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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
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<title>DJ Strouse</title>
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<table width="800" border="0" align="center" cellspacing="0" cellpadding="0">
<tr><td>
<table width="100%" align="center" border="0" cellspacing="0" cellpadding="20">
<tr><td width="67%" valign="middle">
<p align="center">
<name>DJ Strouse</name>
</p><p>
I am an AI Research Scientist at Meta's TBD Lab.
</p><p>
Previously, I was a Member of Technical Staff at <a href="https://openai.com/">OpenAI</a> in San Francisco, working with the Science of RL team on healthy optimization and compute-optimal scaling. Before that, I was a Research Scientist at <a href="https://deepmind.com/">DeepMind</a> in London and New York, where I worked on improving the reasoning capabilities of frontier models with the Blueshift team. I did my PhD in Physics at Princeton University, advised by <a href="http://davidjschwab.com/">David Schwab</a> and <a href="https://www.princeton.edu/~wbialek/wbialek.html">Bill Bialek</a>, and funded by a <a href="http://hertzfoundation.org/">Hertz Fellowship</a> and <a href="https://www.krellinst.org/csgf/">DOE Computational Science Graduate Fellowship</a>. Before that, I did a master's at the University of Cambridge with <a href="http://www3.eng.cam.ac.uk/~ml468/">Mate Lengyel</a> as a <a href="http://www.winstonchurchillfoundation.org/scholarship.html">Churchill Scholar</a> and studied physics and mathematics at the University of Southern California, where I worked with <a href="http://profiles.sc-ctsi.org/bartlett.mel">Bartlett Mel</a> and <a href="https://dornsife.usc.edu/cf/faculty-and-staff/faculty.cfm?pid=1016223">Paolo Zanardi</a> and had a <a href="http://djstrouse.com/blog/">blog</a>. Throughout my studies, I interned at <a href="https://deepmind.com/">DeepMind</a> with <a href="https://scholar.google.com/citations?user=eM916YMAAAAJ">Matt Botvinick</a>, <a href="https://biosciences.stanford.edu/prospective/diversity/ssrp/">Stanford University</a> with <a href="https://web.stanford.edu/group/brainsinsilicon/boahen.html">Kwabena Boahen</a>, the <a href="https://uwaterloo.ca/institute-for-quantum-computing/">Institute for Quantum Computing</a> with <a href="https://www.cs.umd.edu/~amchilds/">Andrew Childs</a>, and <a href="https://www.spotify.com">Spotify</a> NYC with their machine learning team. I also enjoy a <a href="https://docs.google.com/document/d/1q3p4xz0C6qplW8JakNJknF4cnURTTNX7oz2o_kM0WmQ/edit?usp=sharing">good puzzle</a>.
</p><p align=center>
<a href="mailto:danieljstrouse@gmail.com">Email</a>  | 
<a href="https://twitter.com/djstrouse">Twitter</a>  | 
<a href="downloads/DJStrouseCV.pdf">CV</a>  | 
<a href="https://scholar.google.com/citations?user=K8E0T7MAAAAJ">Scholar</a><!--  |  -->
<!-- <a href="https://github.com/djstrouse">GitHub</a> -->
</p></td>
<td width="33%">
<img src="images/dj_circle.png"> <!-- should be 280x280 -->
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<!-- RESEARCH SECTION -->
<table width="100%" align="center" border="0" cellspacing="0" cellpadding="20">
<tr> <td width="100%" valign="middle">
<heading>Research</heading> <p>
I'm broadly interested in improving reasoning capabilities in frontier models. Previously, I've worked on a variety of topics in reinforcement learning, including exploration, and training agents to play cooperative games with humans. My <a href="downloads/DJStrouseThesis.pdf">PhD thesis ("Optimization of MILES")</a> focused on applications of the information bottleneck (IB) across supervised, unsupervised, and reinforcement learning, and definitely not on collecting airline miles. In past lives, I've also worked on quantum information theory and computational neuroscience.
