Add v177 pages

Gemfile

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+source "https://rubygems.org"
+
+git_source(:github) {|repo_name| "https://github.com/#{repo_name}" }
+
+gem 'jekyll'
+
+group :jekyll_plugins do
+  gem 'github-pages'
+  gem 'jekyll-remote-theme'
+  gem 'jekyll-include-cache'
+  gem 'webrick'
+end
+
+# gem "rails"
+

README.md

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+# PMLR 177
+
+To suggest fixes to this volume please make a pull request containing the changes requested and a justification for the changes.
+
+To edit the details of this conference work edit the [_config.yml](./_config.yml) file and submit a pull request.
+
+To make changes to the individual paper details, edit the associated paper file in the [./_posts](./_posts) subdirectory.
+
+For details of how to publish in PMLR please check https://proceedings.mlr.press/faq.html
+
+For details of what is required to submit a proceedings please check https://proceedings.mlr.press/spec.html
+
+
+
+Published as Volume 177 by the Proceedings of Machine Learning Research on 28 June 2022.
+
+Volume Edited by:
+  * Bernhard Schölkopf
+  * Caroline Uhler
+  * Kun Zhang
+
+Series Editors:
+  * Neil D. Lawrence

_config.yml

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+---
+booktitle: Proceedings of the First Conference on Causal Learning and Reasoning
+conference_number: '1'
+end: 2022-04-13
+published: 2022-06-28
+shortname: CLeaR
+start: 2022-04-11
+volume: '177'
+layout: proceedings
+series: Proceedings of Machine Learning Research
+publisher: PMLR
+issn: 2640-3498
+id: clear2022
+month: 0
+cycles: false
+bibtex_editor: Sch{\"o}lkopf, Bernhard and Uhler, Caroline and Zhang, Kun
+editor:
+- given: Bernhard
+  family: Schölkopf
+- given: Caroline
+  family: Uhler
+- given: Kun
+  family: Zhang
+title: Proceedings of Machine Learning Research
+description: |
+  Proceedings of the First Conference on Causal Learning and Reasoning
+    Held in Sequoia Conference Center, Eureka, CA, USA on 11-13 April 2022
+
+  Published as Volume 177 by the Proceedings of Machine Learning Research on 28 June 2022.
+
+  Volume Edited by:
+    Bernhard Schölkopf
+    Caroline Uhler
+    Kun Zhang
+
+  Series Editors:
+    Neil D. Lawrence
+date_str: 11--13 Apr
+url: https://proceedings.mlr.press
+author:
+  name: PMLR
+baseurl: "/v177"
+twitter_username: MLResearchPress
+github_username: mlresearch
+markdown: kramdown
+exclude:
+- README.md
+- Gemfile
+- ".gitignore"
+plugins:
+- jekyll-feed
+- jekyll-seo-tag
+- jekyll-remote-theme
+remote_theme: lawrennd/proceedings
+style: pmlr
+permalink: "/:title.html"
+ghub:
+  edit: true
+  repository: v177
+display:
+  copy_button:
+    bibtex: true
+    endnote: true
+    apa: true
+  comments: false
+volume_type: Volume
+volume_dir: v177
+email: ''
+conference:
+  name: Conference on Causal Learning and Reasoning
+  url: https://cclear.cc
+  location: Sequoia Conference Center, Eureka, CA, USA
+  dates:
+  - 2022-04-11
+  - 2022-04-12
+  - 2022-04-13
+analytics:
+  google:
+    tracking_id: UA-92432422-1
+orig_bibfile: "/Users/neil/mlresearch/v177/clear2022.bib"
+# Site settings
+# Original source:  /Users/neil/mlresearch/v177/clear2022.bib

