research
∙
09/05/2023
Distributionally Robust Model-based Reinforcement Learning with Large State Spaces
Three major challenges in reinforcement learning are the complex dynamic...
share
research
∙
08/03/2023
Multitask Learning with No Regret: from Improved Confidence Bounds to Active Learning
Multitask learning is a powerful framework that enables one to simultane...
research
∙
07/31/2023
Adversarial Causal Bayesian Optimization
In Causal Bayesian Optimization (CBO), an agent intervenes on an unknown...
research
∙
07/25/2023
Submodular Reinforcement Learning
In reinforcement learning (RL), rewards of states are typically consider...
research
∙
07/24/2023
Anytime Model Selection in Linear Bandits
Model selection in the context of bandit optimization is a challenging p...
research
∙
06/29/2023
Safe Model-Based Multi-Agent Mean-Field Reinforcement Learning
Many applications, e.g., in shared mobility, require coordinating a larg...
research
∙
06/23/2023
Safe Risk-averse Bayesian Optimization for Controller Tuning
Controller tuning and parameter optimization are crucial in system desig...
research
∙
06/21/2023
Optimistic Active Exploration of Dynamical Systems
Reinforcement learning algorithms commonly seek to optimize policies for...
research
∙
06/15/2023
Unbalanced Diffusion Schrödinger Bridge
Schrödinger bridges (SBs) provide an elegant framework for modeling the ...
research
∙
06/13/2023
Provably Learning Nash Policies in Constrained Markov Potential Games
Multi-agent reinforcement learning (MARL) addresses sequential decision-...
research
∙
06/12/2023
Tuning Legged Locomotion Controllers via Safe Bayesian Optimization
In this paper, we present a data-driven strategy to simplify the deploym...
research
∙
05/25/2023
Learning Safety Constraints from Demonstrations with Unknown Rewards
We propose Convex Constraint Learning for Reinforcement Learning (CoCoRL...
research
∙
05/16/2023
A Scalable Walsh-Hadamard Regularizer to Overcome the Low-degree Spectral Bias of Neural Networks
Despite the capacity of neural nets to learn arbitrary functions, models...
research
∙
05/09/2023
Safe Deep RL for Intraoperative Planning of Pedicle Screw Placement
Spinal fusion surgery requires highly accurate implantation of pedicle s...
research
∙
03/02/2023
Hallucinated Adversarial Control for Conservative Offline Policy Evaluation
We study the problem of conservative off-policy evaluation (COPE) where ...
research
∙
02/22/2023
Aligned Diffusion Schrödinger Bridges
Diffusion Schrödinger bridges (DSB) have recently emerged as a powerful ...
research
∙
02/07/2023
Linear Partial Monitoring for Sequential Decision-Making: Algorithms, Regret Bounds and Applications
Partial monitoring is an expressive framework for sequential decision-ma...
research
∙
01/24/2023
Learning To Dive In Branch And Bound
Primal heuristics are important for solving mixed integer linear program...
research
∙
11/18/2022
Model-based Causal Bayesian Optimization
How should we intervene on an unknown structural causal model to maximiz...
research
∙
11/14/2022
PAC-Bayesian Meta-Learning: From Theory to Practice
Meta-Learning aims to accelerate the learning on new tasks by acquiring ...
research
∙
11/03/2022
Isotropic Gaussian Processes on Finite Spaces of Graphs
We propose a principled way to define Gaussian process priors on various...
research
∙
11/02/2022
Instance-Dependent Generalization Bounds via Optimal Transport
Existing generalization bounds fail to explain crucial factors that driv...
research
∙
10/27/2022
Lifelong Bandit Optimization: No Prior and No Regret
In practical applications, machine learning algorithms are often repeate...
research
∙
10/26/2022
Leveraging Demonstrations with Latent Space Priors
Demonstrations provide insight into relevant state or action space regio...
research
∙
10/25/2022
A Dynamical System View of Langevin-Based Non-Convex Sampling
Non-convex sampling is a key challenge in machine learning, central to n...
research
∙
10/24/2022
MARS: Meta-Learning as Score Matching in the Function Space
Meta-learning aims to extract useful inductive biases from a set of rela...
research
∙
10/14/2022
Movement Penalized Bayesian Optimization with Application to Wind Energy Systems
Contextual Bayesian optimization (CBO) is a powerful framework for seque...
research
∙
10/12/2022
Near-Optimal Multi-Agent Learning for Safe Coverage Control
In multi-agent coverage control problems, agents navigate their environm...
research
∙
10/04/2022
Reproducible Bandits
In this paper, we introduce the notion of reproducible policies in the c...
research
∙
10/03/2022
Meta-Learning Priors for Safe Bayesian Optimization
In robotics, optimizing controller parameters under safety constraints i...
research
∙
07/21/2022
Log Barriers for Safe Black-box Optimization with Application to Safe Reinforcement Learning
Optimizing noisy functions online, when evaluating the objective require...
research
∙
07/18/2022
Active Exploration for Inverse Reinforcement Learning
Inverse Reinforcement Learning (IRL) is a powerful paradigm for inferrin...
research
∙
07/13/2022
Graph Neural Network Bandits
We consider the bandit optimization problem with the reward function def...
research
∙
07/04/2022
Safe Reinforcement Learning via Confidence-Based Filters
Ensuring safety is a crucial challenge when deploying reinforcement lear...
research
∙
06/29/2022
Active Exploration via Experiment Design in Markov Chains
A key challenge in science and engineering is to design experiments to l...
research
∙
06/28/2022
Supervised Training of Conditional Monge Maps
Optimal transport (OT) theory describes general principles to define and...
research
∙
06/27/2022
Learning To Cut By Looking Ahead: Cutting Plane Selection via Imitation Learning
Cutting planes are essential for solving mixed-integer linear problems (...
research
∙
06/23/2022
Invariant Causal Mechanisms through Distribution Matching
Learning representations that capture the underlying data generating pro...
research
∙
06/14/2022
The Dynamics of Riemannian Robbins-Monro Algorithms
Many important learning algorithms, such as stochastic gradient methods,...
research
∙
06/10/2022
Interactively Learning Preference Constraints in Linear Bandits
We study sequential decision-making with known rewards and unknown const...
research
∙
06/04/2022
Active Bayesian Causal Inference
Causal discovery and causal reasoning are classically treated as separat...
research
∙
06/03/2022
BaCaDI: Bayesian Causal Discovery with Unknown Interventions
Learning causal structures from observation and experimentation is a cen...
research
∙
05/26/2022
Experimental Design for Linear Functionals in Reproducing Kernel Hilbert Spaces
Optimal experimental design seeks to determine the most informative allo...
research
∙
05/25/2022
Amortized Inference for Causal Structure Learning
Learning causal structure poses a combinatorial search problem that typi...
research
∙
04/04/2022
Multi-Scale Representation Learning on Proteins
Proteins are fundamental biological entities mediating key roles in cell...
research
∙
03/26/2022
Tuning Particle Accelerators with Safety Constraints using Bayesian Optimization
Tuning machine parameters of particle accelerators is a repetitive and t...
research
∙
03/14/2022
Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation
We consider model-based multi-agent reinforcement learning, where the en...
research
∙
02/11/2022
Recovering Stochastic Dynamics via Gaussian Schrödinger Bridges
We propose a new framework to reconstruct a stochastic process {ℙ_t: t ∈...
research
∙
02/01/2022
Meta-Learning Hypothesis Spaces for Sequential Decision-making
Obtaining reliable, adaptive confidence sets for prediction functions (h...
research
∙
01/24/2022