<tr> <td width="100%" valign="middle">
<heading>Select Publications</heading> </td> </tr> </table>
<table width="100%" align="center" border="0" cellspacing="0" cellpadding="20">
<!-- <tr> <td width="100%" valign="middle"> -->
<!-- <heading>Select Publications</heading> <p> -->
<!-- project begin -->
<tr id="gemini1p5pro2024"><td class="paperthumbcol">
<!-- project symbol -->
<img src="./images/gemini_1p5pro.jpg" class="paperthumb">
</td><td class="papertextcol">
<!-- title -->
<a href="https://arxiv.org/abs/2403.05530">
<papertitle>Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context</papertitle></a><br>
<!-- authors -->
Gemini Team<br>
<!-- publication status -->
<em>arxiv</em>, 2024<br>
<!-- links -->
<a href="https://arxiv.org/abs/2403.05530">arxiv</a> |
<a href="https://deepmind.google/technologies/gemini/pro/">website</a> |
<a href="https://x.com/djstrouse/status/1791611473686360282">tweet</a> |
<a href="javascript:toggleAbsVsBib('gemini1p5pro2024')" id="toggle">show bibtex</a>
<p></p>
<!-- project description -->
<p id="abstract">We trained a math-specialized version of Gemini 1.5 Pro that was the first model to publicly exceed 90% on Hendrycks MATH, a benchmark of difficult competition-level high school math problems. See Section 7 of <a href="https://arxiv.org/abs/2403.05530">the report</a> for more details, or the tweets from <a href="https://twitter.com/OriolVinyalsML/status/1791521517211107515">Oriol</a>, <a href="https://x.com/JeffDean/status/1791522915021627438">Jeff</a>, <a href="https://x.com/bneyshabur/status/1792304689335480511">Behnam</a>, and <a href="https://x.com/sundarpichai/status/1791582982870089752">Sundar</a>. </p>
<!-- bibtex -->
<p id="bibtex"><tt>
@misc{geminiteam2024gemini1p5, <br>
title = {Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context}, <br>
author = {Gemini Team}, <br>
year = {2024}, <br>
eprint = {2403.05530}, <br>
archivePrefix={arXiv}, <br>
primaryClass={cs.CL}, <br>
url={https://arxiv.org/abs/2403.05530}, <br>
}
</tt></p>
<script xml:space="preserve" language="JavaScript">
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</td></tr>
<!-- project end -->
<!-- project begin -->
<tr id="singh2024tokenization"><td class="paperthumbcol">
<!-- project symbol -->
<img src="./images/tokenization.png" class="paperthumb">
</td><td class="papertextcol">
<!-- title -->
<a href="https://arxiv.org/abs/2402.14903">
<papertitle>Tokenization counts: the impact of tokenization on arithmetic in frontier LLMs</papertitle></a><br>
<!-- authors -->
<a href="https://aadityasingh.github.io/">Aaditya Singh</a>,
<strong>DJ Strouse</strong><br>
<!-- publication status -->
<em>arxiv</em>, 2024<br>
<!-- links -->
<a href="https://arxiv.org/abs/2402.14903">arxiv</a> |
<a href="https://github.com/aadityasingh/TokenizationCounts">github</a> |
<a href="https://x.com/djstrouse/status/1762560855890112622">tweet</a> |
<a href="javascript:toggleAbsVsBib('singh2024tokenization')" id="toggle">show bibtex</a>
<p></p>
<!-- project description -->
<p id="abstract">We show that frontier LLMs (GPT-3.5 and GPT-4) are better at doing addition when numbers are tokenized right-to-left (consistent with the direction we do addition), rather than the default left-to-right. We show that the errors models make with left-to-right number tokenization are highly stereotyped, suggesting systematic rather than random issues with the addition algorithm models learn. We also find evidence that the effects of number tokenization are scale-dependent, with larger models (GPT-4) exhibiting weaker effects than presumably smaller models (GPT-4 Turbo).<br><br>
Our results were subsequently extended to newer models (e.g. Llama 3) by <a href="https://www.beren.io/2024-07-07-Right-to-Left-Integer-Tokenization/">this blog post</a>. Additionally, <a href="https://www.anthropic.com/news/claude-3-family">Claude 3</a>, released after our work and with SOTA math capabilities, also notably <a href="https://x.com/javirandor/status/1768616042224374133?s=20">uses right-to-left number tokenization.</a></p>
<!-- bibtex -->
<p id="bibtex"><tt>
@misc{singh2024tokenization, <br>
title = {Tokenization counts: the impact of tokenization on arithmetic in frontier LLMs}, <br>
author = {Aaditya K. Singh and DJ Strouse}, <br>
year = {2024}, <br>
eprint = {2402.14903}, <br>
archivePrefix={arXiv}, <br>
primaryClass={cs.CL}, <br>
url={https://arxiv.org/abs/2402.14903}, <br>
}
</tt></p>
<script xml:space="preserve" language="JavaScript">
hidebib('singh2024tokenization');
</script>
</td></tr>
<!-- project end -->
<!-- project begin -->
<tr id="moskovitz2023crlhf"><td class="paperthumbcol">
<!-- project symbol -->
