Never Say Never Probabilistic & Temporal Failure Detectors

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The problem of Byzantine resilience in distributed machine learning, a.k.a., Byzantine machine learning, consists in designing distributed algorithms that can train an accurate model despite the presence of Byzantine nodes, i.e., nodes with corrupt data or ...

The advent of comprehensive synaptic wiring diagrams of large neural circuits has created the field of connectomics and given rise to a number of open research questions. One such question is whether it is possible to reconstruct the information stored in ...

We survey lower-bound results in complexity theory that have been obtained via newfound interconnections between propositional proof complexity, boolean circuit complexity, and query/communication complexity. We advocate for the theory of total search prob ...

Today, automatic control is integrated into a wide spectrum of real-world systems such as electrical grids and transportation networks. Many of these systems comprise numerous interconnected agents, perform safety-critical operations, or generate large amo ...

We propose a new approach for normalization and simplification of logical formulas. Our approach is based on algorithms for lattice-like structures. Specifically, we present two efficient algorithms for computing a normal form and deciding the word problem ...

Two prominent categories for achieving coordinated multirobot displacement are flocking and navigation in formation. Both categories have their own body of literature and characteristics, including their respective advantages and disadvantages. While typic ...

Modular robots hold promise of adaptability, robustness, and versatility. Synchronization of modules is critical; if a module is asynchronous, not only will the robot fail to perform tasks properly, but its behavior may be self-destructive through self-col ...

Pearl's do calculus is a complete axiomatic approach to learn the identifiable causal effects from observational data. When such an effect is not identifiable, it is necessary to perform a collection of often costly interventions in the system to learn the ...

In the domains of machine learning, data science and signal processing, graph or network data, is becoming increasingly popular. It represents a large portion of the data in computer, transportation systems, energy networks, social, biological, and other s ...

In a group, the collective dynamics is governed by the inter- actions between individuals, which can manifest differently depending on the available means of communication. In this paper, we compare 3 conditions of communication affordances (global chat, l ...

Since the birth of Information Theory, researchers have defined and exploited various information measures, as well as endowed them with operational meanings. Some were born as a "solution to a problem", like Shannon's Entropy and Mutual Information. Other ...

The design of efficient energy systems, through the development of new technologies and the improvement of current ones, requires the use of rigorous process synthesis methods for generating and analysing design alternatives. We introduce a digital twin of ...

Never Say Never Probabilistic & Temporal Failure Detectors

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Byzantine Machine Learning: A Primer

Bayesian reconstruction of memories stored in neural networks from their connectivity

Proofs, Circuits, and Communication

Data-driven and Safe Networked Control with Applications to Microgrids

Formula Normalizations in Verification

Hybrid Flock - Formation Control Algorithms

Hybrid Wireless-Local Communication via Information Propagation for Modular Robotic Synchronization Applications

Minimum Cost Intervention Design for Causal Effect Identification

Quantization for Distributed Processing and Learning of Structured Data

Effect of Different Communication Affordances on the Emergence of Collaboration Strategies in an Online Multiplayer Game

A Functional Perspective on Information Measures

Overcoming decision paralysis—A digital twin for decision making in energy system design