_posts/2022-06-28-ahsan22a.md

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+---
+abstract: Causal reasoning in relational domains is fundamental to studying real-world
+  social phenomena in which individual units can influence each other’s traits and
+  behavior. Dynamics between interconnected units can be represented as an instantiation
+  of a relational causal model; however, causal reasoning over such instantiation
+  requires additional templating assumptions that capture feedback loops of influence.
+  Previous research has developed lifted representations to address the relational
+  nature of such dynamics but has strictly required that the representation has no
+  cycles. To facilitate cycles in relational representation and learning, we introduce
+  relational $\sigma$-separation, a new criterion for understanding relational systems
+  with feedback loops. We also introduce a new lifted representation, $\sigma$-\textit{abstract
+  ground graph} which helps with abstracting statistical independence relations in
+  all possible instantiations of the cyclic relational model. We show the necessary
+  and sufficient conditions for the completeness of $\sigma$-AGG and that relational
+  $\sigma$-separation is sound and complete in the presence of one or more cycles
+  with arbitrary length. To the best of our knowledge, this is the first work on representation
+  of and reasoning with cyclic relational causal models.
+booktitle: First Conference on Causal Learning and Reasoning
+title: 'Relational Causal Models with Cycles: Representation and Reasoning'
+year: '2022'
+layout: inproceedings
+series: Proceedings of Machine Learning Research
+publisher: PMLR
+issn: 2640-3498
+id: ahsan22a
+month: 0
+tex_title: 'Relational Causal Models with Cycles: Representation and Reasoning'
+firstpage: 1
+lastpage: 18
+page: 1-18
+order: 1
+cycles: false
+bibtex_author: Ahsan, Ragib and Arbour, David and Zheleva, Elena
+author:
+- given: Ragib
+  family: Ahsan
+- given: David
+  family: Arbour
+- given: Elena
+  family: Zheleva
+date: 2022-06-28
+address:
+container-title: Proceedings of the First Conference on Causal Learning and Reasoning
+volume: '177'
+genre: inproceedings
+issued:
+  date-parts:
+  - 2022
+  - 6
+  - 28
+pdf: https://proceedings.mlr.press/v177/ahsan22a/ahsan22a.pdf
+extras: []
+# Format based on citeproc: http://blog.martinfenner.org/2013/07/30/citeproc-yaml-for-bibliographies/
+---

_posts/2022-06-28-ahuja22a.md

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+---
+abstract: 'Humans have a remarkable ability to disentangle complex sensory inputs
+  (e.g., image, text) into simple factors of variation (e.g., shape, color) without
+  much supervision. This ability has inspired many works that attempt to solve the
+  following question: how do we invert the data generation process to extract those
+  factors with minimal or no supervision? Several works in the literature on non-linear
+  independent component analysis have established this negative result; without some
+  knowledge of the data generation process or appropriate inductive biases, it is
+  impossible to perform this inversion. In recent years, a lot of progress has been
+  made on disentanglement under structural assumptions, e.g., when we have access
+  to auxiliary information that makes the factors of variation conditionally independent.
+  However, existing work requires a lot of auxiliary information, e.g., in supervised
+  classification, it prescribes that the number of label classes should be at least
+  equal to the total dimension of all factors of variation. In this work, we depart
+  from these assumptions and ask: a) How can we get disentanglement when the auxiliary
+  information does not provide conditional independence over the factors of variation?
+  b) Can we reduce the amount of auxiliary information required for disentanglement?
+  For a class of models where auxiliary information does not ensure conditional independence,
+  we show theoretically and experimentally that disentanglement (to a large extent)
+  is possible even when the auxiliary information dimension is much less than the
+  dimension of the true latent representation.'
+booktitle: First Conference on Causal Learning and Reasoning
+title: Towards efficient representation identification in supervised learning
+year: '2022'
+layout: inproceedings
+series: Proceedings of Machine Learning Research
+publisher: PMLR
+issn: 2640-3498
+id: ahuja22a
+month: 0
+tex_title: Towards efficient representation identification in supervised learning
+firstpage: 19
+lastpage: 43
+page: 19-43
+order: 19
+cycles: false
+bibtex_author: Ahuja, Kartik and Mahajan, Divyat and Syrgkanis, Vasilis and Mitliagkas,
+  Ioannis
+author:
+- given: Kartik
+  family: Ahuja
+- given: Divyat
+  family: Mahajan
+- given: Vasilis
+  family: Syrgkanis
+- given: Ioannis
+  family: Mitliagkas
+date: 2022-06-28
+address:
+container-title: Proceedings of the First Conference on Causal Learning and Reasoning
+volume: '177'
+genre: inproceedings
+issued:
+  date-parts:
+  - 2022
+  - 6
+  - 28
+pdf: https://proceedings.mlr.press/v177/ahuja22a/ahuja22a.pdf
+extras: []
+# Format based on citeproc: http://blog.martinfenner.org/2013/07/30/citeproc-yaml-for-bibliographies/
+---