<img src="./images/overoptimization.png" class="paperthumb">
</td><td class="papertextcol">
<!-- title -->
<a href="https://arxiv.org/abs/2310.04373">
<papertitle>Confronting reward model overoptimization with constrained RLHF</papertitle></a><br>
<!-- authors -->
<a href="https://tedmoskovitz.github.io/">Ted Moskovitz</a>,
<a href="https://aadityasingh.github.io/">Aaditya Singh</a>,
<strong>DJ Strouse</strong>,
<a href="https://scholar.google.com/citations?user=0DpK1EMAAAAJ&hl=en">Tuomas Sandholm</a>,
<a href="https://scholar.google.co.uk/citations?user=ITZ1e7MAAAAJ&hl=en">Ruslan Salakhutdinov</a>,
<a href="https://scholar.google.com/citations?user=UgHB5oAAAAAJ&hl=en">Anca D. Dragan</a>,
<a href="https://scholar.google.com/citations?user=iEFL4-YAAAAJ&hl=en">Stephen McAleer</a><br>
<!-- publication status -->
<em>International Conference on Learning Representations (ICLR)</em>, 2024 <award>(Spotlight)</award><br>
<!-- links -->
<a href="https://arxiv.org/abs/2310.04373">arxiv</a> |
<a href="https://openreview.net/forum?id=gkfUvn0fLU">openreview</a> |
<a href="https://x.com/ted_moskovitz/status/1714272352400502942">tweet</a> |
<a href="javascript:toggleAbsVsBib('moskovitz2023crlhf')" id="toggle">show bibtex</a>
<p></p>
<!-- project description -->
<p id="abstract"> We use tools from constrained optimization to combat overoptimization during reinforcement learning from human feedback (RLHF) against multiple reward models.</p>
<!-- bibtex -->
<p id="bibtex"><tt>
@inproceedings{moskovitz2023crlhf, <br>
title = {Confronting Reward Model Overoptimization with Constrained RLHF}, <br>
author = {Ted Moskovitz and Aaditya K. Singh and DJ Strouse and Tuomas Sandholm and Ruslan Salakhutdinov and Anca D. Dragan and Stephen McAleer}, <br>
booktitle = {International Conference on Learning Representations (ICLR)}, <br>
year = {2023}, <br>
}
</tt></p>
<script xml:space="preserve" language="JavaScript">
hidebib('moskovitz2023crlhf');
</script>
</td></tr>
<!-- project end -->
<!-- project begin -->
<tr id="laskin2022ad"><td class="paperthumbcol">
<!-- project symbol -->
<img src="./images/algo_distillation.png" class="paperthumb">
</td><td class="papertextcol">
<!-- title -->
<a href="https://openreview.net/forum?id=hy0a5MMPUv">
<papertitle>In-context Reinforcement Learning with Algorithm Distillation</papertitle></a><br>
<!-- authors -->
<a href="https://www.mishalaskin.com/">Michael Laskin</a>,
<a href="https://scholar.google.ca/citations?user=GlOpjUoAAAAJ&hl=en">Luyu Wang</a>,
<a href="https://junhyuk.com/">Junhyuk Oh</a>,
<a href="https://scholar.google.com/citations?user=-GduGkcAAAAJ&hl=en">Emilio Parisotto</a>,
<a href="https://github.com/stompchicken">Stephen Spencer</a>,
<a href="https://scholar.google.com/citations?user=7EWSbv0AAAAJ&hl=en">Richie Steigerwald</a>,
<strong>DJ Strouse</strong>,
<a href="https://scholar.google.co.uk/citations?user=hIOEWsEAAAAJ&hl=en">Steven Hansen</a>
<a href="https://scholar.google.com/citations?user=SGjYdrEAAAAJ&hl=en">Angelos Filos</a>,
<a href="https://www.linkedin.com/in/ethan-brooks/">Ethan Brooks</a>,
<a href="https://scholar.google.com/citations?user=LfmqBJsAAAAJ&hl=en">Maxime Gazeau</a>,
<a href="https://himanshusahni.github.io/">Himanshu Sahni</a>,
<a href="https://web.eecs.umich.edu/~baveja/">Satinder Singh</a>,
<a href="https://scholar.google.co.uk/citations?user=rLdfJ1gAAAAJ&hl=en">Vlad Mnih</a><br>
<!-- publication status -->
<em>International Conference on Learning Representations (ICLR)</em>, 2023 <award>(Oral)</award><br>
<!-- links -->
<a href="https://arxiv.org/abs/2210.14215">arxiv</a> |
<a href="https://openreview.net/forum?id=hy0a5MMPUv">openreview</a> |
<a href="https://x.com/djstrouse/status/1585271559413026816">tweet</a> |
<a href="javascript:toggleAbsVsBib('laskin2022ad')" id="toggle">show bibtex</a>
<p></p>
<!-- project description -->
<p id="abstract">We demonstrate that it is possible to distill entire reinforcement learning (RL) algorithms into the in-context learning abilities of a Transformer, by training models to do supervised prediction of multi-episodic trajectories from RL agents.</p>
<!-- bibtex -->
<p id="bibtex"><tt>
@inproceedings{laskin2023ad, <br>
title = {In-context Reinforcement Learning with Algorithm Distillation}, <br>
author = {Michael Laskin and Luyu Wang and Junhyuk Oh and Emilio Parisotto and Stephen Spencer and Richie Steigerwald and DJ Strouse and Steven Stenberg Hansen and Angelos Filos and Ethan Brooks and Maxime Gazeau and Himanshu Sahni and Satinder Singh and Volodymyr Mnih}, <br>
booktitle = {International Conference on Learning Representations (ICLR)}, <br>
year = {2023}, <br>
}
</tt></p>
<script xml:space="preserve" language="JavaScript">
