M processes. Specifically, we address a composite hypothesis model in which eac…
SINR-Aware Deep Reinforcement Learning for Distributed Dynamic Channel Allocation in Cognitive Interference Networks
Yaniv Cohen, Tomer Gafni, Ronen Greenberg +1
We consider the problem of dynamic channel allocation (DCA) in cognitive communication networks with the goal of maximizing a global signal-to-interference-plus-noise ratio (SINR)…
Federated Learning from Heterogeneous Data via Controlled Bayesian Air Aggregation
Tomer Gafni, Kobi Cohen, Yonina C. Eldar
Federated learning (FL) is an emerging machine learning paradigm for training models across multiple edge devices holding local data sets, without explicitly exchanging the data. R…
Restless Multi-Armed Bandits under Exogenous Global Markov Process
Tomer Gafni, Michal Yemini, Kobi Cohen
We consider an extension to the restless multi-armed bandit (RMAB) problem with unknown arm dynamics, where an unknown exogenous global Markov process governs the rewards distribut…
Anomaly Search over Composite Hypotheses in Hierarchical Statistical Models
Benjamin Wolff, Tomer Gafni, Guy Revach +2
Detection of anomalies among a large number of processes is a fundamental task that has been studied in multiple research areas, with diverse applications spanning from spectrum ac…
Learning in Restless Bandits under Exogenous Global Markov Process
Tomer Gafni, Michal Yemini, Kobi Cohen
We consider an extension to the restless multi-armed bandit (RMAB) problem with unknown arm dynamics, where an unknown exogenous global Markov process governs the rewards distribut…
Federated Learning: A Signal Processing Perspective
Tomer Gafni, Nir Shlezinger, Kobi Cohen +2
The dramatic success of deep learning is largely due to the availability of data. Data samples are often acquired on edge devices, such as smart phones, vehicles and sensors, and i…
Distributed Learning over Markovian Fading Channels for Stable Spectrum Access
Tomer Gafni, Kobi Cohen
We consider the problem of multi-user spectrum access in wireless networks. The bandwidth is divided into K orthogonal channels, and M users aim to access the spectrum. Each user c…
Searching for Anomalies over Composite Hypotheses
Bar Hemo, Tomer Gafni, Kobi Cohen +1
The problem of detecting anomalies in multiple processes is considered. We consider a composite hypothesis case, in which the measurements drawn when observing a process follow a c…
Learning in Restless Multi-Armed Bandits via Adaptive Arm Sequencing Rules
Tomer Gafni, Kobi Cohen
We consider a class of restless multi-armed bandit (RMAB) problems with unknown arm dynamics. At each time, a player chooses an arm out of N arms to play, referred to as an active…