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Publications
How Should We Extract Discrete Audio Tokens from Self-Supervised Models?
A number of deep reinforcement-learning (RL) approaches propose to control traffic signals. Compared to traditional approaches, RL approache… (see more)s can learn from higher-dimensionality input road and vehicle sensors and better adapt to varying traffic conditions resulting in reduced travel times (in simulation). However, these RL methods require training from massive traffic sensor data. To offset this relative inefficiency, some recent RL methods have the ability to first learn from small-scale networks and then generalize to unseen city-scale networks without additional retraining (zero-shot transfer). In this work, we study the robustness of such methods along two axes. First, sensor failures and GPS occlusions create missing-data challenges and we show that recent methods remain brittle in the face of these missing data. Second, we provide a more systematic study of the generalization ability of RL methods to new networks with different traffic regimes. Again, we identify the limitations of recent approaches. We then propose using a combination of distributional and vanilla reinforcement learning through a policy ensemble. Building upon the state-of-the-art previous model which uses a decentralized approach for large-scale traffic signal control with graph convolutional networks (GCNs), we first learn models using a distributional reinforcement learning (DisRL) approach. In particular, we use implicit quantile networks (IQN) to model the state-action return distribution with quantile regression. For traffic signal control problems, an ensemble of standard RL and DisRL yields superior performance across different scenarios, including different levels of missing sensor data and traffic flow patterns. Furthermore, the learning scheme of the resulting model can improve zero-shot transferability to different road network structures, including both synthetic networks and real-world networks (e.g., Luxembourg, Manhattan). We conduct extensive experiments to compare our approach to multi-agent reinforcement learning and traditional transportation approaches. Results show that the proposed method improves robustness and generalizability in the face of missing data, varying road networks, and traffic flows.
2024-01-01
IEEE Open Journal of Intelligent Transportation Systems (published)
This paper gives an experimentally supported review and comparison of several indices based on the conventional K-means inertia criterion fo… (see more)r determining the number of clusters,
This paper gives an experimentally supported review and comparison of several indices based on the conventional K-means inertia criterion fo… (see more)r determining the number of clusters,
This paper gives an experimentally supported review and comparison of several indices based on the conventional K-means inertia criterion fo… (see more)r determining the number of clusters,
This paper gives an experimentally supported review and comparison of several indices based on the conventional K-means inertia criterion fo… (see more)r determining the number of clusters,
In this work, we investigate the interplay between memorization and learning in the context of stochastic convex optimization (SCO)… (see more). We define memorization via the information a learning algorithm reveals about its training data points. We then quantify this information using the framework of conditional mutual information (CMI) proposed by Steinke and Zakynthinou (2020). Our main result is a precise characterization of the tradeoff between the accuracy of a learning algorithm and its CMI, answering an open question posed by Livni (2023). We show that, in the
2024-01-01
International Conference on Machine Learning (published)