hidebib('laskin2022ad');
</script>
</td></tr>
<!-- project end -->
<!-- project begin -->
<tr id="tam2022exploration"><td class="paperthumbcol">
<!-- project symbol -->
<img src="./images/semantic_exploration.png" class="paperthumb">
</td><td class="papertextcol">
<!-- title -->
<a href="https://arxiv.org/abs/2204.05080">
<papertitle>Semantic Exploration from Language Abstractions and Pretrained Representations</papertitle></a><br>
<!-- authors -->
<a href="https://x.com/allisontam_">Allison Tam</a>, <a href="https://scholar.google.com/citations?user=AgUYQMwAAAAJ&hl=en">Neil Rabinowitz</a>, <a href="https://lampinen.github.io/">Andrew Lampinen</a>, <a href="https://scholar.google.com/citations?user=rytiQbMAAAAJ&hl=en">Nicholas Roy</a>, <a href="https://scholar.google.com/citations?user=bXOt49QAAAAJ&hl=en">Stephanie Chan</a>, <strong>DJ Strouse</strong>, <a href="https://www.janexwang.com/">Jane Wang</a>, <a href="https://scholar.google.com/citations?user=QD-sf3IAAAAJ&hl=en">Andrea Banino</a>, <a href="https://scholar.google.com/citations?user=4HLUnhIAAAAJ&hl=en">Felix Hill</a><br>
<!-- publication status -->
<em>Neural Information Processing Systems (NeurIPS)</em>, 2022<br>
<!-- links -->
<a href="https://arxiv.org/abs/2204.05080">arxiv</a> |
<a href="https://openreview.net/forum?id=-NOQJw5z_KY">openreview</a> |
<a href="https://proceedings.neurips.cc/paper_files/paper/2022/hash/a28e024ccd623ed113fb19683fa0910d-Abstract-Conference.html">neurips</a> |
<a href="https://x.com/djstrouse/status/1514696338507710471">tweet</a> |
<a href="javascript:toggleAbsVsBib('tam2022exploration')" id="toggle">show bibtex</a>
<p></p>
<!-- project description -->
<p id="abstract">Exploration in RL traditionally encouraged agents to visit random unexplored states. Taking advantage of improvements in multimodal frontier models, we show how to improve exploration by guiding agents towards semantically novel states, greatly speeding up learning.</p>
<!-- bibtex -->
<p id="bibtex"><tt>
@inproceedings{tam2022exploration, <br>
title = {Semantic Exploration from Language Abstractions and Pretrained Representations}, <br>
author = {Tam, Allison and Rabinowitz, Neil and Lampinen, Andrew and Roy, Nicholas A. and Chan, Stephanie and Strouse, DJ and Wang, Jane and Banino, Andrea and Hill, Felix}, <br>
booktitle = {Neural Information Processing Systems (NeurIPS)}, <br>
year = {2022}, <br>
}
</tt></p>
<script xml:space="preserve" language="JavaScript">
hidebib('tam2022exploration');
</script>
</td></tr>
<!-- project end -->
<!-- project begin -->
<tr id="strouse2021disdain"><td class="paperthumbcol">
<!-- project symbol -->
<img src="./images/DISDAIN.png" class="paperthumb">
</td><td class="papertextcol">
<!-- title -->
<a href="https://openreview.net/forum?id=cU8rknuhxc">
<papertitle>Learning more skills through optimistic exploration</papertitle></a><br>
<!-- authors -->
<strong>DJ Strouse</strong>*,
<a href="https://scholar.google.com/citations?user=feM7-mEAAAAJ&hl=en">Kate Baumli</a>,
<a href="https://scholar.google.co.uk/citations?user=MOgfm8oAAAAJ&hl=en">David Warde-Farley</a>,
<a href="https://scholar.google.co.uk/citations?user=rLdfJ1gAAAAJ&hl=en">Vlad Mnih</a>,
<a href="https://scholar.google.co.uk/citations?user=hIOEWsEAAAAJ&hl=en">Steven Hansen*</a><br>
<!-- publication status -->
<em>International Conference on Learning Representations (ICLR)</em>, 2022 <award>(Spotlight)</award><br>
<!-- links -->
<a href="https://arxiv.org/abs/2107.14226">arxiv</a> |
<a href="https://openreview.net/forum?id=cU8rknuhxc">openreview</a> |
<a href="https://github.com/deepmind/disdain">github</a> |
<a href="https://twitter.com/djstrouse/status/1504534305300750344?s=20&t=UbNiH9G0nLs2_DRnFPsCsg">tweet</a> |
<a href="javascript:toggleAbsVsBib('strouse2021disdain')" id="toggle">show bibtex</a>
<p></p>
<!-- project description -->
<p id="abstract">We highlight the inherent pessmism towards exploration in a popular family of variational unsupervised skill learning methods. To curb this pessimism, we propose an ensemble uncertainty based exploration bonus that we call discriminator disagreement intrinsic reward, or DISDAIN. We show that DISDAIN improves skill learning in both a gridworld and the Atari57 suite. Thus, we encourage researchers to treat pessimism with DISDAIN.</p>
<!-- bibtex -->
<p id="bibtex"><tt>
@inproceedings{strouse2022disdain, <br>
title = {Learning more skills through optimistic exploration}, <br>
author = {Strouse, DJ and Baumli, Kate and Warde-Farley, David and Mnih, Vlad and Hansen, Steven}, <br>
booktitle = {International Conference on Learning Representations (ICLR)}, <br>
year = {2022}, <br>
}
</tt></p>
<script xml:space="preserve" language="JavaScript">
