Bump to 0.1.0

CHANGELOG.md

@@ -16,16 +16,21 @@ To release a new version (e.g. from `1.0.0` -> `2.0.0`):
   `[Unreleased]` notes.
 * At the end of the file:
   * Define the new link url:
-  `[2.0.0]: https://github.com/jax-ml/coix/compare/v1.0.0...v2.0.0`
-  * Update the `[Unreleased]` url: `v1.0.0...HEAD` -> `v2.0.0...HEAD`
+  `[2.0.0]: https://github.com/jax-ml/coix/compare/1.0.0...2.0.0`
+  * Update the `[Unreleased]` url: `1.0.0...HEAD` -> `2.0.0...HEAD`
 
 -->
 
 ## [Unreleased]
 
+## [0.1.0] - 2024-04-17
+
+* First stable release, including documentations, tutorials, and examples.
+
 ## [0.0.1] - 2023-04-25
 
 * Initial release for testing (please don't use)
 
-[Unreleased]: https://github.com/jax-ml/coix/compare/v0.0.1...HEAD
-[0.0.1]: https://github.com/jax-ml/coix/releases/tag/v0.0.1
+[Unreleased]: https://github.com/jax-ml/coix/compare/0.1.0...HEAD
+[0.1.0]: https://github.com/jax-ml/coix/releases/tag/0.1.0
+[0.0.1]: https://github.com/jax-ml/coix/releases/tag/0.0.1

coix/__init__.py

@@ -55,4 +55,4 @@
 
 # A new PyPI release will be pushed everytime `__version__` is increased
 # When changing this, also update the CHANGELOG.md
-__version__ = "0.0.1"
+__version__ = "0.1.0"

coix/algo.py

@@ -65,9 +65,9 @@ def _use_fori_loop(targets, num_targets, *fns):
 def aft(targets, flows, *, num_targets=None):
   """Annealed Flow Transport.
 
-  [1] Annealed Flow Transport Monte Carlo,
-      Michael Arbel, Alexander G. D. G. Matthews, Arnaud Doucet
-      https://arxiv.org/abs/2102.07501
+  1. *Annealed Flow Transport Monte Carlo*,
+     Michael Arbel, Alexander G. D. G. Matthews, Arnaud Doucet
+     https://arxiv.org/abs/2102.07501
 
   Args:
     targets: a list of target programs
@@ -94,10 +94,10 @@ def body_fun(i, q):
 def apgs(target, kernels, *, num_sweeps=1):
   """Amortized Population Gibbs Sampler.
 
-  [1] Amortized Population Gibbs Samplers with Neural Sufficient Statistics,
-      Hao Wu, Heiko Zimmermann, Eli Sennesh, Tuan Anh Le, Jan-Willem van de
-      Meent
-      https://arxiv.org/abs/1911.01382
+  1. *Amortized Population Gibbs Samplers with Neural Sufficient Statistics*,
+     Hao Wu, Heiko Zimmermann, Eli Sennesh, Tuan Anh Le, Jan-Willem van de
+     Meent
+     https://arxiv.org/abs/1911.01382
 
   Args:
     target: the target program
@@ -123,13 +123,13 @@ def body_fn(_, q):
 def dais(targets, momentum, leapfrog, refreshment, *, num_targets=None):
   """Differentiable Annealed Importance Sampling.
 
-  [1] MCMC Variational Inference via Uncorrected Hamiltonian Annealing,
-      Tomas Geffner, Justin Domke
-      https://arxiv.org/abs/2107.04150
-  [2] Differentiable Annealed Importance Sampling and the Perils of Gradient
-      Noise,
-      Guodong Zhang, Kyle Hsu, Jianing Li, Chelsea Finn, Roger Grosse
-      https://arxiv.org/abs/2107.10211
+  1. *MCMC Variational Inference via Uncorrected Hamiltonian Annealing*,
+     Tomas Geffner, Justin Domke
+     https://arxiv.org/abs/2107.04150
+  2. *Differentiable Annealed Importance Sampling and the Perils of Gradient
+     Noise*,
+     Guodong Zhang, Kyle Hsu, Jianing Li, Chelsea Finn, Roger Grosse
+     https://arxiv.org/abs/2107.10211
 
   Args:
     targets: a list of target programs
@@ -166,9 +166,9 @@ def body_fun(i, q):
 def nasmc(targets, proposals, *, num_targets=None):
   """Neural Adaptive Sequential Monte Carlo.
 
