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04/12/2023
DiscoGen: Learning to Discover Gene Regulatory Networks
Accurately inferring Gene Regulatory Networks (GRNs) is a critical and c...
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02/08/2023
Learning How to Infer Partial MDPs for In-Context Adaptation and Exploration
To generalize across tasks, an agent should acquire knowledge from past ...
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10/24/2022
Learning Latent Structural Causal Models
Causal learning has long concerned itself with the accurate recovery of ...
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06/16/2022
Towards Understanding How Machines Can Learn Causal Overhypotheses
Recent work in machine learning and cognitive science has suggested that...
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06/09/2022
On the Generalization and Adaption Performance of Causal Models
Learning models that offer robust out-of-distribution generalization and...
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05/30/2022
Temporal Latent Bottleneck: Synthesis of Fast and Slow Processing Mechanisms in Sequence Learning
Recurrent neural networks have a strong inductive bias towards learning ...
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04/11/2022
Learning to Induce Causal Structure
The fundamental challenge in causal induction is to infer the underlying...
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02/21/2022
Learning Causal Overhypotheses through Exploration in Children and Computational Models
Despite recent progress in reinforcement learning (RL), RL algorithms fo...
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02/17/2022
Retrieval-Augmented Reinforcement Learning
Most deep reinforcement learning (RL) algorithms distill experience into...
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09/06/2021
Learning Neural Causal Models with Active Interventions
Discovering causal structures from data is a challenging inference probl...
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07/02/2021
Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning
Inducing causal relationships from observations is a classic problem in ...
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05/18/2021
Fast and Slow Learning of Recurrent Independent Mechanisms
Decomposing knowledge into interchangeable pieces promises a generalizat...
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03/02/2021
Neural Production Systems
Visual environments are structured, consisting of distinct objects or en...
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03/01/2021
Coordination Among Neural Modules Through a Shared Global Workspace
Deep learning has seen a movement away from representing examples with a...
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02/22/2021
Towards Causal Representation Learning
The two fields of machine learning and graphical causality arose and dev...
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11/23/2020
On the Convergence of Continuous Constrained Optimization for Structure Learning
Structure learning of directed acyclic graphs (DAGs) is a fundamental pr...
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08/21/2020
Amortized learning of neural causal representations
Causal models can compactly and efficiently encode the data-generating p...
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02/07/2020
Causally Correct Partial Models for Reinforcement Learning
In reinforcement learning, we can learn a model of future observations a...
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10/02/2019
Learning Neural Causal Models from Unknown Interventions
Meta-learning over a set of distributions can be interpreted as learning...
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03/05/2019
Learning Dynamics Model in Reinforcement Learning by Incorporating the Long Term Future
In model-based reinforcement learning, the agent interleaves between mod...
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10/06/2018
h-detach: Modifying the LSTM Gradient Towards Better Optimization
Recurrent neural networks are known for their notorious exploding and va...
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09/11/2018
Sparse Attentive Backtracking: Temporal CreditAssignment Through Reminding
Learning long-term dependencies in extended temporal sequences requires ...
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06/12/2018
Focused Hierarchical RNNs for Conditional Sequence Processing
Recurrent Neural Networks (RNNs) with attention mechanisms have obtained...
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01/20/2018
A Deep Reinforcement Learning Chatbot (Short Version)
We present MILABOT: a deep reinforcement learning chatbot developed by t...
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11/24/2017
Ethical Challenges in Data-Driven Dialogue Systems
The use of dialogue systems as a medium for human-machine interaction is...
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11/15/2017
Z-Forcing: Training Stochastic Recurrent Networks
Many efforts have been devoted to training generative latent variable mo...
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11/13/2017
ACtuAL: Actor-Critic Under Adversarial Learning
Generative Adversarial Networks (GANs) are a powerful framework for deep...
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11/07/2017
Sparse Attentive Backtracking: Long-Range Credit Assignment in Recurrent Networks
A major drawback of backpropagation through time (BPTT) is the difficult...
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11/07/2017
Variational Walkback: Learning a Transition Operator as a Stochastic Recurrent Net
We propose a novel method to directly learn a stochastic transition oper...
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09/07/2017
A Deep Reinforcement Learning Chatbot
We present MILABOT: a deep reinforcement learning chatbot developed by t...
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06/03/2016
Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations
We propose zoneout, a novel method for regularizing RNNs. At each timest...
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03/17/2016
Cascading Bandits for Large-Scale Recommendation Problems
Most recommender systems recommend a list of items. The user examines th...
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04/07/2015
