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Found 21 papers, 3 papers with code
A Survey of Temporal Credit Assignment in Deep Reinforcement Learning
In this survey, we review the state of the art of Temporal Credit Assignment (CA) in deep RL.
AGAR: Attention Graph-RNN for Adaptative Motion Prediction of Point Clouds of Deformable Objects
From this understanding, we propose an improved architecture for point cloud prediction of deformable 3D objects.
Online Network Source Optimization with Graph-Kernel MAB
To achieve sample efficiency, we describe the network processes with an adaptive graph dictionary model, which typically leads to sparse spectral representations.
MiDi: Mixed Graph and 3D Denoising Diffusion for Molecule Generation
This work introduces MiDi, a novel diffusion model for jointly generating molecular graphs and their corresponding 3D arrangement of atoms.
Learning Algorithm Generalization Error Bounds via Auxiliary Distributions
Additionally, we demonstrate how our auxiliary distribution method can be used to derive the upper bounds on excess risk of some learning algorithms in the supervised learning context {\blue and the generalization error under the distribution mismatch scenario in supervised learning algorithms, where the distribution mismatch is modeled as $\alpha$-Jensen-Shannon or $\alpha$-R\'enyi divergence between the distribution of test and training data samples distributions.}
Semi-supervised Batch Learning From Logged Data
We propose learning methods for problems where feedback is missing for some samples, so there are samples with feedback and samples missing-feedback in the logged data.
An Information-theoretical Approach to Semi-supervised Learning under Covariate-shift
A common assumption in semi-supervised learning is that the labeled, unlabeled, and test data are drawn from the same distribution.
An Exact Characterization of the Generalization Error for the Gibbs Algorithm
Various approaches have been developed to upper bound the generalization error of a supervised learning algorithm.
Characterizing and Understanding the Generalization Error of Transfer Learning with Gibbs Algorithm
We provide an information-theoretic analysis of the generalization ability of Gibbs-based transfer learning algorithms by focusing on two popular transfer learning approaches, $\alpha$-weighted-ERM and two-stage-ERM.
Characterizing the Generalization Error of Gibbs Algorithm with Symmetrized KL information
As a result, they may fail to characterize the exact generalization ability of a learning algorithm.
Disentangled Predictive Representation for Meta-Reinforcement Learning
A major challenge in reinforcement learning is the design of agents that are able to generalize across tasks that share common dynamics.
Spatio-temporal Graph-RNN for Point Cloud Prediction
In this paper, we propose an end-to-end learning network to predict future frames in a point cloud sequence.
Information-Theoretic Bounds on the Moments of the Generalization Error of Learning Algorithms
Generalization error bounds are critical to understanding the performance of machine learning models.
Jensen-Shannon Information Based Characterization of the Generalization Error of Learning Algorithms
Generalization error bounds are critical to understanding the performance of machine learning models.
Graph signal processing for machine learning: A review and new perspectives
The effective representation, processing, analysis, and visualization of large-scale structured data, especially those related to complex domains such as networks and graphs, are one of the key questions in modern machine learning.
Differentiable Linear Bandit Algorithm
Theoretically, we show that the proposed algorithm achieves a $\tilde{\mathcal{O}}(\hat{\beta}\sqrt{dT})$ upper bound of $T$-round regret, where $d$ is the dimension of arm features and $\hat{\beta}$ is the learned size of confidence bound.
State2vec: Off-Policy Successor Features Approximators
In this paper, we propose state2vec, an efficient and low-complexity framework for learning successor features which (i) generalize across policies, (ii) ensure sample-efficiency during meta-test.
Laplacian-regularized graph bandits: Algorithms and theoretical analysis
In terms of network regret (sum of cumulative regret over $n$ users), the proposed algorithm leads to a scaling as $\tilde{\mathcal{O}}(\Psi d\sqrt{nT})$, which is a significant improvement over $\tilde{\mathcal{O}}(nd\sqrt{T})$ in the state-of-the-art algorithm \algo{Gob. Lin} \Ccite{cesa2013gang}.
Error Analysis on Graph Laplacian Regularized Estimator
We provide a theoretical analysis of the representation learning problem aimed at learning the latent variables (design matrix) $\Theta$ of observations $Y$ with the knowledge of the coefficient matrix $X$.
Representation Learning on Graphs: A Reinforcement Learning Application
In this work, we study value function approximation in reinforcement learning (RL) problems with high dimensional state or action spaces via a generalized version of representation policy iteration (RPI).
Graph-Based Recommendation System
In this work, we study recommendation systems modelled as contextual multi-armed bandit (MAB) problems.
