Nan Rosemary Ke

research

∙ 04/12/2023

DiscoGen: Learning to Discover Gene Regulatory Networks

Accurately inferring Gene Regulatory Networks (GRNs) is a critical and c...

0 Nan Rosemary Ke, et al. ∙

research

∙ 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 ...

0 Chentian Jiang, et al. ∙

research

∙ 10/24/2022

Learning Latent Structural Causal Models

Causal learning has long concerned itself with the accurate recovery of ...

0 Jithendaraa Subramanian, et al. ∙

research

∙ 06/16/2022

Towards Understanding How Machines Can Learn Causal Overhypotheses

Recent work in machine learning and cognitive science has suggested that...

8 Eliza Kosoy, et al. ∙

research

∙ 06/09/2022

On the Generalization and Adaption Performance of Causal Models

Learning models that offer robust out-of-distribution generalization and...

11 Nino Scherrer, et al. ∙

research

∙ 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 ...

14 Aniket Didolkar, et al. ∙

research

∙ 04/11/2022

Learning to Induce Causal Structure

The fundamental challenge in causal induction is to infer the underlying...

0 Nan Rosemary Ke, et al. ∙

research

∙ 02/21/2022

Learning Causal Overhypotheses through Exploration in Children and Computational Models

Despite recent progress in reinforcement learning (RL), RL algorithms fo...

21 Eliza Kosoy, et al. ∙

research

∙ 02/17/2022

Retrieval-Augmented Reinforcement Learning

Most deep reinforcement learning (RL) algorithms distill experience into...

0 Anirudh Goyal, et al. ∙

research

∙ 09/06/2021

Learning Neural Causal Models with Active Interventions

Discovering causal structures from data is a challenging inference probl...

7 Nino Scherrer, et al. ∙

research

∙ 07/02/2021

Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning

Inducing causal relationships from observations is a classic problem in ...

7 Nan Rosemary Ke, et al. ∙

research

∙ 05/18/2021

Fast and Slow Learning of Recurrent Independent Mechanisms

Decomposing knowledge into interchangeable pieces promises a generalizat...

9 Kanika Madan, et al. ∙

research

∙ 03/02/2021

Neural Production Systems

Visual environments are structured, consisting of distinct objects or en...

12 Anirudh Goyal, et al. ∙

research

∙ 03/01/2021

Coordination Among Neural Modules Through a Shared Global Workspace

Deep learning has seen a movement away from representing examples with a...

14 Anirudh Goyal, et al. ∙

research

∙ 02/22/2021

Towards Causal Representation Learning

The two fields of machine learning and graphical causality arose and dev...

44 Bernhard Schölkopf, et al. ∙

research

∙ 11/23/2020

On the Convergence of Continuous Constrained Optimization for Structure Learning

Structure learning of directed acyclic graphs (DAGs) is a fundamental pr...

0 Ignavier Ng, et al. ∙

research

∙ 08/21/2020

Amortized learning of neural causal representations

Causal models can compactly and efficiently encode the data-generating p...

5 Nan Rosemary Ke, et al. ∙

research

∙ 02/07/2020

Causally Correct Partial Models for Reinforcement Learning

In reinforcement learning, we can learn a model of future observations a...

17 Danilo J. Rezende, et al. ∙

research

∙ 10/02/2019

Learning Neural Causal Models from Unknown Interventions

Meta-learning over a set of distributions can be interpreted as learning...

31 Nan Rosemary Ke, et al. ∙

research

∙ 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...

14 Nan Rosemary Ke, et al. ∙

research

∙ 10/06/2018

h-detach: Modifying the LSTM Gradient Towards Better Optimization

Recurrent neural networks are known for their notorious exploding and va...

0 Devansh Arpit, et al. ∙

research

∙ 09/11/2018

Sparse Attentive Backtracking: Temporal CreditAssignment Through Reminding

Learning long-term dependencies in extended temporal sequences requires ...

0 Nan Rosemary Ke, et al. ∙

research

∙ 06/12/2018

Focused Hierarchical RNNs for Conditional Sequence Processing

Recurrent Neural Networks (RNNs) with attention mechanisms have obtained...

0 Nan Rosemary Ke, et al. ∙

research

∙ 01/20/2018

A Deep Reinforcement Learning Chatbot (Short Version)

We present MILABOT: a deep reinforcement learning chatbot developed by t...

0 Iulian V. Serban, et al. ∙

research

∙ 11/24/2017

Ethical Challenges in Data-Driven Dialogue Systems

The use of dialogue systems as a medium for human-machine interaction is...

0 Peter Henderson, et al. ∙

research

∙ 11/15/2017

Z-Forcing: Training Stochastic Recurrent Networks

Many efforts have been devoted to training generative latent variable mo...

0 Anirudh Goyal, et al. ∙

research

∙ 11/13/2017

ACtuAL: Actor-Critic Under Adversarial Learning

Generative Adversarial Networks (GANs) are a powerful framework for deep...

0 Anirudh Goyal, et al. ∙

research

∙ 11/07/2017

Sparse Attentive Backtracking: Long-Range Credit Assignment in Recurrent Networks

A major drawback of backpropagation through time (BPTT) is the difficult...

0 Nan Rosemary Ke, et al. ∙

research

∙ 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...

0 Anirudh Goyal, et al. ∙

research

∙ 09/07/2017

A Deep Reinforcement Learning Chatbot

We present MILABOT: a deep reinforcement learning chatbot developed by t...

0 Iulian V. Serban, et al. ∙

research

∙ 06/03/2016

Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations

We propose zoneout, a novel method for regularizing RNNs. At each timest...

0 David Krueger, et al. ∙

research

∙ 03/17/2016

Cascading Bandits for Large-Scale Recommendation Problems

Most recommender systems recommend a list of items. The user examines th...

0 Shi Zong, et al. ∙

research

∙ 04/07/2015

Transferring Knowledge from a RNN to a DNN

Deep Neural Network (DNN) acoustic models have yielded many state-of-the...

0 William Chan, et al. ∙

Nan Rosemary Ke

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