hidebib('strouse2021disdain');
</script>
</td></tr>
<!-- project end -->
<!-- project begin -->
<!-- <tr id="tacchetti2019auctioncnn"><td class="paperthumbcol"> -->
<!-- project symbol -->
<!-- <img src="./images/auctionCNN.png" class="paperthumb">
</td><td class="papertextcol"> -->
<!-- title -->
<!-- <a href="https://arxiv.org/abs/1907.05181">
<papertitle>Learning Truthful, Efficient, and Welfare Maximizing Auction Rules</papertitle></a><br> -->
<!-- authors -->
<!-- <a href="https://www.andreatacchetti.com/">Andrea Tacchetti</a>,
<strong>DJ Strouse</strong>,
<a href="https://www.doc.ic.ac.uk/~mg4413/">Marta Garnelo</a>,
<a href="https://scholar.google.co.uk/citations?hl=en&user=PNH24toAAAAJ">Thore Graepel</a>,
<a href="https://scholar.google.com/citations?user=0W63ivcAAAAJ&hl=en">Yoram Bachrach</a><br> -->
<!-- publication status -->
<!-- <em>ICLR Gamification and Multiagent Solutions Workshop</em>, 2022<br> -->
<!-- links -->
<!-- <a href="https://arxiv.org/abs/1907.05181">arxiv</a> |
<a href="https://openreview.net/forum?id=Sql8oqJTe9">openreview</a> |
<a href="javascript:toggleAbsVsBib('tacchetti2019auctioncnn')" id="toggle">show bibtex</a>
<p></p> -->
<!-- project description -->
<!-- <p id="abstract">We present a deep learning approach to auction design that guarantees truthfulness (bidders are incentivized to be honest) and efficiency (whoever wants the item most gets it). We focus on social utility maximizing auctions, where the goal is to achieve the former constraints while placing as little economic burden on the bidders as possible.</p> -->
<!-- bibtex -->
<!-- <p id="bibtex"><tt>
@inproceedings{tacchetti2022auctioncnn, <br>
title = {Learning Truthful, Efficient, and Welfare Maximizing Auction Rules}, <br>
author = {Tacchetti, Andrea and Strouse, DJ and Garnelo, Marta and Graepel, Thore and Bachrach, Yoram}, <br>
booktitle = {ICLR Gamification and Multiagent Solutions Workshop}, <br>
year = {2022}, <br>
} -->
<!-- </tt></p>
<script xml:space="preserve" language="JavaScript">
hidebib('tacchetti2019auctioncnn');
</script>
</td></tr> -->
<!-- project end -->
<!-- project begin -->
<tr id="strouse2021fcp"><td class="paperthumbcol">
<!-- project symbol -->
<img src="./images/fcp.jpg" class="paperthumb">
</td><td class="papertextcol">
<!-- title -->
<a href="https://arxiv.org/abs/2110.08176">
<papertitle>Collaborating with Humans without Human Data</papertitle></a><br>
<!-- authors -->
<strong>DJ Strouse</strong>*,
<a href="https://www.empiricallykev.com/">Kevin R. McKee</a>,
<a href="https://scholar.google.com/citations?user=eM916YMAAAAJ">Matt Botvinick</a>,
<a href="https://edwardhughes.io/">Edward Hughes</a>,
<a href="https://scholar.google.co.uk/citations?user=IIlnyWQAAAAJ&hl=en">Richard Everett*</a><br>
<!-- publication status -->
<em>Neural Information Processing Systems (NeurIPS)</em>, 2021 <award>(Spotlight)</award><br>
<!-- links -->
<a href="https://arxiv.org/abs/2110.08176">arxiv</a> |
<a href="https://papers.nips.cc/paper/2021/hash/797134c3e42371bb4979a462eb2f042a-Abstract.html">neurips</a> |
<a href="https://openreview.net/forum?id=1Kof-nkmQB8">openreview</a> |
<a href="https://twitter.com/djstrouse/status/1468256320126132226?s=20&t=UbNiH9G0nLs2_DRnFPsCsg">tweet</a> |
<a href="https://mailchi.mp/a08565612790/an-169collaborating-with-humans-without-human-data">alignment newsletter</a> |
<a href="javascript:toggleAbsVsBib('strouse2021fcp')" id="toggle">show bibtex</a>
<p></p>
<!-- project description -->
<p id="abstract">We introduce Fictitious Co-Play (FCP), a simple and intuitive training method for producing agents capable of zero-shot coordination with humans in Overcooked. FCP works by training an agent as the best response to a frozen pool of self-play agents and their past checkpoints. Notably, FCP exhibits robust generalization to humans, despite not using any human data during training.</p>
<!-- bibtex -->
<p id="bibtex"><tt>
@inproceedings{strouse2021fcp, <br>
title = {Collaborating with Humans without Human Data}, <br>
author = {Strouse, DJ and McKee, Kevin R. and Botvinick, Matt and Hughes, Edward and Everett, Richard}, <br>
booktitle = {Neural Information Processing Systems (NeurIPS)}, <br>
year = {2021}, <br>
}
</tt></p>
<script xml:space="preserve" language="JavaScript">
hidebib('strouse2021fcp');
</script>
</td></tr>
<!-- project end -->
<!-- project begin -->
<tr id="jaques2019influence"><td class="paperthumbcol">
<!-- project symbol -->
<img src="./images/influence.png" class="paperthumb">
</td><td class="papertextcol">
<!-- title -->
<a href="https://arxiv.org/abs/1810.08647">
<papertitle>Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning</papertitle></a><br>
