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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...
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06/15/2023
Who Needs to Know? Minimal Knowledge for Optimal Coordination
To optimally coordinate with others in cooperative games, it is often cr...
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06/12/2023
TASRA: a Taxonomy and Analysis of Societal-Scale Risks from AI
While several recent works have identified societal-scale and extinction...
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04/19/2023
Bridging RL Theory and Practice with the Effective Horizon
Deep reinforcement learning (RL) works impressively in some environments...
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03/02/2023
Active Reward Learning from Multiple Teachers
Reward learning algorithms utilize human feedback to infer a reward func...
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11/22/2022
imitation: Clean Imitation Learning Implementations
imitation provides open-source implementations of imitation and reward l...
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11/01/2022
Optimal Conservative Offline RL with General Function Approximation via Augmented Lagrangian
Offline reinforcement learning (RL), which refers to decision-making fro...
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11/01/2022
Adversarial Policies Beat Professional-Level Go AIs
We attack the state-of-the-art Go-playing AI system, KataGo, by training...
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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...
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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...
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05/16/2022
An Empirical Investigation of Representation Learning for Imitation
Imitation learning often needs a large demonstration set in order to han...
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04/25/2022
Estimating and Penalizing Induced Preference Shifts in Recommender Systems
The content that a recommender system (RS) shows to users influences the...
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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...
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10/13/2021
Detecting Modularity in Deep Neural Networks
A neural network is modular to the extent that parts of its computationa...
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10/07/2021
Cross-Domain Imitation Learning via Optimal Transport
Cross-domain imitation learning studies how to leverage expert demonstra...
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09/30/2021
Scalable Online Planning via Reinforcement Learning Fine-Tuning
Lookahead search has been a critical component of recent AI successes, s...
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07/12/2021
Explore and Control with Adversarial Surprise
Reinforcement learning (RL) provides a framework for learning goal-direc...
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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...
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06/19/2021
Learning the Preferences of Uncertain Humans with Inverse Decision Theory
Existing observational approaches for learning human preferences, such a...
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06/18/2021
MADE: Exploration via Maximizing Deviation from Explored Regions
In online reinforcement learning (RL), efficient exploration remains par...
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03/22/2021
Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism
Offline (or batch) reinforcement learning (RL) algorithms seek to learn ...
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03/04/2021
Clusterability in Neural Networks
The learned weights of a neural network have often been considered devoi...
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01/25/2021
Accumulating Risk Capital Through Investing in Cooperation
Recent work on promoting cooperation in multi-agent learning has resulte...
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12/29/2020
Multi-Principal Assistance Games: Definition and Collegial Mechanisms
We introduce the concept of a multi-principal assistance game (MPAG), an...
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12/10/2020
Understanding Learned Reward Functions
In many real-world tasks, it is not possible to procedurally specify an ...
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12/03/2020
Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design
A wide range of reinforcement learning (RL) problems - including robustn...
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12/02/2020
DERAIL: Diagnostic Environments for Reward And Imitation Learning
The objective of many real-world tasks is complex and difficult to proce...
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11/01/2020
The MAGICAL Benchmark for Robust Imitation
Imitation Learning (IL) algorithms are typically evaluated in the same e...
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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...
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07/19/2020
Multi-Principal Assistance Games
Assistance games (also known as cooperative inverse reinforcement learni...
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06/24/2020
Quantifying Differences in Reward Functions
For many tasks, the reward function is too complex to be specified proce...
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03/10/2020
Neural Networks are Surprisingly Modular
The learned weights of a neural network are often considered devoid of s...
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09/10/2019
Bayesian Relational Memory for Semantic Visual Navigation
We introduce a new memory architecture, Bayesian Relational Memory (BRM)...
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05/25/2019
Adversarial Policies: Attacking Deep Reinforcement Learning
Deep reinforcement learning (RL) policies are known to be vulnerable to ...
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10/24/2018
Inverse reinforcement learning for video games
Deep reinforcement learning achieves superhuman performance in a range o...
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09/28/2018
Learning and Planning with a Semantic Model
Building deep reinforcement learning agents that can generalize and adap...
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07/24/2018
Learning Plannable Representations with Causal InfoGAN
In recent years, deep generative models have been shown to 'imagine' con...
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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...
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06/06/2018
Discrete-Continuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference Algorithms
Despite the recent successes of probabilistic programming languages (PPL...
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06/06/2018
On Discrete-Continuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference Algorithms
Despite of the recent successes of probabilistic programming languages (...
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11/08/2017
Inverse Reward Design
Autonomous agents optimize the reward function we give them. What they d...
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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...
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05/28/2017
Should Robots be Obedient?
Intuitively, obedience -- following the order that a human gives -- seem...
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11/24/2016
The Off-Switch Game
It is clear that one of the primary tools we can use to mitigate the pot...
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06/30/2016
Swift: Compiled Inference for Probabilistic Programming Languages
A probabilistic program defines a probability measure over its semantic ...
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06/09/2016
Cooperative Inverse Reinforcement Learning
For an autonomous system to be helpful to humans and to pose no unwarran...
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03/29/2016
Towards Practical Bayesian Parameter and State Estimation
Joint state and parameter estimation is a core problem for dynamic Bayes...
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02/10/2016
Research Priorities for Robust and Beneficial Artificial Intelligence
Success in the quest for artificial intelligence has the potential to br...
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12/24/2015
Probabilistic Model-Based Approach for Heart Beat Detection
Nowadays, hospitals are ubiquitous and integral to modern society. Patie...
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08/09/2014