-  [1] Neural Adaptive Sequential Monte Carlo,
-      Shixiang Gu, Zoubin Ghahramani, Richard E. Turner
-      https://arxiv.org/abs/1506.03338
+  1. *Neural Adaptive Sequential Monte Carlo*,
+     Shixiang Gu, Zoubin Ghahramani, Richard E. Turner
+     https://arxiv.org/abs/1506.03338
 
   Args:
     targets: a list of target programs
@@ -196,10 +196,10 @@ def body_fun(i, q):
 def nvi_avo(targets, forwards, reverses, *, num_targets=None):
   """AIS with Annealed Variational Objective.
 
-  [1] Improving Explorability in Variational Inference with Annealed Variational
-      Objectives,
-      Chin-Wei Huang, Shawn Tan, Alexandre Lacoste, Aaron Courville
-      https://arxiv.org/abs/1809.01818
+  1. *Improving Explorability in Variational Inference with Annealed Variational
+     Objectives*,
+     Chin-Wei Huang, Shawn Tan, Alexandre Lacoste, Aaron Courville
+     https://arxiv.org/abs/1809.01818
 
   Args:
     targets: a list of target programs
@@ -231,9 +231,9 @@ def nvi_fkl(targets, proposals, *, num_targets=None):
   This is different from `nasmc`, where we assume that the targets are filtering
   distributions. We also assume that the final target does not have parameters.
 
-  [1] Nested Variational Inference,
-      Heiko Zimmermann, Hao Wu, Babak Esmaeili, Jan-Willem van de Meent
-      https://arxiv.org/abs/2106.11302
+  1. *Nested Variational Inference*,
+     Heiko Zimmermann, Hao Wu, Babak Esmaeili, Jan-Willem van de Meent
+     https://arxiv.org/abs/2106.11302
 
   Args:
     targets: a list of target programs
@@ -270,9 +270,9 @@ def nvi_rkl(targets, forwards, reverses, *, num_targets=None):
   initial target to the final target. Here we use ELBO loss in the last step
   to also maximize likelihood in case there are parameters in the final target.
 
-  [1] Nested Variational Inference,
-      Heiko Zimmermann, Hao Wu, Babak Esmaeili, Jan-Willem van de Meent
-      https://arxiv.org/abs/2106.11302
+  1. *Nested Variational Inference*,
+     Heiko Zimmermann, Hao Wu, Babak Esmaeili, Jan-Willem van de Meent
+     https://arxiv.org/abs/2106.11302
 
   Args:
     targets: a list of target programs
@@ -302,12 +302,12 @@ def body_fun(i, q):
 def rws(target, proposal):
   """Reweighted Wake-Sleep.
 
-  [1] Reweighted Wake-Sleep,
-      Jörg Bornschein, Yoshua Bengio
-      https://arxiv.org/abs/1406.2751
-  [2] Revisiting Reweighted Wake-Sleep for Models with Stochastic Control Flow,
-      Tuan Anh Le, Adam R. Kosiorek, N. Siddharth, Yee Whye Teh, Frank Wood
-      https://arxiv.org/abs/1805.10469
+  1. *Reweighted Wake-Sleep*,
+     Jörg Bornschein, Yoshua Bengio
+     https://arxiv.org/abs/1406.2751
+  2. *Revisiting Reweighted Wake-Sleep for Models with Stochastic Control Flow*,
+     Tuan Anh Le, Adam R. Kosiorek, N. Siddharth, Yee Whye Teh, Frank Wood
+     https://arxiv.org/abs/1805.10469
 
   Args:
     target: the target program
@@ -322,13 +322,13 @@ def rws(target, proposal):
 def svi(target, proposal):
   """Stochastic Variational Inference.
 
