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06/02/2023
Improving the generalizability and robustness of large-scale traffic signal control
A number of deep reinforcement-learning (RL) approaches propose to contr...
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05/30/2023
Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network
Generative Flow Networks (GFlowNets), a class of generative models over ...
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04/25/2023
Towards Compute-Optimal Transfer Learning
The field of transfer learning is undergoing a significant shift with th...
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11/04/2022
Bayesian learning of Causal Structure and Mechanisms with GFlowNets and Variational Bayes
Bayesian causal structure learning aims to learn a posterior distributio...
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08/01/2022
Model-based graph reinforcement learning for inductive traffic signal control
Most reinforcement learning methods for adaptive-traffic-signal-control ...
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07/10/2022
Scaling the Number of Tasks in Continual Learning
Standard gradient descent algorithms applied to sequences of tasks are k...
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06/27/2022
Learning To Cut By Looking Ahead: Cutting Plane Selection via Imitation Learning
Cutting planes are essential for solving mixed-integer linear problems (...
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05/28/2022
Task-Agnostic Continual Reinforcement Learning: In Praise of a Simple Baseline
We study task-agnostic continual reinforcement learning (TACRL) in which...
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04/30/2022
Foundational Models for Continual Learning: An Empirical Study of Latent Replay
Rapid development of large-scale pre-training has resulted in foundation...
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03/04/2022
The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights
Combinatorial optimization is a well-established area in operations rese...
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03/03/2022
A New Era: Intelligent Tutoring Systems Will Transform Online Learning for Millions
Despite artificial intelligence (AI) having transformed major aspects of...
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11/15/2021
Continual Learning via Local Module Composition
Modularity is a compelling solution to continual learning (CL), the prob...
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08/02/2021
Sequoia: A Software Framework to Unify Continual Learning Research
The field of Continual Learning (CL) seeks to develop algorithms that ac...
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06/09/2021
Pretraining Representations for Data-Efficient Reinforcement Learning
Data efficiency is a key challenge for deep reinforcement learning. We a...
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04/15/2021
Comparative Study of Learning Outcomes for Online Learning Platforms
Personalization and active learning are key aspects to successful learni...
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03/18/2021
Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations
Explainability for machine learning models has gained considerable atten...
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10/27/2020
Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles
Multi-document summarization is a challenging task for which there exist...
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09/14/2020
Synbols: Probing Learning Algorithms with Synthetic Datasets
Progress in the field of machine learning has been fueled by the introdu...
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05/06/2020
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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04/23/2020
Inference for travel time on transportation networks
Travel time is essential for making travel decisions in real-world trans...
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03/12/2020
Online Fast Adaptation and Knowledge Accumulation: a New Approach to Continual Learning
Learning from non-stationary data remains a great challenge for machine ...
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03/06/2020
IG-RL: Inductive Graph Reinforcement Learning for Massive-Scale Traffic Signal Control
Scaling adaptive traffic-signal control involves dealing with combinator...
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08/11/2019
Online Continual Learning with Maximally Interfered Retrieval
Continual learning, the setting where a learning agent is faced with a n...
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06/04/2019
Exact Combinatorial Optimization with Graph Convolutional Neural Networks
Combinatorial optimization problems are typically tackled by the branch-...
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06/03/2019
Continual Learning of New Sound Classes using Generative Replay
Continual learning consists in incrementally training a model on a seque...
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02/25/2019
Session-based Social Recommendation via Dynamic Graph Attention Networks
Online communities such as Facebook and Twitter are enormously popular a...
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12/18/2018
Towards Deep Conversational Recommendations
There has been growing interest in using neural networks and deep learni...
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11/06/2018
Language GANs Falling Short
Generating high-quality text with sufficient diversity is essential for ...
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08/20/2018
The Deconfounded Recommender: A Causal Inference Approach to Recommendation
The goal of a recommender system is to show its users items that they wi...
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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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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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10/06/2017
Learnable Explicit Density for Continuous Latent Space and Variational Inference
In this paper, we study two aspects of the variational autoencoder (VAE)...
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11/18/2016
Generative Deep Neural Networks for Dialogue: A Short Review
Researchers have recently started investigating deep neural networks for...
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05/19/2016
A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues
Sequential data often possesses a hierarchical structure with complex de...
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05/18/2016
On the Evaluation of Dialogue Systems with Next Utterance Classification
An open challenge in constructing dialogue systems is developing methods...
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03/25/2016
How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation
We investigate evaluation metrics for dialogue response generation syste...
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12/17/2015
A Survey of Available Corpora for Building Data-Driven Dialogue Systems
During the past decade, several areas of speech and language understandi...
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10/23/2015
Modeling User Exposure in Recommendation
Collaborative filtering analyzes user preferences for items (e.g., books...
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09/15/2015
Dynamic Poisson Factorization
Models for recommender systems use latent factors to explain the prefere...
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11/10/2014
Deep Exponential Families
We describe deep exponential families (DEFs), a class of latent variable...
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06/18/2012
Active Learning for Matching Problems
Effective learning of user preferences is critical to easing user burden...
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06/13/2012
Hierarchical POMDP Controller Optimization by Likelihood Maximization
Planning can often be simpli ed by decomposing the task into smaller tas...
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02/14/2012