_posts/2022-06-28-ali22a.md

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+---
+abstract: 'We consider the problem of extracting semantic attributes, using only classification
+  labels for supervision. For example, when learning to classify images of birds into
+  species, we would like to observe the emergence of features used by zoologists to
+  classify birds. To tackle this problem, we propose training a neural network with
+  discrete features in the last layer, followed by two heads: a multi-layered perceptron
+  (MLP) and a decision tree. The decision tree utilizes simple binary decision stumps,
+  thus encouraging features to have semantic meaning. We present theoretical analysis,
+  as well as a practical method for learning in the intersection of two hypothesis
+  classes. Compared with various benchmarks, our results show an improved ability
+  to extract a set of features highly correlated with a ground truth set of unseen
+  attributes.'
+booktitle: First Conference on Causal Learning and Reasoning
+title: Weakly Supervised Discovery of Semantic Attributes
+year: '2022'
+layout: inproceedings
+series: Proceedings of Machine Learning Research
+publisher: PMLR
+issn: 2640-3498
+id: ali22a
+month: 0
+tex_title: Weakly Supervised Discovery of Semantic Attributes
+firstpage: 44
+lastpage: 69
+page: 44-69
+order: 44
+cycles: false
+bibtex_author: Ali, Ameen Ali and Galanti, Tomer and Zheltonozhskii, Evgenii and Baskin,
+  Chaim and Wolf, Lior
+author:
+- given: Ameen Ali
+  family: Ali
+- given: Tomer
+  family: Galanti
+- given: Evgenii
+  family: Zheltonozhskii
+- given: Chaim
+  family: Baskin
+- given: Lior
+  family: Wolf
+date: 2022-06-28
+address:
+container-title: Proceedings of the First Conference on Causal Learning and Reasoning
+volume: '177'
+genre: inproceedings
+issued:
+  date-parts:
+  - 2022
+  - 6
+  - 28
+pdf: https://proceedings.mlr.press/v177/ali22a/ali22a.pdf
+extras: []
+# Format based on citeproc: http://blog.martinfenner.org/2013/07/30/citeproc-yaml-for-bibliographies/
+---

_posts/2022-06-28-assouel22a.md

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+---
+abstract: We introduce a variational inference model called VIM, for Variational Independent
+  Modules, for sequential data that learns and infers latent representations as a
+  set of objects and discovers modular causal mechanisms over these objects. These
+  mechanisms - which we call modules - are independently parametrized, define the
+  stochastic transitions of entities and are shared across entities.  At each time
+  step, our model infers from a low-level input sequence a high-level sequence of
+  categorical latent variables to select which transition modules to apply to which
+  high-level object. We evaluate this model in video prediction tasks where the goal
+  is to predict multi-modal future events given previous observations. We demonstrate
+  empirically that VIM can model 2D visual sequences in an interpretable way and is
+  able to identify the underlying dynamically instantiated mechanisms of the generation
+  process.  We additionally show that the learnt modules can be composed at test time
+  to generalize to out-of-distribution observations.
+booktitle: First Conference on Causal Learning and Reasoning
+title: 'VIM: Variational Independent Modules for Video Prediction'
+year: '2022'
+layout: inproceedings
+series: Proceedings of Machine Learning Research
+publisher: PMLR
+issn: 2640-3498
+id: assouel22a
+month: 0
+tex_title: "{VIM}: Variational Independent Modules for Video Prediction"
+firstpage: 70
+lastpage: 89
+page: 70-89
+order: 70
+cycles: false
+bibtex_author: Assouel, Rim and Castrejon, Lluis and Courville, Aaron and Ballas,
+  Nicolas and Bengio, Yoshua
+author:
+- given: Rim
+  family: Assouel
+- given: Lluis
+  family: Castrejon
+- given: Aaron
+  family: Courville
+- given: Nicolas
+  family: Ballas
+- given: Yoshua
+  family: Bengio
+date: 2022-06-28
+address:
+container-title: Proceedings of the First Conference on Causal Learning and Reasoning
+volume: '177'
+genre: inproceedings
+issued:
+  date-parts:
+  - 2022
+  - 6
+  - 28
+pdf: https://proceedings.mlr.press/v177/assouel22a/assouel22a.pdf
+extras: []
+# Format based on citeproc: http://blog.martinfenner.org/2013/07/30/citeproc-yaml-for-bibliographies/
+---