<!-- authors -->
<a href="https://www.media.mit.edu/people/jaquesn/overview/">Natasha Jaques</a>,
<a href="http://angelikilazaridou.github.io/">Angeliki Lazaridou</a>,
<a href="https://scholar.google.com/citations?user=3tj5358AAAAJ&hl=en">Edward Hughes</a>,
<a href="https://caglar.github.io/">Caglar Gulcehre</a>,
<a href="http://www.adaptiveagents.org/">Pedro A. Ortega</a>,
<strong>DJ Strouse</strong>,
<a href="http://www.jzleibo.com/">Joel Z. Leibo</a>,
<a href="http://www.cs.ox.ac.uk/people/nando.defreitas/">Nando de Freitas</a><br>
<!-- publication status -->
<em>International Conference on Machine Learning (ICML)</em>, 2019 <award>(Best Paper Honorable Mention)</award><br>
<!-- links -->
<a href="https://arxiv.org/abs/1810.08647">arxiv</a> |
<a href="https://proceedings.mlr.press/v97/jaques19a.html">icml</a> |
<a href="https://openreview.net/forum?id=B1lG42C9Km">openreview</a> |
<a href="javascript:toggleAbsVsBib('jaques2019influence')" id="toggle">show bibtex</a>
<p></p>
<!-- project description -->
<p id="abstract">We reward agents for influencing the actions of other agents, and show that this gives rise to better cooperation and more meaningful emergent communication protocols.</p>
<!-- bibtex -->
<p id="bibtex"><tt>
@inproceedings{jaques2019influence, <br>
title = {Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning}, <br>
author = {Jaques, Natasha and Lazaridou, Angeliki and Hughes, Edward and Gulcehre, Caglar and Ortega, Pedro and Strouse, DJ and Leibo, Joel Z. and De Freitas, Nando}, <br>
booktitle = {International Conference on Machine Learning (ICML)}, <br>
year = {2019}, <br>
}
</tt></p>
<script xml:space="preserve" language="JavaScript">
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</script>
</td></tr>
<!-- project end -->
<!-- project begin -->
<tr id="goyal2019infobot"><td class="paperthumbcol">
<!-- project symbol -->
<img src="./images/infobot.jpg" class="paperthumb">
</td><td class="papertextcol">
<!-- title -->
<a href="https://openreview.net/forum?id=rJg8yhAqKm">
<papertitle>InfoBot: Transfer and Exploration via the Information Bottleneck</papertitle></a><br>
<!-- authors -->
<a href="https://anirudh9119.github.io/">Anirudh Goyal</a>,
<a href="https://riashatislam.com/">Riashat Islam</a>,
<strong>DJ Strouse</strong>,
<a href="http://www.zafarali.me/">Zafarali Ahmed</a>,
<a href="https://ai.google/research/people/105144">Hugo Larochelle</a>,
<a href="https://scholar.google.com/citations?user=eM916YMAAAAJ">Matt Botvinick</a>,
<a href="https://people.eecs.berkeley.edu/~svlevine/">Sergey Levine</a>,
<a href="http://www.iro.umontreal.ca/~bengioy/yoshua_en/">Yoshua Bengio</a><br>
<!-- publication status -->
<em>International Conference on Learning Representations (ICLR)</em>, 2019<br>
<!-- links -->
<a href="https://arxiv.org/abs/1901.10902">arxiv</a> |
<a href="https://openreview.net/forum?id=rJg8yhAqKm">openreview</a> |
<a href="javascript:toggleAbsVsBib('goyal2019infobot')" id="toggle">show bibtex</a>
<p></p>
<!-- project description -->
<p id="abstract">We train agents in multi-goal environments with an information bottleneck between their goal and policy. This encourages agents to develop useful "habits" that generalize across goals. We identify the states where agents must deviate from their habits to solve a task as "decision states" and show that they are useful targets for an exploration bonus.</p>
<!-- bibtex -->
<p id="bibtex"><tt>
@inproceedings{goyal2019infobot, <br>
title={Transfer and Exploration via the Information Bottleneck}, <br>
author={Anirudh Goyal and Riashat Islam and DJ Strouse and Zafarali Ahmed and Matthew Botvinick and Hugo Larochelle and Yoshua Bengio and Sergey Levine}, <br>
booktitle={International Conference on Learning Representations (ICLR)}, <br>
year = {2019}, <br>
}
</tt></p>
<script xml:space="preserve" language="JavaScript">
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</script>
</td></tr>
<!-- project end -->
<!-- project begin -->
<tr id="strouse2018intentions"><td class="paperthumbcol">
<!-- project symbol -->
<img src="./images/infobot2.jpg" class="paperthumb">
</td><td class="papertextcol">
<!-- title -->
<a href="https://arxiv.org/abs/1808.02093">
<papertitle>Learning to share and hide intentions using information regularization</papertitle></a><br>
<!-- authors -->
<strong>DJ Strouse</strong>,
<a href="http://www.mit.edu/~maxkw/">Max Kleiman-Weiner</a>,
<a href="http://web.mit.edu/cocosci/josh.html">Josh Tenenbaum</a>,
<a href="https://scholar.google.com/citations?user=eM916YMAAAAJ">Matt Botvinick</a>,
<a href="https://www.gc.cuny.edu/Faculty/Core-Bios/David-Schwab">David Schwab</a><br>
