no code implementations • 4 Mar 2025 • Emma Ceccherini, Ian Gallagher, Andrew Jones, Daniel Lawson
Stability for dynamic network embeddings ensures that nodes behaving the same at different times receive the same embedding, allowing comparison of nodes in the network across time.
1 code implementation • 13 Mar 2023 • Aishwarya Mandyam, Didong Li, Diana Cai, Andrew Jones, Barbara E. Engelhardt
In this work, we incorporate existing domain-specific data to achieve better posterior concentration rates.
no code implementations • pproximateinference AABI Symposium 2022 • Aishwarya Mandyam, Didong Li, Diana Cai, Andrew Jones, Barbara Engelhardt
Inverse reinforcement learning (IRL) methods attempt to recover the reward function of an agent by observing its behavior.
no code implementations • 6 Oct 2021 • Aishwarya Mandyam, Andrew Jones, Jiayu Yao, Krzysztof Laudanski, Barbara Engelhardt
CFQI uses a compositional $Q$-value function with separate modules for each task variant, allowing it to take advantage of shared knowledge while learning distinct policies for each variant.
no code implementations • 29 Sep 2021 • Aishwarya Mandyam, Andrew Jones, Krzysztof Laudanski, Barbara Engelhardt
Off-policy reinforcement learning (RL) has proven to be a powerful framework for guiding agents' actions in environments with stochastic rewards and unknown or noisy state dynamics.
1 code implementation • NeurIPS 2021 • Ian Gallagher, Andrew Jones, Patrick Rubin-Delanchy
We consider the problem of embedding a dynamic network, to obtain time-evolving vector representations of each node, which can then be used to describe changes in behaviour of individual nodes, communities, or the entire graph.
1 code implementation • CVPR 2021 • Petr Kellnhofer, Lars Jebe, Andrew Jones, Ryan Spicer, Kari Pulli, Gordon Wetzstein
Novel view synthesis is a challenging and ill-posed inverse rendering problem.
2 code implementations • 14 Dec 2020 • Didong Li, Andrew Jones, Barbara Engelhardt
Recently, contrastive principal component analysis (CPCA) was proposed for this setting.
1 code implementation • 20 Jul 2020 • Andrew Jones, Patrick Rubin-Delanchy
We present a comprehensive extension of the latent position network model known as the random dot product graph to accommodate multiple graphs -- both undirected and directed -- which share a common subset of nodes, and propose a method for jointly embedding the associated adjacency matrices, or submatrices thereof, into a suitable latent space.
no code implementations • 12 Oct 2019 • Ian Gallagher, Andrew Jones, Anna Bertiger, Carey Priebe, Patrick Rubin-Delanchy
When analyzing weighted networks using spectral embedding, a judicious transformation of the edge weights may produce better results.
no code implementations • CVPR 2018 • Loc Huynh, Weikai Chen, Shunsuke Saito, Jun Xing, Koki Nagano, Andrew Jones, Paul Debevec, Hao Li
We present a learning-based approach for synthesizing facial geometry at medium and fine scales from diffusely-lit facial texture maps.