Search Results for author:
Found 21 papers, 11 papers with code
Hallucinated Adversarial Control for Conservative Offline Policy Evaluation
We study the problem of conservative off-policy evaluation (COPE) where given an offline dataset of environment interactions, collected by other agents, we seek to obtain a (tight) lower bound on a policy's performance.
PAC-Bayesian Meta-Learning: From Theory to Practice
Meta-Learning aims to accelerate the learning on new tasks by acquiring useful inductive biases from related data sources.
Instance-Dependent Generalization Bounds via Optimal Transport
Existing generalization bounds fail to explain crucial factors that drive generalization of modern neural networks.
Lifelong Bandit Optimization: No Prior and No Regret
Our algorithm can be paired with any kernelized or linear bandit algorithm and guarantees oracle optimal performance, meaning that as more tasks are solved, the regret of LIBO on each task converges to the regret of the bandit algorithm with oracle knowledge of the true kernel.
MARS: Meta-Learning as Score Matching in the Function Space
To circumvent these issues, we approach meta-learning through the lens of functional Bayesian neural network inference, which views the prior as a stochastic process and performs inference in the function space.
Meta-Learning Priors for Safe Bayesian Optimization
In the presence of unknown safety constraints, it is crucial to choose reliable model hyper-parameters to avoid safety violations.
BaCaDI: Bayesian Causal Discovery with Unknown Interventions
Inferring causal structures from experimentation is a central task in many domains.
Amortized Inference for Causal Structure Learning
Rather than searching over structures, we train a variational inference model to directly predict the causal structure from observational or interventional data.
Meta-Learning Hypothesis Spaces for Sequential Decision-making
We demonstrate our approach on the kernelized bandit problem (a. k. a.~Bayesian optimization), where we establish regret bounds competitive with those given the true kernel.
Variational Causal Networks: Approximate Bayesian Inference over Causal Structures
However, a crucial aspect to acting intelligently upon the knowledge about causal structure which has been inferred from finite data demands reasoning about its uncertainty.
Meta-Learning Reliable Priors in the Function Space
When data are scarce meta-learning can improve a learner's accuracy by harnessing previous experience from related learning tasks.
DiBS: Differentiable Bayesian Structure Learning
In this work, we propose a general, fully differentiable framework for Bayesian structure learning (DiBS) that operates in the continuous space of a latent probabilistic graph representation.
Robustness to Pruning Predicts Generalization in Deep Neural Networks
Existing generalization measures that aim to capture a model's simplicity based on parameter counts or norms fail to explain generalization in overparameterized deep neural networks.
Meta-Learning Bayesian Neural Network Priors Based on PAC-Bayesian Theory
Bayesian deep learning is a promising approach towards improved uncertainty quantification and sample efficiency.
PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees
Meta-learning can successfully acquire useful inductive biases from data.
Noise Regularization for Conditional Density Estimation
To address this issue, we develop a model-agnostic noise regularization method for CDE that adds random perturbations to the data during training.
Conditional Density Estimation with Neural Networks: Best Practices and Benchmarks
Given a set of empirical observations, conditional density estimation aims to capture the statistical relationship between a conditional variable $\mathbf{x}$ and a dependent variable $\mathbf{y}$ by modeling their conditional probability $p(\mathbf{y}|\mathbf{x})$.
ProMP: Proximal Meta-Policy Search
Credit assignment in Meta-reinforcement learning (Meta-RL) is still poorly understood.
Model-Based Reinforcement Learning via Meta-Policy Optimization
Finally, we demonstrate that our approach is able to match the asymptotic performance of model-free methods while requiring significantly less experience.
Model-based Reinforcement Learning
reinforcement-learning
+1
Introducing the Simulated Flying Shapes and Simulated Planar Manipulator Datasets
We release two artificial datasets, Simulated Flying Shapes and Simulated Planar Manipulator that allow to test the learning ability of video processing systems.
Deep Episodic Memory: Encoding, Recalling, and Predicting Episodic Experiences for Robot Action Execution
We present a novel deep neural network architecture for representing robot experiences in an episodic-like memory which facilitates encoding, recalling, and predicting action experiences.
