32 citations · 78 across the 9 of their papers we have counts for
9 papers
Learning to Build: Autonomous Robotic Assembly of Stable Structures Without Predefined Plans
Jingwen Wang, Johannes Kirschner, Paul Rolland +2
This paper presents a novel autonomous robotic assembly framework for constructing stable structures without relying on predefined architectural blueprints. Instead of following fi…
Identifiability and generalizability from multiple experts in Inverse Reinforcement Learning
Paul Rolland, Luca Viano, Norman Schuerhoff +2
While Reinforcement Learning (RL) aims to train an agent from a reward function in a given environment, Inverse Reinforcement Learning (IRL) seeks to recover the reward function fr…
Score matching enables causal discovery of nonlinear additive noise models
Paul Rolland, Volkan Cevher, Matthäus Kleindessner +4
This paper demonstrates how to recover causal graphs from the score of the data distribution in non-linear additive (Gaussian) noise models. Using score matching algorithms as a bu…
Efficient Proximal Mapping of the 1-path-norm of Shallow Networks
Fabian Latorre, Paul Rolland, Nadav Hallak +1
We demonstrate two new important properties of the 1-path-norm of shallow neural networks. First, despite its non-smoothness and non-convexity it allows a closed form proximal oper…
Double-Loop Unadjusted Langevin Algorithm
Paul Rolland, Armin Eftekhari, Ali Kavis +1
A well-known first-order method for sampling from log-concave probability distributions is the Unadjusted Langevin Algorithm (ULA). This work proposes a new annealing step-size sch…
Lipschitz constant estimation of Neural Networks via sparse polynomial optimization
Fabian Latorre, Paul Rolland, Volkan Cevher
We introduce LiPopt, a polynomial optimization framework for computing increasingly tighter upper bounds on the Lipschitz constant of neural networks. The underlying optimization p…
Robust Reinforcement Learning via Adversarial training with Langevin Dynamics
Parameswaran Kamalaruban, Yu-Ting Huang, Ya-Ping Hsieh +3
We introduce a sampling perspective to tackle the challenging task of training robust Reinforcement Learning (RL) agents. Leveraging the powerful Stochastic Gradient Langevin Dynam…
Efficient learning of smooth probability functions from Bernoulli tests with guarantees
Paul Rolland, Ali Kavis, Alex Immer +2
We study the fundamental problem of learning an unknown, smooth probability function via pointwise Bernoulli tests. We provide a scalable algorithm for efficiently solving this pro…
High-Dimensional Bayesian Optimization via Additive Models with Overlapping Groups
Paul Rolland, Jonathan Scarlett, Ilija Bogunovic +1
Bayesian optimization (BO) is a popular technique for sequential black-box function optimization, with applications including parameter tuning, robotics, environmental monitoring,…