Stuart Russell

research

∙ 09/01/2023

Image Hijacks: Adversarial Images can Control Generative Models at Runtime

Are foundation models secure from malicious actors? In this work, we foc...

0 Luke Bailey, et al. ∙

research

∙ 06/15/2023

Who Needs to Know? Minimal Knowledge for Optimal Coordination

To optimally coordinate with others in cooperative games, it is often cr...

0 Niklas Lauffer, et al. ∙

research

∙ 06/12/2023

TASRA: a Taxonomy and Analysis of Societal-Scale Risks from AI

While several recent works have identified societal-scale and extinction...

0 Andrew Critch, et al. ∙

research

∙ 04/19/2023

Bridging RL Theory and Practice with the Effective Horizon

Deep reinforcement learning (RL) works impressively in some environments...

0 Cassidy Laidlaw, et al. ∙

research

∙ 03/02/2023

Active Reward Learning from Multiple Teachers

Reward learning algorithms utilize human feedback to infer a reward func...

0 Peter Barnett, et al. ∙

research

∙ 11/22/2022

imitation: Clean Imitation Learning Implementations

imitation provides open-source implementations of imitation and reward l...

0 Adam Gleave, et al. ∙

research

∙ 11/01/2022

Optimal Conservative Offline RL with General Function Approximation via Augmented Lagrangian

Offline reinforcement learning (RL), which refers to decision-making fro...

0 Paria Rashidinejad, et al. ∙

research

∙ 11/01/2022

Adversarial Policies Beat Professional-Level Go AIs

We attack the state-of-the-art Go-playing AI system, KataGo, by training...

0 Tony Tong Wang, et al. ∙

research

∙ 08/15/2022

Cooperative and uncooperative institution designs: Surprises and problems in open-source game theory

It is increasingly possible for real-world agents, such as software-base...

0 Andrew Critch, et al. ∙

research

∙ 07/07/2022

For Learning in Symmetric Teams, Local Optima are Global Nash Equilibria

Although it has been known since the 1970s that a globally optimal strat...

7 Scott Emmons, et al. ∙

research

∙ 05/16/2022

An Empirical Investigation of Representation Learning for Imitation

Imitation learning often needs a large demonstration set in order to han...

8 Xin Chen, et al. ∙

research

∙ 04/25/2022

Estimating and Penalizing Induced Preference Shifts in Recommender Systems

The content that a recommender system (RS) shows to users influences the...

4 Micah Carroll, et al. ∙

research

∙ 03/14/2022

Invariance in Policy Optimisation and Partial Identifiability in Reward Learning

It's challenging to design reward functions for complex, real-world task...

0 Joar Skalse, et al. ∙

research

∙ 10/13/2021

Detecting Modularity in Deep Neural Networks

A neural network is modular to the extent that parts of its computationa...

10 Shlomi Hod, et al. ∙

research

∙ 10/07/2021

Cross-Domain Imitation Learning via Optimal Transport

Cross-domain imitation learning studies how to leverage expert demonstra...

1 Arnaud Fickinger, et al. ∙

research

∙ 09/30/2021

Scalable Online Planning via Reinforcement Learning Fine-Tuning

Lookahead search has been a critical component of recent AI successes, s...

4 Arnaud Fickinger, et al. ∙

research

∙ 07/12/2021

Explore and Control with Adversarial Surprise

Reinforcement learning (RL) provides a framework for learning goal-direc...

4 Arnaud Fickinger, et al. ∙

research

∙ 07/05/2021

The MineRL BASALT Competition on Learning from Human Feedback

The last decade has seen a significant increase of interest in deep lear...

5 Rohin Shah, et al. ∙

research

∙ 06/19/2021

Learning the Preferences of Uncertain Humans with Inverse Decision Theory

Existing observational approaches for learning human preferences, such a...

11 Cassidy Laidlaw, et al. ∙

research

∙ 06/18/2021

MADE: Exploration via Maximizing Deviation from Explored Regions

In online reinforcement learning (RL), efficient exploration remains par...

4 Tianjun Zhang, et al. ∙

research

∙ 03/22/2021

Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism

Offline (or batch) reinforcement learning (RL) algorithms seek to learn ...

41 Paria Rashidinejad, et al. ∙

research

∙ 03/04/2021

Clusterability in Neural Networks

The learned weights of a neural network have often been considered devoi...

6 Daniel Filan, et al. ∙

research

∙ 01/25/2021

Accumulating Risk Capital Through Investing in Cooperation

Recent work on promoting cooperation in multi-agent learning has resulte...

1 Charlotte Roman, et al. ∙

research

∙ 12/29/2020

Multi-Principal Assistance Games: Definition and Collegial Mechanisms

We introduce the concept of a multi-principal assistance game (MPAG), an...

3 Arnaud Fickinger, et al. ∙

research

∙ 12/10/2020

Understanding Learned Reward Functions

In many real-world tasks, it is not possible to procedurally specify an ...