<!-- publication status -->
<em>Neural Information Processing Systems (NIPS)</em>, 2018<br>
<!-- links -->
<a href="https://arxiv.org/abs/1808.02093">arxiv</a> |
<a href="https://papers.nips.cc/paper/8227-learning-to-share-and-hide-intentions-using-information-regularization">nips</a> |
<a href="https://github.com/djstrouse/InfoMARL">code</a> |
<a href="javascript:toggleAbsVsBib('strouse2018intentions')" id="toggle">show bibtex</a>
<p></p>
<!-- project description -->
<p id="abstract">We train agents to cooperate / compete by regularizing the reward-relevant information they share with other agents, enabling agents trained alone to nevertheless perform well in a multi-agent setting.</p>
<!-- bibtex -->
<p id="bibtex"><tt>
@inproceedings{strouse2018intentions, <br>
title={Learning to share and hide intentions using information regularization}, <br>
author = {Strouse, DJ and Kleiman-Weiner, Max and Tenenbaum, Josh and Botvinick, Matt and Schwab, David J}, <br>
booktitle = {Neural Information Processing Systems (NeurIPS)}, <br>
year = {2018}, <br>
}
</tt></p>
<script xml:space="preserve" language="JavaScript">
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</script>
</td></tr>
<!-- project end -->
<!-- paper begin -->
<tr id="strouse2019clustering"><td class="paperthumbcol">
<!-- project symbol -->
<img src="./images/geometric_IB.jpg" class="paperthumb">
</td><td class="papertextcol">
<!-- title -->
<a href="https://arxiv.org/abs/1712.09657">
<papertitle>The information bottleneck and geometric clustering</papertitle></a><br>
<!-- authors -->
<strong>DJ Strouse</strong>,
<a href="">David Schwab</a><br>
<!-- publication status -->
<em>Neural Computation (NECO)</em>, 2019<br>
<!-- links -->
<a href="downloads/strouse2019clustering.pdf">pdf</a> |
<a href="https://www.mitpressjournals.org/doi/abs/10.1162/neco_a_01136">neco</a> |
<a href="https://arxiv.org/abs/1712.09657">arxiv</a> |
<a href="https://github.com/djstrouse/information-bottleneck">code</a> |
<a href="javascript:toggleAbsVsBib('strouse2019clustering')" id="toggle">show bibtex</a>
<p></p>
<!-- project description -->
<p id="abstract">We show how to use the (deterministic) information bottleneck to perform geometric clustering, introducing a novel information-theoretic model selection criterion. We show how this relates to and generalizes k-means and gaussian mixture models (GMMs).</p>
<!-- bibtex -->
<p id="bibtex"><tt>
@article{strouse2019clustering, <br>
title = {Geometric Clustering with the Information Bottleneck}, <br>
author = {Strouse, DJ and Schwab, David J.}, <br>
journal = {Neural Computation}, <br>
year = {2019}, <br>
volume = {31}, <br>
number = {3}, <br>
pages = {596-612}, <br>
}
</tt></p>
<script xml:space="preserve" language="JavaScript">
hidebib('strouse2019clustering');
</script>
</td></tr>
<!-- project end -->
<!-- paper begin -->
<tr id="strouse2017dib"><td class="paperthumbcol">
<!-- project symbol -->
<img src="./images/DIB.jpg" class="paperthumb">
</td><td class="papertextcol">
<!-- title -->
<a href="https://arxiv.org/abs/1604.00268">
<papertitle>The deterministic information bottleneck</papertitle></a><br>
<!-- authors -->
<strong>DJ Strouse</strong>,
<a href="">David Schwab</a> <br>
<!-- publication status -->
<em>Neural Computation (NECO)</em>, 2017 & <em>Uncertainty in Artificial Intelligence (UAI)</em>, 2016<br>
<!-- links -->
<a href="downloads/strouse2017dib.pdf">pdf</a> |
<a href="https://arxiv.org/abs/1604.00268">arxiv</a> |
<a href="https://github.com/djstrouse/information-bottleneck">code</a> |
<a href="http://auai.org/uai2016/proceedings/papers/319.pdf">uai</a> |
<a href="https://www.mitpressjournals.org/doi/abs/10.1162/NECO_a_00961">neco</a> |
<a href="javascript:toggleAbsVsBib('strouse2017dib')" id="toggle">show bibtex</a>
<p></p>
<!-- project description -->
<p id="abstract">We introduce the deterministic information bottleneck (DIB), an alternative formulation of the information bottleneck that uses entropy instead of mutual information to measure compression. This results in a hard clustering algorithm with a built-in preference for using fewer clusters.</p>
<!-- bibtex -->
<p id="bibtex"><tt>
@article{strouse2017dib, <br>
title = {The Deterministic Information Bottleneck}, <br>
author = {Strouse, DJ and Schwab, David J.}, <br>
journal = {Neural Computation}, <br>
year = {2017}, <br>
volume = {29}, <br>
number = {6}, <br>
pages = {1611-1630}, <br>
}
</tt></p>
<script xml:space="preserve" language="JavaScript">
hidebib('strouse2017dib');
</script>
</td></tr>
<!-- project end -->
<!-- <tr><td width="25%">
<heading2><i>Neuroscience</i></heading2>
</td/></tr> -->
<!-- paper begin -->
<!-- <tr><td class="paperthumbcol"> -->
