Ziemann, Ingvar

11 publications

L4DC 2025 A Short Information-Theoretic Analysis of Linear Auto-Regressive Learning Ingvar Ziemann

TMLR 2025 A Vector Bernstein Inequality for Self-Normalized Martingales Ingvar Ziemann

L4DC 2025 Logarithmic Regret for Nonlinear Control James Wang, Bruce Lee, Ingvar Ziemann, Nikolai Matni

L4DC 2025 Nonconvex Linear System Identification with Minimal State Representation Uday Kiran Reddy Tadipatri, Benjamin D. Haeffele, Joshua Agterberg, Ingvar Ziemann, Rene Vidal

ICLR 2025 Shallow Diffusion Networks Provably Learn Hidden Low-Dimensional Structure Nicholas Matthew Boffi, Arthur Jacot, Stephen Tu, Ingvar Ziemann

L4DC 2025 State Space Models, Emergence, and Ergodicity: How Many Parameters Are Needed for Stable Predictions? Ingvar Ziemann, Nikolai Matni, George Pappas

ICML 2024 Guarantees for Nonlinear Representation Learning: Non-Identical Covariates, Dependent Data, Fewer Samples Thomas Tck Zhang, Bruce D Lee, Ingvar Ziemann, George J. Pappas, Nikolai Matni

ICML 2024 Sharp Rates in Dependent Learning Theory: Avoiding Sample Size Deflation for the Square Loss Ingvar Ziemann, Stephen Tu, George J. Pappas, Nikolai Matni

NeurIPS 2023 The Noise Level in Linear Regression with Dependent Data Ingvar Ziemann, Stephen Tu, George J. Pappas, Nikolai Matni

NeurIPS 2022 Learning with Little Mixing Ingvar Ziemann, Stephen Tu

L4DC 2021 On Uninformative Optimal Policies in Adaptive LQR with Unknown B-Matrix Ingvar Ziemann, Henrik Sandberg