Search Results for author:
Found 17 papers, 11 papers with code
DESPOT: Online POMDP Planning with Regularization
We show that the best policy obtained from a DESPOT is near-optimal, with a regret bound that depends on the representation size of the optimal policy.
Discriminative Particle Filter Reinforcement Learning for Complex Partial Observations
The particle filter maintains a belief using learned discriminative update, which is trained end-to-end for decision making.
Nesterov Acceleration of Alternating Least Squares for Canonical Tensor Decomposition: Momentum Step Size Selection and Restart Mechanisms
While Nesterov acceleration turns gradient descent into an optimal first-order method for convex problems by adding a momentum term with a specific weight sequence, a direct application of this method and weight sequence to ALS results in erratic convergence behaviour.
Revisiting Maximum Entropy Inverse Reinforcement Learning: New Perspectives and Algorithms
This improves the previous heuristic derivation of the MaxEnt IRL model (for stochastic MDPs), allows a unified view of MaxEnt IRL and Relative Entropy IRL, and leads to a model-free learning algorithm for the MaxEnt IRL model.
Positive-Unlabeled Learning using Random Forests via Recursive Greedy Risk Minimization
Key to our approach is a new interpretation of decision tree algorithms for positive and negative data as \emph{recursive greedy risk minimization algorithms}.
Adaptive Discretization using Voronoi Trees for Continuous-Action POMDPs
A Voronoi tree is a Binary Space Partitioning (BSP) that implicitly maintains the partition of a cell as the Voronoi diagram of two points sampled from the cell.
Adaptive Discretization using Voronoi Trees for Continuous POMDPs
ADVT uses the estimated diameters of the cells to form an upper-confidence bound on the action value function within the cell, guiding the Monte Carlo Tree Search expansion and further discretization of the action space.
Fast Controllable Diffusion Models for Undersampled MRI Reconstruction
Supervised deep learning methods have shown promise in undersampled Magnetic Resonance Imaging (MRI) reconstruction, but their requirement for paired data limits their generalizability to the diverse MRI acquisition parameters.
Robust Loss Functions for Training Decision Trees with Noisy Labels
We consider training decision trees using noisily labeled data, focusing on loss functions that can lead to robust learning algorithms.
Greedy Convex Ensemble
Theoretically, we first consider whether we can use linear, instead of convex, combinations, and obtain generalization results similar to existing ones for learning from a convex hull.
Robustness of Bayesian Pool-based Active Learning Against Prior Misspecification
This suggests we should use a Lipschitz utility for AL if robustness is required.
Active Learning for Probabilistic Hypotheses Using the Maximum Gibbs Error Criterion
We introduce a new objective function for pool-based Bayesian active learning with probabilistic hypotheses.
Conditional Random Fields with High-Order Features for Sequence Labeling
Dependencies among neighbouring labels in a sequence is an important source of information for sequence labeling problems.
Tensor Belief Propagation
We propose a new approximate inference algorithm for graphical models, tensor belief propagation, based on approximating the messages passed in the junction tree algorithm.
LiMIIRL: Lightweight Multiple-Intent Inverse Reinforcement Learning
Multiple-Intent Inverse Reinforcement Learning (MI-IRL) seeks to find a reward function ensemble to rationalize demonstrations of different but unlabelled intents.
A Boosting Algorithm for Positive-Unlabeled Learning
This is because AdaPU learns a weak classifier and its weight using a weighted positive-negative (PN) dataset with some negative data weights $-$ the dataset is derived from the original PU data, and the data weights are determined by the current weighted classifier combination, but some data weights are negative.
A Surprisingly Simple Continuous-Action POMDP Solver: Lazy Cross-Entropy Search Over Policy Trees
At each planning step, our method uses a novel lazy Cross-Entropy method to search the space of policy trees, which provide a simple policy representation.