16 Eric J. Michaud, et al. ∙

research

∙ 12/03/2020

Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design

A wide range of reinforcement learning (RL) problems - including robustn...

0 Michael Dennis, et al. ∙

research

∙ 12/02/2020

DERAIL: Diagnostic Environments for Reward And Imitation Learning

The objective of many real-world tasks is complex and difficult to proce...

1 Pedro Freire, et al. ∙

research

∙ 11/01/2020

The MAGICAL Benchmark for Robust Imitation

Imitation Learning (IL) algorithms are typically evaluated in the same e...

5 Sam Toyer, et al. ∙

research

∙ 10/12/2020

SLIP: Learning to Predict in Unknown Dynamical Systems with Long-Term Memory

We present an efficient and practical (polynomial time) algorithm for on...

0 Paria Rashidinejad, et al. ∙

research

∙ 07/19/2020

Multi-Principal Assistance Games

Assistance games (also known as cooperative inverse reinforcement learni...

0 Arnaud Fickinger, et al. ∙

research

∙ 06/24/2020

Quantifying Differences in Reward Functions

For many tasks, the reward function is too complex to be specified proce...

23 Adam Gleave, et al. ∙

research

∙ 03/10/2020

Neural Networks are Surprisingly Modular

The learned weights of a neural network are often considered devoid of s...

40 Daniel Filan, et al. ∙

research

∙ 09/10/2019

Bayesian Relational Memory for Semantic Visual Navigation

We introduce a new memory architecture, Bayesian Relational Memory (BRM)...

26 Yi Wu, et al. ∙

research

∙ 05/25/2019

Adversarial Policies: Attacking Deep Reinforcement Learning

Deep reinforcement learning (RL) policies are known to be vulnerable to ...

0 Adam Gleave, et al. ∙

research

∙ 10/24/2018

Inverse reinforcement learning for video games

Deep reinforcement learning achieves superhuman performance in a range o...

0 Aaron Tucker, et al. ∙

research

∙ 09/28/2018

Learning and Planning with a Semantic Model

Building deep reinforcement learning agents that can generalize and adap...

4 Yi Wu, et al. ∙

research

∙ 07/24/2018

Learning Plannable Representations with Causal InfoGAN

In recent years, deep generative models have been shown to 'imagine' con...

8 Thanard Kurutach, et al. ∙

research

∙ 06/11/2018

An Efficient, Generalized Bellman Update For Cooperative Inverse Reinforcement Learning

Our goal is for AI systems to correctly identify and act according to th...

0 Dhruv Malik, et al. ∙

research

∙ 06/06/2018

Discrete-Continuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference Algorithms

Despite the recent successes of probabilistic programming languages (PPL...

0 Yi Wu, et al. ∙

research

∙ 06/06/2018

On Discrete-Continuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference Algorithms

Despite of the recent successes of probabilistic programming languages (...

0 Yi Wu, et al. ∙

research

∙ 11/08/2017

Inverse Reward Design

Autonomous agents optimize the reward function we give them. What they d...

0 Dylan Hadfield-Menell, et al. ∙

research

∙ 10/31/2017

Servant of Many Masters: Shifting priorities in Pareto-optimal sequential decision-making

It is often argued that an agent making decisions on behalf of two or mo...

0 Andrew Critch, et al. ∙

research

∙ 05/28/2017

Should Robots be Obedient?

Intuitively, obedience -- following the order that a human gives -- seem...

0 Smitha Milli, et al. ∙

research

∙ 11/24/2016

The Off-Switch Game

It is clear that one of the primary tools we can use to mitigate the pot...

0 Dylan Hadfield-Menell, et al. ∙

research

∙ 06/30/2016

Swift: Compiled Inference for Probabilistic Programming Languages

A probabilistic program defines a probability measure over its semantic ...

0 Yi Wu, et al. ∙

research

∙ 06/09/2016

Cooperative Inverse Reinforcement Learning

For an autonomous system to be helpful to humans and to pose no unwarran...

0 Dylan Hadfield-Menell, et al. ∙

research

∙ 03/29/2016

Towards Practical Bayesian Parameter and State Estimation

Joint state and parameter estimation is a core problem for dynamic Bayes...

0 Yusuf Bugra Erol, et al. ∙

research

∙ 02/10/2016

Research Priorities for Robust and Beneficial Artificial Intelligence

Success in the quest for artificial intelligence has the potential to br...

0 Stuart Russell, et al. ∙

research

∙ 12/24/2015

Probabilistic Model-Based Approach for Heart Beat Detection

Nowadays, hospitals are ubiquitous and integral to modern society. Patie...

0 Hugh Chen, et al. ∙

research

∙ 08/09/2014

Selecting Computations: Theory and Applications

Sequential decision problems are often approximately solvable by simulat...

0 Nicholas Hay, et al. ∙

Stuart Russell

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