-  [1] Auto-Encoding Variational Bayes,
-      Diederik P Kingma, Max Welling
-      https://arxiv.org/abs/1312.6114
-  [2] Stochastic Backpropagation and Approximate Inference in Deep Generative
-      Models,
-      Danilo Jimenez Rezende, Shakir Mohamed, Daan Wierstra
-      https://arxiv.org/abs/1401.4082
+  1. *Auto-Encoding Variational Bayes*,
+     Diederik P Kingma, Max Welling
+     https://arxiv.org/abs/1312.6114
+  2. *Stochastic Backpropagation and Approximate Inference in Deep Generative
+     Models*,
+     Danilo Jimenez Rezende, Shakir Mohamed, Daan Wierstra
+     https://arxiv.org/abs/1401.4082
 
   Args:
     target: the target program
@@ -343,9 +343,9 @@ def svi(target, proposal):
 def svi_iwae(target, proposal):
   """SVI with Important Weighted Autoencoder objective.
 
-  [1] Importance Weighted Autoencoders,
-      Yuri Burda, Roger Grosse, Ruslan Salakhutdinov
-      https://arxiv.org/abs/1509.00519
+  1. *Importance Weighted Autoencoders*,
+     Yuri Burda, Roger Grosse, Ruslan Salakhutdinov
+     https://arxiv.org/abs/1509.00519
 
   Args:
     target: the target program
@@ -360,10 +360,10 @@ def svi_iwae(target, proposal):
 def svi_stl(target, proposal):
   """SVI with Sticking-the-Landing objective.
 
-  [1] Sticking the Landing: Simple, Lower-Variance Gradient Estimators for
-      Variational Inference,
-      Geoffrey Roeder, Yuhuai Wu, David Duvenaud
-      https://arxiv.org/abs/1703.09194
+  1. *Sticking the Landing: Simple, Lower-Variance Gradient Estimators for
+     Variational Inference*,
+     Geoffrey Roeder, Yuhuai Wu, David Duvenaud
+     https://arxiv.org/abs/1703.09194
 
   Args:
     target: the target program
@@ -382,20 +382,20 @@ def vsmc(targets, proposals, *, num_targets=None):
   masking) during SMC steps. The targets can be filtering distributions or
   smoothing distributions (as in [4]).
 
-  [1] Filtering Variational Objectives,
-      Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess,
-      Mohammad Norouzi, Andriy Mnih, Arnaud Doucet, Yee Whye Teh
-      https://arxiv.org/abs/1705.09279
-  [2] Auto-Encoding Sequential Monte Carlo,
-      Tuan Anh Le, Maximilian Igl, Tom Rainforth, Tom Jin, Frank Wood
-      https://arxiv.org/abs/1705.10306
-  [3] Variational Sequential Monte Carlo,
-      Christian A. Naesseth, Scott W. Linderman, Rajesh Ranganath, David M. Blei
-      https://arxiv.org/abs/1705.11140
-  [4] Twisted Variational Sequential Monte Carlo,
-      Dieterich Lawson, George Tucker, Christian A Naesseth, Chris J Maddison,
-      Ryan P Adams, Yee Whye Teh
-      http://bayesiandeeplearning.org/2018/papers/111.pdf
+  1. *Filtering Variational Objectives*,
+     Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess,
+     Mohammad Norouzi, Andriy Mnih, Arnaud Doucet, Yee Whye Teh
+     https://arxiv.org/abs/1705.09279
+  2. *Auto-Encoding Sequential Monte Carlo*,
+     Tuan Anh Le, Maximilian Igl, Tom Rainforth, Tom Jin, Frank Wood
+     https://arxiv.org/abs/1705.10306
+  3. *Variational Sequential Monte Carlo*,
+     Christian A. Naesseth, Scott W. Linderman, Rajesh Ranganath, David M. Blei
+     https://arxiv.org/abs/1705.11140
+  4. *Twisted Variational Sequential Monte Carlo*,
+     Dieterich Lawson, George Tucker, Christian A Naesseth, Chris J Maddison,
+     Ryan P Adams, Yee Whye Teh
+     http://bayesiandeeplearning.org/2018/papers/111.pdf
 
   Args:
     targets: a list of target programs

coix/api.py

@@ -125,7 +125,7 @@ def propose(p, q, *, loss_fn=None, detach=False, chain=False):
   `vmap(propose(p, q))`.
 
   Note: We assume superfluous variables, which appear in `q` but not in `p`,
-    implicitly follow Delta distribution in `p`.
+  implicitly follow Delta distribution in `p`.
 
   Args:
     p: a target program