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update website for 2025 including change templating and styling
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‎2024/index.md‎

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---
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layout: workshop
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year: 2024
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date: May 11th, 2024
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location: <a href="iclr.cc/2024">ICLR 2024</a> in Vienna, Austria
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paper_url: https://arxiv.org/abs/2310.13018
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cover_image:
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image: "/assets/images/locations/singapore.jpg"
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author: Fabian Lackner
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licence_name: "CC BY-SA 3.0"
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licence_link: "https://creativecommons.org/licenses/by-sa/3.0/"
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buttons:
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- text: OpenReview
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url: https://openreview.net/group?id=ICLR.cc/2024/Workshop/Re-Align
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- text: ICLR Schedule
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url: https://iclr.cc/virtual/2024/workshop/20578
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show_news: true
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show_dates: false
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show_cfp: false
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show_schedule: false
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show_reviewers: true
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show_sponsors: true
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speakers:
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- name: SueYeon Chung
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affiliation: New York University
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website: https://sites.google.com/site/sueyeonchung/
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image: profile_chung.jpeg
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- name: Andrew Lampinen
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affiliation: Google DeepMind
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website: https://lampinen.github.io/
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image: profile_lampinen.jpeg
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- name: Bradley Love
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affiliation: University College London
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website: http://bradlove.org/
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image: profile_love.jpeg
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- name: Simon Kornblith
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affiliation: Anthropic
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website: https://simonster.com/
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image: profile_kornblith.jpeg
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- name: Mariya Toneva
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affiliation: Max Planck Institute for Software Systems
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website: https://mtoneva.com/
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image: profile_toneva.jpeg
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organizers:
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- name: Ilia Sucholutsky
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affiliation: Princeton University
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website: https://ilia10000.github.io/
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image: ilia.jpeg
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- name: Lukas Muttenthaler
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affiliation: TU Berlin
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website: https://lukasmut.github.io/
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image: lukas.jpeg
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- name: Erin Grant
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affiliation: University College London
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website: https://eringrant.github.io/
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image: erin.jpeg
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- name: Katherine Hermann
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affiliation: Google DeepMind
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website: https://www.linkedin.com/in/katherine-hermann-0a319a1b7
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image: katherine.jpeg
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- name: Jascha Achterberg
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affiliation: University of Cambridge
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website: https://www.jachterberg.com/
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image: jascha.jpeg
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sponsors:
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- name: EY
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url: https://www.ey.com/en_gl
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image: EY.png
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news:
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- content: We've update the Re-Align position paper ([📄 paper](https://arxiv.org/abs/2310.13018)) with new insights generated during the workshop. Check it out!
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- content: Thank you to our invited speakers and panellists, contributing authors, and attendees for a successful workshop! The video recording of all sessions are now available on the [ICLR.cc website](https://iclr.cc/virtual/2024/workshop/20578).
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- content: All decisions have been sent out and the list of accepted papers and the updated workshop schedule can be found below. We are super excited to see you all in Vienna!
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about: |
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The question of <i>What makes a good representation?</i> in machine
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learning can be addressed in one of several ways: By evaluating
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downstream behavior, by inspecting internal representations, or by
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characterizing a system's inductive biases. Each of these
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methodologies involves measuring the alignment of an artificial
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intelligence (AI) system to a ground truth system (usually a human or
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a population of humans) at some level of analysis (be it behavior,
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internal representation, or something in between). However, despite
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this shared goal, the machine learning, neuroscience, and cognitive
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science communities that study alignment among artificial and
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biological intelligence systems currently lack a shared framework for
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conveying insights across methodologies and disciplines.
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This workshop aims to bridge this gap by
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<strong>defining, evaluating, and understanding the implications of
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representational alignment among biological & artificial
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systems</strong>. We invite researchers across the machine learning, neuroscience,
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and cognitive science communities to contribute to this discussion in
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the form of invited talks, contributed papers, and structured
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discussions that address questions such as:
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- How can we measure representational alignment among biological and artificial intelligence (AI) systems?
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- Can representational alignment tell us if AI systems use the same strategies to solve tasks as humans do?
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- What are the consequences (positive, neutral, and negative) of representational alignment?
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- How does representational alignment connect to behavioral alignment and value alignment, as understood in AI safety and interpretability & explainability?
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- How can we increase (or decrease) representational alignment of an AI system?
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- How does the degree of representational alignment between two systems impact their ability to compete, cooperate, and communicate?
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In collaboration with other researchers, the organizers have prepared
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a position paper ([📄 paper](https://arxiv.org/abs/2310.13018))
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that collects prior work and highlights key issues on the topic of
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representational alignment. A concrete goal of the workshop is to
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expand this paper with any new insights generated during the workshop.
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program_committee:
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name: Andreea Bobu
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name: Andrew Lampinen
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name: Valentino Maiorca
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name: Akash Nagaraj
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name: Dota (Tianai) Dong
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- url: https://openreview.net/profile?id=~Jinyung_Hong1
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name: Jinyung Hong
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name: Jonas Dippel
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name: Jacob Zavatone-Veth
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name: Stephanie Fu
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name: Sunayana Rane
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name: Elom Amematsro
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name: Alex Murphy
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name: Jenelle Feather
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name: Harry (Yizhou) Tian
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name: Michael Lepori
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---

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