410 citations · 2.1k across the 125 of their papers we have counts for
21 papers · 1 filter
The noise level in linear regression with dependent data
Ingvar Ziemann, Stephen Tu, George J. Pappas +1
We derive upper bounds for random design linear regression with dependent (-mixing) data absent any realizability assumptions. In contrast to the strictly realizable martingale…
Certified Invertibility in Neural Networks via Mixed-Integer Programming
Tianqi Cui, Thomas Bertalan, George J. Pappas +3
Neural networks are known to be vulnerable to adversarial attacks, which are small, imperceptible perturbations that can significantly alter the network's output. Conversely, there…
NOMAD: Nonlinear Manifold Decoders for Operator Learning
Jacob H. Seidman, Georgios Kissas, Paris Perdikaris +1
Supervised learning in function spaces is an emerging area of machine learning research with applications to the prediction of complex physical systems such as fluid flows, solid m…
Collaborative Linear Bandits with Adversarial Agents: Near-Optimal Regret Bounds
Aritra Mitra, Arman Adibi, George J. Pappas +1
We consider a linear stochastic bandit problem involving agents that can collaborate via a central server to minimize regret. A fraction of these agents are adversarial and…
Learning to Control Linear Systems can be Hard
Anastasios Tsiamis, Ingvar Ziemann, Manfred Morari +2
In this paper, we study the statistical difficulty of learning to control linear systems. We focus on two standard benchmarks, the sample complexity of stabilization, and the regre…
Distributed Statistical Min-Max Learning in the Presence of Byzantine Agents
Arman Adibi, Aritra Mitra, George J. Pappas +1
Recent years have witnessed a growing interest in the topic of min-max optimization, owing to its relevance in the context of generative adversarial networks (GANs), robust control…