Search Results for author: David Krueger

Found 19 papers, 9 papers with code

Active Reinforcement Learning: Observing Rewards at a Cost

no code implementations13 Nov 2020 David Krueger, Jan Leike, Owain Evans, John Salvatier

Active reinforcement learning (ARL) is a variant on reinforcement learning where the agent does not observe the reward unless it chooses to pay a query cost c > 0.

Multi-Armed Bandits

Hidden Incentives for Auto-Induced Distributional Shift

no code implementations19 Sep 2020 David Krueger, Tegan Maharaj, Jan Leike

We introduce the term auto-induced distributional shift (ADS) to describe the phenomenon of an algorithm causing a change in the distribution of its own inputs.

Meta-Learning Q-Learning

AI Research Considerations for Human Existential Safety (ARCHES)

no code implementations30 May 2020 Andrew Critch, David Krueger

Framed in positive terms, this report examines how technical AI research might be steered in a manner that is more attentive to humanity's long-term prospects for survival as a species.

Out-of-Distribution Generalization via Risk Extrapolation (REx)

3 code implementations2 Mar 2020 David Krueger, Ethan Caballero, Joern-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Dinghuai Zhang, Remi Le Priol, Aaron Courville

Distributional shift is one of the major obstacles when transferring machine learning prediction systems from the lab to the real world.

Domain Generalization

Scalable agent alignment via reward modeling: a research direction

3 code implementations19 Nov 2018 Jan Leike, David Krueger, Tom Everitt, Miljan Martic, Vishal Maini, Shane Legg

One obstacle to applying reinforcement learning algorithms to real-world problems is the lack of suitable reward functions.

Atari Games

Neural Autoregressive Flows

4 code implementations ICML 2018 Chin-wei Huang, David Krueger, Alexandre Lacoste, Aaron Courville

Normalizing flows and autoregressive models have been successfully combined to produce state-of-the-art results in density estimation, via Masked Autoregressive Flows (MAF), and to accelerate state-of-the-art WaveNet-based speech synthesis to 20x faster than real-time, via Inverse Autoregressive Flows (IAF).

Density Estimation Speech Synthesis

Nested LSTMs

1 code implementation31 Jan 2018 Joel Ruben Antony Moniz, David Krueger

We propose Nested LSTMs (NLSTM), a novel RNN architecture with multiple levels of memory.

Language Modelling

Deep Prior

no code implementations13 Dec 2017 Alexandre Lacoste, Thomas Boquet, Negar Rostamzadeh, Boris Oreshkin, Wonchang Chung, David Krueger

The recent literature on deep learning offers new tools to learn a rich probability distribution over high dimensional data such as images or sounds.

Regularizing RNNs by Stabilizing Activations

1 code implementation26 Nov 2015 David Krueger, Roland Memisevic

We stabilize the activations of Recurrent Neural Networks (RNNs) by penalizing the squared distance between successive hidden states' norms.

Language Modelling

NICE: Non-linear Independent Components Estimation

16 code implementations30 Oct 2014 Laurent Dinh, David Krueger, Yoshua Bengio

It is based on the idea that a good representation is one in which the data has a distribution that is easy to model.

Ranked #59 on Image Generation on CIFAR-10 (bits/dimension metric)

Image Generation

Zero-bias autoencoders and the benefits of co-adapting features

no code implementations13 Feb 2014 Kishore Konda, Roland Memisevic, David Krueger

We show that negative biases are a natural result of using a hidden layer whose responsibility is to both represent the input data and act as a selection mechanism that ensures sparsity of the representation.

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