<!-- project symbol -->
<!-- <img src="./images/dendrite.jpg" class="paperthumb">
</td><td class="papertextcol"> -->
<!-- title -->
<!-- <a href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006892">
<papertitle>How Dendrites Affect Online Recognition Memory</papertitle></a><br> -->
<!-- authors -->
<!-- <a href="https://neurotree.org/beta/peopleinfo.php?pid=155582">Xundong Wu</a>,
<a href="https://twitter.com/meldefon">Gabriel Mel</a>,
<strong>DJ Strouse</strong>,
<a href="http://profiles.sc-ctsi.org/bartlett.mel">Bartlett Mel</a><br> -->
<!-- publication status -->
<!-- <em>PLoS Computational Biology</em>, 2019<br> -->
<!-- links -->
<!-- <a href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006892">plos</a>
<p></p> -->
<!-- project description -->
<!-- <p>We study the optimal conditions for online recognition memory in a biologically-inspired neural network with "dendrite-aware" learning rules.</p>
</td></tr> -->
<!-- paper end -->
<!-- paper begin -->
<!-- <tr><td class="paperthumbcol"> -->
<!-- project symbol -->
<!-- <img src="./images/synaptic_stimulation.jpg" class="paperthumb">
</td><td class="papertextcol"> -->
<!-- title -->
<!-- <a href="downloads/MPhil_thesis_final.pdf">
<papertitle>Using neural networks to understand the computational role of dendrites</papertitle></a><br> -->
<!-- authors -->
<!-- <strong>DJ Strouse</strong>,
<a href="https://scholar.google.com/citations?user=Qup7UAYAAAAJ&hl=en">Balazs Ujfalussy</a>,
<a href="http://www3.eng.cam.ac.uk/~ml468/">Mate Lengyel</a><br> -->
<!-- publication status -->
<!-- <em>Computational and Systems Neuroscience (Cosyne)</em>, 2012 & 2013<br> -->
<!-- links -->
<!-- <a href="downloads/Cosyne2012_poster.pdf">2012 poster</a> & <a href="downloads/Cosyne2012_abstract.pdf">abstract</a> |
<a href="downloads/Cosyne2013_poster.pdf">2013 poster</a> & <a href="downloads/Cosyne2013_abstract.pdf">abstract</a> |
<a href="downloads/MPhil_thesis_final.pdf">master's thesis</a> |
<a href="downloads/why.txt">why</a>
<p></p> -->
<!-- project description -->
<!-- <p>We fit neural network models to single neuron data to understand the computational role of dendrites in integrating their synaptic input.</p>
</td></tr> -->
<!-- paper end -->
<!-- project begin -->
<!-- <tr><td class="paperthumbcol"> -->
<!-- project symbol -->
<!-- <img src="./images/sniff.png" class="paperthumb">
</td><td class="papertextcol"> -->
<!-- title -->
<!-- <a href="downloads/SCNE_poster.pdf">
<papertitle>Behaviorally-locked structure in a sensory neural code</papertitle></a><br> -->
<!-- authors -->
<!-- <strong>DJ Strouse</strong>,
<a href="https://www.mackelab.org/">Jakob Macke</a>,
<a href="http://www.romashusterman.com/">Roman Shusterman</a>,
<a href="https://www.rinberglab.com/">Dima Rinberg</a>,
<a href="http://www.weizmann.ac.il/neurobiology/labs/schneidman/The_Schneidman_Lab/Home.html">Elad Schneidman</a><br> -->
<!-- publication status -->
<!-- <em>Sensory Coding & the Natural Environments (SCNE)</em>, 2012<br> -->
<!-- links -->
<!-- <a href="downloads/SCNE2012_abstract.pdf">abstract</a> |
<a href="downloads/SCNE2012_poster.pdf">poster</a>
<p></p> -->
<!-- project description -->
<!-- <p>We study the olfactory neural code in mice and find that much of the information about the stimulus is only decodable when interpreting neural activity relative to the sniff phase, providing evidence for the importance of considering sensory sampling behavior when interpreting neural codes.</p>
</td></tr> -->
<!-- project end -->
<!-- <tr><td width="25%">
<heading2><i>Physics</i></heading2>
</td/></tr> -->
<!-- paper begin -->
<!-- <tr><td class="paperthumbcol"> -->
<!-- project symbol -->
<!-- <img src="./images/levinson.jpg" class="paperthumb">--> <!-- should be 160x160 -->
<!-- </td><td class="papertextcol"> -->
<!-- title -->
<!-- <a href="https://arxiv.org/abs/1103.5077">
<papertitle>Levinson's theorem for graphs</papertitle></a><br> -->
<!-- authors -->
<!-- <a href="https://www.cs.umd.edu/~amchilds/">Andrew Childs</a>,
<strong>DJ Strouse</strong><br> -->
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<!-- <em>Journal of Mathematical Physics (JMP)</em>, 2011<br> -->
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<a href="http://aip.scitation.org/doi/abs/10.1063/1.3622608">jmp</a> |
<a href="downloads/IQC_colloquium_slides.pptx">talk</a>
<p></p> -->
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<!-- <p>We prove an analog of a classic result in quantum scattering theory for the setting of scattering on graphs. The goal is to provide additional tools for designing quantum algorithms in this setting.</p>
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