Search Results for author:
Found 14 papers, 5 papers with code
Bag of Policies for Distributional Deep Exploration
To test whether optimistic ensemble method can improve on distributional RL as did on scalar RL, by e. g. Bootstrapped DQN, we implement the BoP approach with a population of distributional actor-critics using Bayesian Distributional Policy Gradients (BDPG).
On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology
Our analysis provides a unified framework to study different recent methods introduced to cope with over-squashing and serves as a justification for a class of methods that fall under graph rewiring.
Schedule-Robust Online Continual Learning
A continual learning (CL) algorithm learns from a non-stationary data stream.
Meta Optimal Transport
We study the use of amortized optimization to predict optimal transport (OT) maps from the input measures, which we call Meta OT.
Heterogeneous manifolds for curvature-aware graph embedding
Graph embeddings, wherein the nodes of the graph are represented by points in a continuous space, are used in a broad range of Graph ML applications.
Enabling risk-aware Reinforcement Learning for medical interventions through uncertainty decomposition
Reinforcement Learning (RL) is emerging as tool for tackling complex control and decision-making problems.
Exploiting MMD and Sinkhorn Divergences for Fair and Transferable Representation Learning
One way to reach this goal is by modifying the data representation in order to meet certain fairness constraints.
Generalization Properties of Optimal Transport GANs with Latent Distribution Learning
The Generative Adversarial Networks (GAN) framework is a well-established paradigm for probability matching and realistic sample generation.
Aligning Time Series on Incomparable Spaces
Dynamic time warping (DTW) is a useful method for aligning, comparing and combining time series, but it requires them to live in comparable spaces.
A Non-Asymptotic Analysis for Stein Variational Gradient Descent
We study the Stein Variational Gradient Descent (SVGD) algorithm, which optimises a set of particles to approximate a target probability distribution $\pi\propto e^{-V}$ on $\mathbb{R}^d$.
The Wasserstein Proximal Gradient Algorithm
Using techniques from convex optimization and optimal transport, we analyze the FB scheme as a minimization algorithm on the Wasserstein space.
Sinkhorn Barycenters with Free Support via Frank-Wolfe Algorithm
We present a novel algorithm to estimate the barycenter of arbitrary probability distributions with respect to the Sinkhorn divergence.
Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction
We study the interplay between surrogate methods for structured prediction and techniques from multitask learning designed to leverage relationships between surrogate outputs.
Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance
Applications of optimal transport have recently gained remarkable attention thanks to the computational advantages of entropic regularization.
