8 citations · 28 across the 12 of their papers we have counts for
12 papers
On Combining Expert Demonstrations in Imitation Learning via Optimal Transport
Ilana Sebag, Samuel Cohen, Marc Peter Deisenroth
Imitation learning (IL) seeks to teach agents specific tasks through expert demonstrations. One of the key approaches to IL is to define a distance between agent and expert and to…
Optimal Transport for Offline Imitation Learning
Yicheng Luo, Zhengyao Jiang, Samuel Cohen +2
With the advent of large datasets, offline reinforcement learning (RL) is a promising framework for learning good decision-making policies without the need to interact with the rea…
Matching Normalizing Flows and Probability Paths on Manifolds
Heli Ben-Hamu, Samuel Cohen, Joey Bose +5
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
Brandon Amos, Samuel Cohen, Giulia Luise +1
We study the use of amortized optimization to predict optimal transport (OT) maps from the input measures, which we call Meta OT. This helps repeatedly solve similar OT problems be…
Cross-Domain Imitation Learning via Optimal Transport
Arnaud Fickinger, Samuel Cohen, Stuart Russell +1
Cross-domain imitation learning studies how to leverage expert demonstrations of one agent to train an imitation agent with a different embodiment or morphology. Comparing trajecto…
Riemannian Convex Potential Maps
Samuel Cohen, Brandon Amos, Yaron Lipman
Modeling distributions on Riemannian manifolds is a crucial component in understanding non-Euclidean data that arises, e.g., in physics and geology. The budding approaches in this…
Identifiability in inverse reinforcement learning
Haoyang Cao, Samuel N. Cohen, Lukasz Szpruch
Inverse reinforcement learning attempts to reconstruct the reward function in a Markov decision problem, using observations of agent actions. As already observed in Russell [1998]…
Healing Products of Gaussian Processes
Samuel Cohen, Rendani Mbuvha, Tshilidzi Marwala +1
Gaussian processes (GPs) are nonparametric Bayesian models that have been applied to regression and classification problems. One of the approaches to alleviate their cubic training…
Sliced Multi-Marginal Optimal Transport
Samuel Cohen, Alexander Terenin, Yannik Pitcan +3
Multi-marginal optimal transport enables one to compare multiple probability measures, which increasingly finds application in multi-task learning problems. One practical limitatio…
Estimating Barycenters of Measures in High Dimensions
Samuel Cohen, Michael Arbel, Marc Peter Deisenroth
Barycentric averaging is a principled way of summarizing populations of measures. Existing algorithms for estimating barycenters typically parametrize them as weighted sums of Dira…
Aligning Time Series on Incomparable Spaces
Samuel Cohen, Giulia Luise, Alexander Terenin +2
Dynamic time warping (DTW) is a useful method for aligning, comparing and combining time series, but it requires them to live in comparable spaces. In this work, we consider a sett…
Multi-Graph Decoding for Code-Switching ASR
Emre Yılmaz, Samuel Cohen, Xianghu Yue +2
In the FAME! Project, a code-switching (CS) automatic speech recognition (ASR) system for Frisian-Dutch speech is developed that can accurately transcribe the local broadcaster's b…