- [`poutine.broadcast`](http://docs.pyro.ai/en/dev/poutine.html#pyro.poutine.broadcast) is a new effect hadler that allows sample site shapes to be automatically broadcast based on their enclosig `iarange`s. This makes it very easy to experiment with different models by moving sample sites in and out of `iarange`s without any manual `.expand()` changes. See the [tensor shapes tutorial](http://pyro.ai/examples/tensor_shapes.html) for details.
- [`pyro.optim.PyroLRScheduler`](http://docs.pyro.ai/en/dev/optimization.html#pyro.optim.lr_scheduler.PyroLRScheduler) makes it easy to use [PyTorch learning rate schedulers](https://pytorch.org/docs/master/optim.html#how-to-adjust-learning-rate) in Pyro.
- [`pyro.contrib.autoguide`](http://docs.pyro.ai/en/dev/contrib.autoguide.html) now supports custom name prefixes and has more thorough error messages for name collision. This makes it easier to combine multiple autoguide strategies.
- [`pyro.ops.newton.newton_step_2d`](http://docs.pyro.ai/en/dev/ops.html?highlight=newton_step_2d#pyro.ops.newton.newton_step_2d) is a fast differentiable optimizer for batched 2-dimensional loss functions that are themselves twice differentiable.
- [`pyro.contrib.gp.kernels.Coregionalize`](http://docs.pyro.ai/en/dev/contrib.gp.html#coregionalize) and [`pyro.contrib.autoguide.AutoLowRankMultivariateNormal`](http://docs.pyro.ai/en/dev/contrib.autoguide.html#pyro.contrib.autoguide.AutoLowRankMultivariateNormal) both provide models multivariate data with low-rank plus diagonal covariance.
- [`TorchDistribution.expand()`](http://docs.pyro.ai/en/dev/distributions.html#pyro.distributions.torch_distribution.TorchDistributionMixin.expand) is more flexible and more PyTorch idiomatic than the older `TorchDistribution.expand_by()`.
- Miscellaneous bugfixes