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Model-Invariant State Abstractions for Model-Based Reinforcement Learning
Accuracy and generalization of dynamics models is key to the success of ...
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Domain Adversarial Reinforcement Learning
We consider the problem of generalization in reinforcement learning wher...
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Multi-Task Reinforcement Learning with Context-based Representations
The benefit of multi-task learning over single-task learning relies on t...
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Exploring the Limits of Few-Shot Link Prediction in Knowledge Graphs
Real-world knowledge graphs are often characterized by low-frequency rel...
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COVID-19 Deterioration Prediction via Self-Supervised Representation Learning and Multi-Image Prediction
The rapid spread of COVID-19 cases in recent months has strained hospita...
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Unnatural Language Inference
Natural Language Understanding has witnessed a watershed moment with the...
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Intervention Design for Effective Sim2Real Transfer
The goal of this work is to address the recent success of domain randomi...
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Exploring Zero-Shot Emergent Communication in Embodied Multi-Agent Populations
Effective communication is an important skill for enabling information e...
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Regularized Inverse Reinforcement Learning
Inverse Reinforcement Learning (IRL) aims to facilitate a learner's abil...
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Novelty Search in representational space for sample efficient exploration
We present a new approach for efficient exploration which leverages a lo...
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Constrained Markov Decision Processes via Backward Value Functions
Although Reinforcement Learning (RL) algorithms have found tremendous su...
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How To Evaluate Your Dialogue System: Probe Tasks as an Alternative for Token-level Evaluation Metrics
Though generative dialogue modeling is widely seen as a language modelin...
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Multi-Task Reinforcement Learning as a Hidden-Parameter Block MDP
Multi-task reinforcement learning is a rich paradigm where information f...
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TDprop: Does Jacobi Preconditioning Help Temporal Difference Learning?
We investigate whether Jacobi preconditioning, accounting for the bootst...
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Deep interpretability for GWAS
Genome-Wide Association Studies are typically conducted using linear mod...
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Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization
Adversarial imitation learning alternates between learning a discriminat...
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Plan2Vec: Unsupervised Representation Learning by Latent Plans
In this paper we introduce plan2vec, an unsupervised representation lear...
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A Large-Scale, Open-Domain, Mixed-Interface Dialogue-Based ITS for STEM
We present Korbit, a large-scale, open-domain, mixed-interface, dialogue...
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Automated Personalized Feedback Improves Learning Gains in an Intelligent Tutoring System
We investigate how automated, data-driven, personalized feedback in a la...
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Learning an Unreferenced Metric for Online Dialogue Evaluation
Evaluating the quality of a dialogue interaction between two agents is a...
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Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)
One of the challenges in machine learning research is to ensure that pre...
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Evaluating Logical Generalization in Graph Neural Networks
Recent research has highlighted the role of relational inductive biases ...
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Interference and Generalization in Temporal Difference Learning
We study the link between generalization and interference in temporal-di...
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Invariant Causal Prediction for Block MDPs
Generalization across environments is critical to the successful applica...
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Stable Policy Optimization via Off-Policy Divergence Regularization
Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization...
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The importance of transparency and reproducibility in artificial intelligence research
In their study, McKinney et al. showed the high potential of artificial ...
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Scalable Multi-Agent Inverse Reinforcement Learning via Actor-Attention-Critic
Multi-agent adversarial inverse reinforcement learning (MA-AIRL) is a re...
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Provably efficient reconstruction of policy networks
Recent research has shown that learning poli-cies parametrized by large ...
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Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning
Accurate reporting of energy and carbon usage is essential for understan...
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Exploiting Spatial Invariance for Scalable Unsupervised Object Tracking
The ability to detect and track objects in the visual world is a crucial...
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Online Learned Continual Compression with Stacked Quantization Module
We introduce and study the problem of Online Continual Compression, wher...
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MVFST-RL: An Asynchronous RL Framework for Congestion Control with Delayed Actions
Effective network congestion control strategies are key to keeping the I...
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Benchmarking Batch Deep Reinforcement Learning Algorithms
Widely-used deep reinforcement learning algorithms have been shown to fa...
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Improving Sample Efficiency in Model-Free Reinforcement Learning from Images
Training an agent to solve control tasks directly from high-dimensional ...
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Attraction-Repulsion Actor-Critic for Continuous Control Reinforcement Learning
Continuous control tasks in reinforcement learning are important because...
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No Press Diplomacy: Modeling Multi-Agent Gameplay
Diplomacy is a seven-player non-stochastic, non-cooperative game, where ...
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CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text
The recent success of natural language understanding (NLU) systems has b...
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Learning Causal State Representations of Partially Observable Environments
Intelligent agents can cope with sensory-rich environments by learning t...
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Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning
Multi-simulator training has contributed to the recent success of Deep R...
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Recurrent Value Functions
Despite recent successes in Reinforcement Learning, value-based methods ...
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Leveraging exploration in off-policy algorithms via normalizing flows
Exploration is a crucial component for discovering approximately optimal...
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On the Pitfalls of Measuring Emergent Communication
How do we know if communication is emerging in a multi-agent system? The...
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Separating value functions across time-scales
In many finite horizon episodic reinforcement learning (RL) settings, it...
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The Second Conversational Intelligence Challenge (ConvAI2)
We describe the setting and results of the ConvAI2 NeurIPS competition t...
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Deep Generative Modeling of LiDAR Data
Building models capable of generating structured output is a key challen...
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An Introduction to Deep Reinforcement Learning
Deep reinforcement learning is the combination of reinforcement learning...
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Natural Environment Benchmarks for Reinforcement Learning
While current benchmark reinforcement learning (RL) tasks have been usef...
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Compositional Language Understanding with Text-based Relational Reasoning
Neural networks for natural language reasoning have largely focused on e...
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The RLLChatbot: a solution to the ConvAI Challenge
Current conversational systems can follow simple commands and answer bas...
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Language GANs Falling Short
Generating high-quality text with sufficient diversity is essential for ...
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