user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Bayesian Meta Sampling for Fast Uncertainty Adaptation

Paper content:
Meta learning has been making impressive progress for fast model adaptation.
However, limited work has been done on learning fast uncertainty adaption for Bayesian modeling.
In this paper, we propose to achieve the goal by placing meta learning on the space of probability measures, inducing the concept of meta sampling for fast uncertainty adaption.
Specifically, we propose a Bayesian meta sampling framework consisting of two main components: a meta sampler and a sample adapter.
The meta sampler is constructed by adopting a neural-inverse-autoregressive-flow (NIAF) structure, a variant of the recently proposed neural autoregressive flows, to efficiently generate meta samples to be adapted.
The sample adapter moves meta samples to task-specific samples, based on a newly proposed and general Bayesian sampling technique, called optimal-transport Bayesian sampling.
The combination of the two components allows a simple learning procedure for the
meta sampler to be developed, which can be efficiently optimized via standard back-propagation.
Extensive experimental results demonstrate the efficiency and effectiveness of the proposed framework, obtaining better sample quality and faster
uncertainty adaption compared to related methods.
Meta learning (Schmidhuber, 1987; Andrychowicz et al., 2016) is an important topic in modern machine learning.
The goal is to learn some abstract concepts from different but related tasks, which can then be adapted and generalized to new tasks and environments that have never been encountered during training.
There has been lots of research on this topic.
A recent review classifies the methods as metric-based, model-based and optimization-based methods (Weng, 2018) .
Among these methods, learning-to-learn seeks to learn a meta optimizer that can be applied to different models, with some task-specific information such as current gradients as input (Andrychowicz et al., 2016) .
Model agnostic meta learning (MAML) aims to learn a meta parameter/model from a set of training tasks such that it can quickly adapt to models for new tasks (Finn et al., 2017) .
Many follow-up works have been proposed recently, including but not limited to the meta network (Munkhdalai & Yu, 2017) , the meta learner (Ravi & Larochelle, 2017) , the Reptile model (Nichol et al., 2018) , and the lately extensions to an online setting (Finn et al., 2019) , to model hierarchical relation (Yao et al., 2019) and sequential strategies (Ortega et al., 2019) , and to its stable version Antoniou et al. (2019) and to some theoretical analysis (Khodak et al., 2019) .
It is worth noting that all the aforementioned models are designed from an optimization perspective.
Bayesian modeling, in parallel with optimization, has also been gaining increasing attention and found various applications in deep learning.
Recent research has extended the above meta-learning methods to a Bayesian setting.
For example, Bayesian MAML (BMAML) replaces the stochasticgradient-descent (SGD) step with Stein variational gradient descent (SVGD) for posterior sampling (Yoon et al., 2018) .
Probabilistic MAML (PMAML) extends standard MAML by incorporating a parameter distribution of the adapted model trained via a variational lower bound (Finn et al., 2018) .
Amortized Bayesian Meta Learning extends the idea of MAML to amortized variational inference (Ravi & Beatson, 2019; Choi et al., 2019) .
VERSA (Gordon et al., 2019) uses an amortization network to approximate the posterior predictive distributions.
Meta particle flow realizes Bayes's rule based on ODE neural operator that can be trained in a meta-learning framework.
Though methodologically elegant with many interesting applications, the above methods lack the ability to uncertainty propagation/adaption, in the sense that uncertainty is either not considered (e.g., in MAML) or only considered in the specific task level (e.g., BMAML).
This could slow down model adaption or even inaccurate uncertainty modeling when considering from a Bayesian modeling perspective.
For example, suppose one is given samples from a set of Gaussians with different mean and covariance matrices, how can she/he efficiently leverage uncertainty in these samples to generate samples from a complex yet related distribution such as a Gaussian mixture?
To tackle this problem, we propose to perform meta learning on the space of probability measures, i.e., instead of adapting parameters to a new task, one adapts a meta distribution to new tasks.
When implementing distribution adaption in algorithms where distributions are approximated by samples, our distribution-adaptation framework becomes sample-to-sample adaption.
In other words, the meta parameter in standard MAML becomes meta samples in our method, where uncertainty can be well encoded.
For this reason, we call our framework Bayesian meta sampling.
Specifically, we propose a mathematically elegant framework for Bayesian meta sampling based on the theory of Wasserstein gradient flows (WGF) (Ambrosio et al., 2005) .
Our goal is to learn a meta sampler whose samples can be fast adapted to new tasks.
Our framework contains two main components: a meta sampler and a sample adapter.
For the meta sampler, we adopt the state-ofthe-art flow-based method to learn to transport noise samples to meta samples.
Our meta sampler is parameterized by a neural inverse-autoregressive flow (NIAF), an extension of the recently developed neural autoregressive flows (NAFs) (Huang et al., 2018) .
The NIAF consists of a meta-sample generator and an autoregressive conditioner model, which outputs the parameters of the meta-sample generator.
The NIAF takes some task-specific information (such as gradients of target distributions) and random noise as input and outputs meta samples from its generator.
These meta samples are then quickly adapted to task-specific samples of target distributions by feeding them to the sample adapter.
To ensure efficient and accurate adaptations to new task distributions, a novel optimal-transport Bayesian sampling (OP-sampling) scheme, based on Wasserstein gradient flows, is proposed as the adaptation mechanism of the sample adapter.
The OP-sampling is general and can ensure samples to be adapted in a way that makes the sample density evolve to a target distribution optimally, thus endowing the property of fast uncertainty adaption.
Finally, when one aims to perform specific tasks such as Bayesian classification with a task network, these samples are used to encode uncertainty into modeling.
To this end, we further develop an efficient learning algorithm to optimize the task network based on variational inference.
Extensive experiments are conducted to test the advantages of the proposed meta-sampling framework, ranging from synthetic-distribution to posterior-distribution adaption and to k-shot learning in Bayesian neural networks and reinforcement learning.
Our results demonstrate a better performance of the proposed model compared to related methods.
We present a Bayesian meta-sampling framework, called DAMS, consisting of a meta sampler and a sample adapter for effective uncertainty adaption.
Our model is based on the recently proposed neural autoregressive flows and related theory from optimal transport, enabling a simple yet effective training procedure.
To make the proposed model scalable, an efficient uncertainty parameterization is proposed for the task network, which is trained by variational inference.
DAMS is general and can be applied to different scenarios with an ability for fast uncertainty adaptation.
Experiments on a series of tasks demonstrate the advantages of the proposed framework over other methods including the recently proposed meta SG-MCMC, in terms of both sample efficiency and fast uncertainty adaption.

assistant: text: We proposed a Bayesian meta sampling method for adapting the model uncertainty in meta learning