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Projects

Robustness and uncertainty in dynamic decision problems
Understanding how to deal with model uncertainty is key for building resilient agents that can overcome environments that are unforeseen. My research group has studied for years different approaches that build robust agents that can cope with different types of uncertainties. Robustness means that policies are immune to changes ... more

Robustness and uncertainty in dynamic decision problems

Understanding how to deal with model uncertainty is key for building resilient agents that can overcome environments that are unforeseen. My research group has studied for years different approaches that build robust agents that can cope with different types of uncertainties. Robustness means that policies are immune to changes in the environment leading to better real time performance. In a sequence of papers we developed robust reinforcement learning and planning algorithms including scaling up such algorithms, learning the uncertainty set online, adapting quickly to unknown uncertainties, and online adaptation. The main application areas here are energy and transport services.

Using Reinforcement Learning for bit-rate selection
We consider a reinforcement learning scheme for selecting how and what to transfer in 5G networks. The problem at hand is to decide which bit-rate to use and which channels would yield the best tradeoff in terms of power, performance, and cost. We employ multi-objective, multi-agent reinforcement learning to best decide how to t... more

Using Reinforcement Learning for bit-rate selection

We consider a reinforcement learning scheme for selecting how and what to transfer in 5G networks. The problem at hand is to decide which bit-rate to use and which channels would yield the best tradeoff in terms of power, performance, and cost. We employ multi-objective, multi-agent reinforcement learning to best decide how to transmit the data. In previous work, we proposed to use multi-armed bandit algorithms that ignore the current channel and agent state (see O. Avner and S. Mannor, Multi-User Communication Networks: A Coordinated Multi-Armed Bandit Approach, IEEE/ACM Transactions on Networking ( Volume: 27, Issue: 6, Dec. 2019), https://ieeexplore.ieee.org/document/8875003), but in this project we go further and consider the state of the transmission, the real time requirements, and the changing channel.

Energy consumption and visual comfort in buildings
he aim of the project is to develop a new methodology for deciphering the human factor in illuminance-related building operation by taking advantage of recent developments in commercial building automation systems and the increasing prevalence of digital control systems for shading operation. The project involves the analysis of... more

Energy consumption and visual comfort in buildings

he aim of the project is to develop a new methodology for deciphering the human factor in illuminance-related building operation by taking advantage of recent developments in commercial building automation systems and the increasing prevalence of digital control systems for shading operation. The project involves the analysis of a large-scale dataset of long-term roller blinds operation in a multi-story office building in Tel Aviv, reflecting user preferences on indoor lighting conditions.

Methods for Wireless Sensor Network Localization
This project focuses on the design, analysis, development, and practical implementation of simple algorithms for solving the Wireless Sensor Network (WSN) Localization problems. In a recent paper, we solve the original non-convex and non-smooth formulation using first-order methods. We proposed a parameter-free algorithmic frame... more

Methods for Wireless Sensor Network Localization

This project focuses on the design, analysis, development, and practical implementation of simple algorithms for solving the Wireless Sensor Network (WSN) Localization problems. In a recent paper, we solve the original non-convex and non-smooth formulation using first-order methods. We proposed a parameter-free algorithmic framework that includes the whole spectrum ranging from a fully centralized to a fully distributed implementation, and that it can also achieve partial parallelization.

OPCloud is a web-based collaborative software environment for creating conceptual models of systems and phenomena with OPM standard  ISO 19450:2015. It is used in dozens of universities and enterprises, and its development is continuously adding new features and capabilities.
People:
Dov Dori
Massive Parallelization of Deep Learning
Improvements in training speed are needed to develop the next generation of deep learning models. To perform such a massive amount of computation in a reasonable time, it is parallelized across multiple GPU cores. Perhaps the most popular parallelization method is to use a large batch of data in each iteration of SGD, so the gra... more

Massive Parallelization of Deep Learning

Improvements in training speed are needed to develop the next generation of deep learning models. To perform such a massive amount of computation in a reasonable time, it is parallelized across multiple GPU cores. Perhaps the most popular parallelization method is to use a large batch of data in each iteration of SGD, so the gradient computation can be performed in parallel on multiple workers. We aim to enable massive parallelization without performance degradation, as commonly observed.

Resource efficient deep learning
We aim to improve the resource efficiency of deep learning (e.g., energy, bandwidth) for training and inference. Our focus is decreasing the numerical precision of the neural network model is a simple and effective way to improve their resource efficiency. Nearly all recent deep learning related hardware relies heavily on lower ... more

Resource efficient deep learning

We aim to improve the resource efficiency of deep learning (e.g., energy, bandwidth) for training and inference. Our focus is decreasing the numerical precision of the neural network model is a simple and effective way to improve their resource efficiency. Nearly all recent deep learning related hardware relies heavily on lower precision math. The benefits are a reduction in the memory required to store the neural network, a reduction in chip area, and a drastic improvement in energy efficiency.

Understanding and controlling the implicit bias in deep learning
Significant research efforts are being invested in improving Deep Neural Networks (DNNs) via various modifications. However, such modifications often cause an unexplained degradation in the generalization performance DNNs to unseen data. Recent findings suggest that this degradation is caused by changes to the hidden algorithmi... more

Understanding and controlling the implicit bias in deep learning

Significant research efforts are being invested in improving Deep Neural Networks (DNNs) via various modifications. However, such modifications often cause an unexplained degradation in the generalization performance DNNs to unseen data. Recent findings suggest that this degradation is caused by changes to the hidden algorithmic bias of the training algorithm and model. This bias determines which solution is selected from all solutions which fit the data. We aim to understand and control this algorithmic bias.

Queue mining for delay prediction in multi-class service processes
Information recorded by service systems (e.g., in the telecommunication, finance, and health sectors) during their operation provides an angle for operational process analysis, commonly referred to as process mining. Here we establish a queueing perspective in process mining to address the online delay prediction problem, which ... more

Queue mining for delay prediction in multi-class service processes

Information recorded by service systems (e.g., in the telecommunication, finance, and health sectors) during their operation provides an angle for operational process analysis, commonly referred to as process mining. Here we establish a queueing perspective in process mining to address the online delay prediction problem, which refers to the time that the execution of an activity for a running instance of a service process is delayed due to queueing effects. We develop predictors for waiting-times from event logs recorded by an information system during process execution. Based on large datasets from the telecommunications and financial sectors, our evaluation demonstrate accurate online predictions, which drastically improve over predictors neglecting the queueing perspective.

Dimensionality reduction
In this setting we study how to reduce the dimensionality of data for learning and for optimization, avoiding the “curse of dimensionality”.
People:
Nir Ailon
Ranking and preference learning
In this setting we study how to model people’s preferences over a set of choices, and how to optimize and learn given user preferences in a variety of applications.
People:
Nir Ailon
Online and bandit optimization
In this project we study how to make decisions in an unknown environment in an online setting.
People:
Nir Ailon
Large matrix approximation for acceleration of deep networks
In this work we apply matrix approximation theory to reduce the cost of training and deploying of dense layers in deep networks.
People:
Nir Ailon