109 citations · 410 across the 44 of their papers we have counts for
18 papers · 1 filter
Revisiting Random Forests in a Comparative Evaluation of Graph Convolutional Neural Network Variants for Traffic Prediction
Ta Jiun Ting, Xiaocan Li, Scott Sanner +1
Traffic prediction is a spatiotemporal predictive task that plays an essential role in intelligent transportation systems. Today, graph convolutional neural networks (GCNNs) have b…
Perimeter Control Using Deep Reinforcement Learning: A Model-free Approach towards Homogeneous Flow Rate Optimization
Xiaocan Li, Ray Coden Mercurius, Ayal Taitler +4
Perimeter control maintains high traffic efficiency within protected regions by controlling transfer flows among regions to ensure that their traffic densities are below critical v…
A Generalized Framework for Predictive Clustering and Optimization
Aravinth Chembu, Scott Sanner
Clustering is a powerful and extensively used data science tool. While clustering is generally thought of as an unsupervised learning technique, there are also supervised variation…
Safe MDP Planning by Learning Temporal Patterns of Undesirable Trajectories and Averting Negative Side Effects
Siow Meng Low, Akshat Kumar, Scott Sanner
In safe MDP planning, a cost function based on the current state and action is often used to specify safety aspects. In the real world, often the state representation used may lack…
Learning to Follow Instructions in Text-Based Games
Mathieu Tuli, Andrew C. Li, Pashootan Vaezipoor +3
Text-based games present a unique class of sequential decision making problem in which agents interact with a partially observable, simulated environment via actions and observatio…
Conservative Bayesian Model-Based Value Expansion for Offline Policy Optimization
Jihwan Jeong, Xiaoyu Wang, Michael Gimelfarb +3
Offline reinforcement learning (RL) addresses the problem of learning a performant policy from a fixed batch of data collected by following some behavior policy. Model-based approa…
Multi-axis Attentive Prediction for Sparse EventData: An Application to Crime Prediction
Yi Sui, Ga Wu, Scott Sanner
Spatiotemporal prediction of event data is a challenging task with a long history of research. While recent work in spatiotemporal prediction has leveraged deep sequential models t…
RAPTOR: End-to-end Risk-Aware MDP Planning and Policy Learning by Backpropagation
Noah Patton, Jihwan Jeong, Michael Gimelfarb +1
Planning provides a framework for optimizing sequential decisions in complex environments. Recent advances in efficient planning in deterministic or stochastic high-dimensional dom…
Risk-Aware Transfer in Reinforcement Learning using Successor Features
Michael Gimelfarb, André Barreto, Scott Sanner +1
Sample efficiency and risk-awareness are central to the development of practical reinforcement learning (RL) for complex decision-making. The former can be addressed by transfer le…
EDDA: Explanation-driven Data Augmentation to Improve Explanation Faithfulness
Ruiwen Li, Zhibo Zhang, Jiani Li +5
Recent years have seen the introduction of a range of methods for post-hoc explainability of image classifier predictions. However, these post-hoc explanations may not always be fa…
Supervised Contrastive Replay: Revisiting the Nearest Class Mean Classifier in Online Class-Incremental Continual Learning
Zheda Mai, Ruiwen Li, Hyunwoo Kim +1
Online class-incremental continual learning (CL) studies the problem of learning new classes continually from an online non-stationary data stream, intending to adapt to new data w…
Online Continual Learning in Image Classification: An Empirical Survey
Zheda Mai, Ruiwen Li, Jihwan Jeong +3
Online continual learning for image classification studies the problem of learning to classify images from an online stream of data and tasks, where tasks may include new classes (…
Online Class-Incremental Continual Learning with Adversarial Shapley Value
Dongsub Shim, Zheda Mai, Jihwan Jeong +3
As image-based deep learning becomes pervasive on every device, from cell phones to smart watches, there is a growing need to develop methods that continually learn from data while…
Batch-level Experience Replay with Review for Continual Learning
Zheda Mai, Hyunwoo Kim, Jihwan Jeong +1
Continual learning is a branch of deep learning that seeks to strike a balance between learning stability and plasticity. The CVPR 2020 CLVision Continual Learning for Computer Vis…
ε-BMC: A Bayesian Ensemble Approach to Epsilon-Greedy Exploration in Model-Free Reinforcement Learning
Michael Gimelfarb, Scott Sanner, Chi-Guhn Lee
Resolving the exploration-exploitation trade-off remains a fundamental problem in the design and implementation of reinforcement learning (RL) algorithms. In this paper, we focus o…
Bayesian Experience Reuse for Learning from Multiple Demonstrators
Michael Gimelfarb, Scott Sanner, Chi-Guhn Lee
Learning from demonstrations (LfD) improves the exploration efficiency of a learning agent by incorporating demonstrations from experts. However, demonstration data can often come…
Contextual Policy Transfer in Reinforcement Learning Domains via Deep Mixtures-of-Experts
Michael Gimelfarb, Scott Sanner, Chi-Guhn Lee
In reinforcement learning, agents that consider the context, or current state, when selecting source policies for transfer have been shown to outperform context-free approaches. Ho…
Scalable Planning with Tensorflow for Hybrid Nonlinear Domains
Ga Wu, Buser Say, Scott Sanner
Given recent deep learning results that demonstrate the ability to effectively optimize high-dimensional non-convex functions with gradient descent optimization on GPUs, we ask in…