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Found 15 papers, 7 papers with code
Healing Gaussian Process Experts
Gaussian processes (GPs) are nonparametric Bayesian models that have been applied to regression and classification problems.
On Combining Expert Demonstrations in Imitation Learning via Optimal Transport
One of the key approaches to IL is to define a distance between agent and expert and to find an agent policy that minimizes that distance.
Optimal Transport for Offline Imitation Learning
In this paper, we introduce Optimal Transport Reward labeling (OTR), an algorithm that assigns rewards to offline trajectories, with a few high-quality demonstrations.
Inefficiency of CFMs: hedging perspective and agent-based simulations
We investigate whether the fee income from trades on the CFM is sufficient for the liquidity providers to hedge away the exposure to market risk.
Matching Normalizing Flows and Probability Paths on Manifolds
Continuous Normalizing Flows (CNFs) are a class of generative models that transform a prior distribution to a model distribution by solving an ordinary differential equation (ODE).
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.
Cross-Domain Imitation Learning via Optimal Transport
Cross-domain imitation learning studies how to leverage expert demonstrations of one agent to train an imitation agent with a different embodiment or morphology.
Imitation Learning from Pixel Observations for Continuous Control
In this setting, we explore recipes for imitation learning based on adversarial learning and optimal transport.
Riemannian Convex Potential Maps
Modeling distributions on Riemannian manifolds is a crucial component in understanding non-Euclidean data that arises, e. g., in physics and geology.
Sliced Multi-Marginal Optimal Transport
To construct this distance, we introduce a characterization of the one-dimensional multi-marginal Kantorovich problem and use it to highlight a number of properties of the sliced multi-marginal Wasserstein distance.
Healing Products of Gaussian Processes
Gaussian processes (GPs) are nonparametric Bayesian models that have been applied to regression and classification problems.
Generalised correlated batched bandits via the ARC algorithm with application to dynamic pricing
The Asymptotic Randomised Control (ARC) algorithm provides a rigorous approximation to the optimal strategy for a wide class of Bayesian bandits, while retaining low computational complexity.
Estimating Barycenters of Measures in High Dimensions
Barycentric averaging is a principled way of summarizing populations of measures.
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.
Multi-Graph Decoding for Code-Switching ASR
This archive contains recordings with monolingual Frisian and Dutch speech segments as well as Frisian-Dutch CS speech, hence the recognition performance on monolingual segments is also vital for accurate transcriptions.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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