Christoph Dann | DeepAI

DeepAI AI Chat

Featured Co-authors

Eric P Xing
147 publications
Wei Wei
146 publications
Andrew Gordon Wilson
89 publications
Yishay Mansour
88 publications
Nan Jiang
83 publications
Akshay Krishnamurthy
74 publications
Emma Brunskill
63 publications
Lihong Li
61 publications
Alekh Agarwal
61 publications
John Langford
57 publications
Tor Lattimore
56 publications

∙ 02/20/2023

A Blackbox Approach to Best of Both Worlds in Bandits and Beyond

Best-of-both-worlds algorithms for online learning which achieve near-op...

0 Christoph Dann, et al. ∙

∙ 02/18/2023

Best of Both Worlds Policy Optimization

Policy optimization methods are popular reinforcement learning algorithm...

0 Christoph Dann, et al. ∙

∙ 02/03/2023

Pseudonorm Approachability and Applications to Regret Minimization

Blackwell's celebrated approachability theory provides a general framewo...

0 Christoph Dann, et al. ∙

∙ 10/17/2022

A Unified Algorithm for Stochastic Path Problems

We study reinforcement learning in stochastic path (SP) problems. The go...

0 Christoph Dann, et al. ∙

∙ 06/29/2022

Best of Both Worlds Model Selection

We study the problem of model selection in bandit scenarios in the prese...

0 Aldo Pacchiano, et al. ∙

∙ 06/19/2022

Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation

Myopic exploration policies such as epsilon-greedy, softmax, or Gaussian...

0 Christoph Dann, et al. ∙

∙ 02/21/2022

Same Cause; Different Effects in the Brain

To study information processing in the brain, neuroscientists manipulate...

12 Mariya Toneva, et al. ∙

∙ 10/07/2021

A Model Selection Approach for Corruption Robust Reinforcement Learning

We develop a model selection approach to tackle reinforcement learning w...

0 Chen-Yu Wei, et al. ∙

∙ 07/02/2021

Beyond Value-Function Gaps: Improved Instance-Dependent Regret Bounds for Episodic Reinforcement Learning

We provide improved gap-dependent regret bounds for reinforcement learni...

6 Christoph Dann, et al. ∙

∙ 06/22/2021

Agnostic Reinforcement Learning with Low-Rank MDPs and Rich Observations

There have been many recent advances on provably efficient Reinforcement...

10 Christoph Dann, et al. ∙

∙ 06/06/2021

Neural Active Learning with Performance Guarantees

We investigate the problem of active learning in the streaming setting i...

0 Pranjal Awasthi, et al. ∙

∙ 12/24/2020

Regret Bound Balancing and Elimination for Model Selection in Bandits and RL

We propose a simple model selection approach for algorithms in stochasti...

0 Aldo Pacchiano, et al. ∙

∙ 05/07/2020

Reinforcement Learning with Feedback Graphs

We study episodic reinforcement learning in Markov decision processes wh...

28 Christoph Dann, et al. ∙

∙ 11/05/2019

Being Optimistic to Be Conservative: Quickly Learning a CVaR Policy

While maximizing expected return is the goal in most reinforcement learn...

0 Ramtin Keramati, et al. ∙

∙ 11/07/2018

Policy Certificates: Towards Accountable Reinforcement Learning

The performance of a reinforcement learning algorithm can vary drastical...

0 Christoph Dann, et al. ∙

∙ 03/01/2018

On Polynomial Time PAC Reinforcement Learning with Rich Observations

We study the computational tractability of provably sample-efficient (PA...

0 Christoph Dann, et al. ∙

∙ 06/09/2017

Decoupling Learning Rules from Representations

In the artificial intelligence field, learning often corresponds to chan...

0 Philip S. Thomas, et al. ∙

∙ 03/22/2017

Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning

Statistical performance bounds for reinforcement learning (RL) algorithm...

0 Christoph Dann, et al. ∙

∙ 02/21/2017

Sample Efficient Policy Search for Optimal Stopping Domains

Optimal stopping problems consider the question of deciding when to stop...

0 Karan Goel, et al. ∙

∙ 11/21/2016

Memory Lens: How Much Memory Does an Agent Use?

We propose a new method to study the internal memory used by reinforceme...

0 Christoph Dann, et al. ∙

∙ 11/05/2015

Thoughts on Massively Scalable Gaussian Processes

We introduce a framework and early results for massively scalable Gaussi...

0 Andrew Gordon Wilson, et al. ∙

∙ 10/29/2015

Sample Complexity of Episodic Fixed-Horizon Reinforcement Learning

Recently, there has been significant progress in understanding reinforce...

0 Christoph Dann, et al. ∙

∙ 10/26/2015

The Human Kernel

Bayesian nonparametric models, such as Gaussian processes, provide a com...

0 Andrew Gordon Wilson, et al. ∙

∙ 07/22/2015

Bayesian Time-of-Flight for Realtime Shape, Illumination and Albedo

We propose a computational model for shape, illumination and albedo infe...

0 Amit Adam, et al. ∙