DJ Strouse

I am a Research Scientist at DeepMind in London.

I did a PhD in Physics at Princeton University, advised by David Schwab and Bill Bialek, and funded by a Hertz Fellowship and a DOE Computational Science Graduate Fellowship. I did a master's at the University of Cambridge with Mate Lengyel as a 苹果手机怎么挂vnp教程 and studied physics and mathematics at the University of Southern California, where I worked with Bartlett Mel and Paolo Zanardi and had a blog. Throughout my studies, I also spent time at DeepMind working with Matt Botvinick, Stanford University working with 国内ipad怎么看youtube, the Institute for Quantum Computing working with Andrew Childs, and Spotify NYC working with their machine learning team.

Email  /  Twitter  /  CV  /  Scholar  /  翻外墙看youtube加速软件


I'm interested in reinforcement learning (RL), information theory, and deep learning with an eye toward understanding and creating intelligent agents. My PhD thesis ("Optimization of MILES") focussed on applications of the information bottleneck (IB) in supervised, unsupervised, and reinforcement learning. In the past, I've also worked on quantum information theory and computational neuroscience.

A Neural Architecture for Designing Truthful and Efficient Auctions
Andrea Tacchetti, DJ Strouse, Marta Garnelo, Thore Graepel, Yoram Bachrach
arxiv, 2019

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.

Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning
Natasha Jaques, Angeliki Lazaridou, Edward Hughes, Caglar Gulcehre, Pedro A. Ortega, DJ Strouse, 国内ipad怎么看youtube, Nando de Freitas
International Conference on Machine Learning (ICML), 2019 (best paper honorable mention)
arxiv / openreview

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.

The information bottleneck and geometric clustering
DJ Strouse, David Schwab
Neural Computation (NECO), 2019
pdf / neco / arxiv / code / bibtex

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InfoBot: Transfer and Exploration via the Information Bottleneck
Anirudh Goyal, Riashat Islam, DJ Strouse, 苹果手机怎么挂vnp教程, Hugo Larochelle, Matthew Botvinick, Sergey Levine, Yoshua Bengio
International Conference on Learning Representations (ICLR), 2019
openreview / 翻了墙可以看哪些网站

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.

Learning to share and hide intentions using information regularization
DJ Strouse, Max Kleiman-Weiner, Josh Tenenbaum, Matt Botvinick, 翻外墙看youtube加速软件
Neural Information Processing Systems (NIPS), 2018
arxiv / 国内ipad怎么看youtube / iPhone怎样能看YouTube / bibtex

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.

The deterministic information bottleneck
DJ Strouse, David Schwab
Neural Computation (NECO), 2017 & Uncertainty in Artificial Intelligence (UAI), 2016
pdf / arxiv / code / uai / neco / bibtex

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.

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unpublished note

We describe how to use the variational deterministic information bottleneck to regularize discrete latent variable models, encouraging the use of as few latent variables as possible for a given level of performance.

How Dendrites Affect Online Recognition Memory
Xundong Wu, Gabriel Mel, DJ Strouse, Bartlett Mel
PLoS Computational Biology, 2019

We study the optimal conditions for online recognition memory in a biologically-inspired neural network with "dendrite-aware" learning rules.

Using neural networks to understand the computational role of dendrites
DJ Strouse, Balazs Ujfalussy, Mate Lengyel
Computational and Systems Neuroscience (Cosyne), 2012 & 2013
2012 poster & abstract / 2013 poster & abstract / 翻外墙看youtube加速软件 / why

We fit neural network models to single neuron data to understand the computational role of dendrites in integrating their synaptic input.

Behaviorally-locked structure in a sensory neural code
DJ Strouse, Jakob Macke, 翻了墙可以看哪些网站, Dima Rinberg, Elad Schneidman
Sensory Coding & the Natural Environments (SCNE), 2012
abstract / poster

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Levinson's theorem for graphs
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Journal of Mathematical Physics (JMP), 2011
arxiv / jmp / talk

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.

Good artists copy.

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