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Found 18 papers, 1 papers with code
Mathematical Algorithm Design for Deep Learning under Societal and Judicial Constraints: The Algorithmic Transparency Requirement
Motivated by the observation that the current evolution of deep learning models necessitates a change in computing technology, we derive a mathematical framework which enables us to analyze whether a transparent implementation in a computing model is feasible.
Reliable AI: Does the Next Generation Require Quantum Computing?
The cause of these computability problems is rooted in the fact that digital hardware is based on the computing model of the Turing machine, which is inherently discrete.
Sensing-Assisted Receivers for Resilient-By-Design 6G MU-MIMO Uplink
We address the resilience of future 6G MIMO communications by considering an uplink scenario where multiple legitimate transmitters try to communicate with a base station in the presence of an adversarial jammer.
Computability of Optimizers
Optimization problems are a staple of today's scientific and technical landscape.
Well-definedness of Physical Law Learning: The Uniqueness Problem
Finally, we provide extensive numerical experiments showing that our algorithms in combination with common approaches for learning physical laws indeed allow to guarantee that a unique governing differential equation is learnt, without assuming any knowledge about the function, thereby ensuring reliability.
Secure and Private Source Coding with Private Key and Decoder Side Information
The problem of secure source coding with multiple terminals is extended by considering a remote source whose noisy measurements are the correlated random variables used for secure source reconstruction.
Inverse Problems Are Solvable on Real Number Signal Processing Hardware
For this, we focus on the class of inverse problems, which, in particular, encompasses any task to reconstruct data from measurements.
Limitations of Deep Learning for Inverse Problems on Digital Hardware
Deep neural networks have seen tremendous success over the last years.
Welfare Measure for Resource Allocation with Algorithmic Implementation: Beyond Average and Max-Min
In this work, we propose an axiomatic approach for measuring the performance/welfare of a system consisting of concurrent agents in a resource-driven system.
Mosaics of combinatorial designs for information-theoretic security
Every member of a mosaic is associated with a unique color, and each color corresponds to a unique message or key value.
Information Theory Combinatorics Information Theory
Secure Identification for Gaussian Channels and Identification for Multi-Antenna Gaussian Channels
This scheme allows a separation of signal-processing and Gaussian coding.
Optimal Signal Processing for Common Randomness Generation over MIMO Gaussian Channels with Applications in Identification
In many new applications such as several machine-to-machine and human-to-machine systems and the tactile internet, the identification, also called the post-Shannon scheme, is more efficient than the classical transmission.
On Information Asymmetry in Competitive Multi-Agent Reinforcement Learning: Convergence and Optimality
In this work, we study the system of interacting non-cooperative two Q-learning agents, where one agent has the privilege of observing the other's actions.
Coordinated Online Learning for Multi-Agent Systems with Coupled Constraints and Perturbed Utility Observations
Competitive non-cooperative online decision-making agents whose actions increase congestion of scarce resources constitute a model for widespread modern large-scale applications.
Quantum Channel State Masking
Communication over a quantum channel that depends on a quantum state is considered when the encoder has channel side information (CSI) and is required to mask information on the quantum channel state from the decoder.
Information Theory Information Theory Quantum Physics
Robust Online Learning for Resource Allocation -- Beyond Euclidean Projection and Dynamic Fit
Online-learning literature has focused on designing algorithms that ensure sub-linear growth of the cumulative long-term constraint violations.
Semi-Decentralized Coordinated Online Learning for Continuous Games with Coupled Constraints via Augmented Lagrangian
We give a condition on the step sizes and the degree of the augmentation of the Lagrangian, such that the proposed algorithm converges to a generalized Nash equilibrium.
Pricing Mechanism for Resource Sustainability in Competitive Online Learning Multi-Agent Systems
In case that the noise is persistent, and for several choices of the intrinsic parameter of the agents, such as their learning rate, and of the mechanism parameters, such as the learning rate of -, the progressivity of the price-setters, and the extrinsic price sensitivity of the agents, we show that the accumulative violation of the resource constraints of the resulted iterates is sub-linear w. r. t.
