DeepAI

# Robust Reconfigurable Intelligent Surfaces via Invariant Risk and Causal Representations

In this paper, the problem of robust reconfigurable intelligent surface (RIS) system design under changes in data distributions is investigated. Using the notion of invariant risk minimization (IRM), an invariant causal representation across multiple environments is used such that the predictor is simultaneously optimal for each environment. A neural network-based solution is adopted to seek the predictor and its performance is validated via simulations against an empirical risk minimization-based design. Results show that leveraging invariance yields more robustness against unseen and out-of-distribution testing environments.

• 25 publications
• 70 publications
• 169 publications
07/05/2019

### Invariant Risk Minimization

We introduce Invariant Risk Minimization (IRM), a learning paradigm to e...
08/04/2020

### Out-of-Distribution Generalization with Maximal Invariant Predictor

Out-of-Distribution (OOD) generalization problem is a problem of seeking...
12/17/2021

### Balancing Fairness and Robustness via Partial Invariance

The Invariant Risk Minimization (IRM) framework aims to learn invariant ...
10/12/2021

### Gated Information Bottleneck for Generalization in Sequential Environments

Deep neural networks suffer from poor generalization to unseen environme...
06/13/2020

### Risk Variance Penalization: From Distributional Robustness to Causality

Learning under multi-environments often requires the ability of out-of-d...
07/26/2022

### Repeated Environment Inference for Invariant Learning

We study the problem of invariant learning when the environment labels a...
06/06/2020

### Domain Extrapolation via Regret Minimization

Many real prediction tasks such as molecular property prediction require...