← Authors

Mathias Lechner

23 papers · 2020–2025 · 4 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer 🌍 Conference Polyglot (4) πŸƒ Academic Marathon (5) 🐝 Cross-Pollinator (7) 🌈 Renaissance Researcher (7) πŸ—ΊοΈ Taxonomy Completionist (39) πŸ‘‘ Triple Crown πŸ† Grand Slam 🀝 Dynamic Duo (16) πŸ’Ž Century Club (23) πŸ”₯ Unstoppable (6) ⚑ Prolific Year (8) πŸ—ƒοΈ Keyword Collector (89)

Conferences

AAAI (7) ICML (6) ICLR (5) NIPS (5)

Top co-authors

Ramin Hasani (16) Daniela Rus (15) Alexander Amini (6) ĐorΔ‘e Ε½ikeliΔ‡ (6) Noel Loo (5) Radu Grosu (5) Krishnendu Chatterjee (5) Thomas A. Henzinger (5) Wei Xiao (3) Tsun-Hsuan Wang (3)

Research topics

Optimization & Theory (1)

Keywords

formal verification (6) reinforcement learning (4) neural network (4) stochastic control (3) neural ordinary differential equation (3) model compression (2) reach-avoid specification (2) neural network verification (2) reachability analysis (2) continuous-time neural network (2) dataset distillation (2) quantized neural network (2) compositional learning (1) neural tangent kernel (1) posterior sampling (1) knowledge distillation (1) image classification (1) stochastic optimization (1) convex optimization (1) excess risk (1)

Papers

SafeDiffuser: Safe Planning with Diffusion Probabilistic Models ICLR 2025
Large Scale Dataset Distillation with Domain Shift ICML 2024
Leveraging Low-Rank and Sparse Recurrent Connectivity for Robust Closed-Loop Control ICLR 2024
Understanding Reconstruction Attacks with the Neural Tangent Kernel and Dataset Distillation ICLR 2024
State-Free Inference of State-Space Models: The *Transfer Function* Approach ICML 2024
Quantization-Aware Interval Bound Propagation for Training Certifiably Robust Quantized Neural Networks AAAI 2023
Gigastep - One Billion Steps per Second Multi-agent Reinforcement Learning NIPS 2023
Compositional Policy Learning in Stochastic Control Systems with Formal Guarantees NIPS 2023
On the Size and Approximation Error of Distilled Datasets NIPS 2023
Learning Control Policies for Stochastic Systems with Reach-Avoid Guarantees AAAI 2023
Liquid Structural State-Space Models ICLR 2023
Dataset Distillation with Convexified Implicit Gradients ICML 2023
On the Forward Invariance of Neural ODEs ICML 2023
Stability Verification in Stochastic Control Systems via Neural Network Supermartingales AAAI 2022
GoTube: Scalable Statistical Verification of Continuous-Depth Models AAAI 2022
Liquid Time-constant Networks AAAI 2021
On-Off Center-Surround Receptive Fields for Accurate and Robust Image Classification ICML 2021
Causal Navigation by Continuous-time Neural Networks NIPS 2021
On the Verification of Neural ODEs with Stochastic Guarantees AAAI 2021
Scalable Verification of Quantized Neural Networks AAAI 2021
Infinite Time Horizon Safety of Bayesian Neural Networks NIPS 2021
A Natural Lottery Ticket Winner: Reinforcement Learning with Ordinary Neural Circuits ICML 2020
Learning representations for binary-classification without backpropagation ICLR 2020