| 1. |
- Antaris, Stefanos, 1988-, et al.
(författare)
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A Deep Graph Reinforcement Learning Model for Improving User Experience in Live Video Streaming
- 2021
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Ingår i: 2021 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 1787-1796
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Konferensbidrag (refereegranskat)abstract
- In this paper we present a deep graph reinforcement learning model to predict and improve the user experience during a live video streaming event, orchestrated by an agent/tracker. We first formulate the user experience prediction problem as a classification task, accounting for the fact that most of the viewers at the beginning of an event have poor quality of experience due to low-bandwidth connections and limited interactions with the tracker. In our model we consider different factors that influence the quality of user experience and train the proposed model on diverse state-action transitions when viewers interact with the tracker. In addition, provided that past events have various user experience characteristics we follow a gradient boosting strategy to compute a global model that learns from different events. Our experiments with three real-world datasets of live video streaming events demonstrate the superiority of the proposed model against several baseline strategies. Moreover, as the majority of the viewers at the beginning of an event has poor experience, we show that our model can significantly increase the number of viewers with high quality experience by at least 75% over the first streaming minutes. Our evaluation datasets and implementation are publicly available at https://publicresearch.z13.web.core.windows.net
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| 2. |
- Antaris, Stefanos, 1988-, et al.
(författare)
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Distill2Vec : Dynamic Graph Representation Learning with Knowledge Distillation
- 2020
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Ingår i: 2020 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 60-64
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Konferensbidrag (refereegranskat)abstract
- Dynamic graph representation learning strategies are based on different neural architectures to capture the graph evolution over time. However, the underlying neural architectures require a large amount of parameters to train and suffer from high online inference latency, that is several model parameters have to be updated when new data arrive online. In this study we propose Distill2Vec, a knowledge distillation strategy to train a compact model with a low number of trainable parameters, so as to reduce the latency of online inference and maintain the model accuracy high. We design a distillation loss function based on Kullback-Leibler divergence to transfer the acquired knowledge from a teacher model trained on offline data, to a small-size student model for online data. Our experiments with publicly available datasets show the superiority of our proposed model over several state-of-the-art approaches with relative gains up to 5% in the link prediction task. In addition, we demonstrate the effectiveness of our knowledge distillation strategy, in terms of number of required parameters, where Distill2Vec achieves a compression ratio up to 7:100 when compared with baseline approaches. For reproduction purposes, our implementation is publicly available at https://stefanosantaris.github.io/Distill2Vec.
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| 3. |
- Antaris, Stefanos, 1988-, et al.
(författare)
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EGAD : Evolving Graph Representation Learning with Self-Attention and Knowledge Distillation for Live Video Streaming Events
- 2020
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Ingår i: 2020 IEEE international conference on big data (big data). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 1455-1464
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Konferensbidrag (refereegranskat)abstract
- In this study, we present a dynamic graph representation learning model on weighted graphs to accurately predict the network capacity of connections between viewers in a live video streaming event. We propose EGAD, a neural network architecture to capture the graph evolution by introducing a self-attention mechanism on the weights between consecutive graph convolutional networks. In addition, we account for the fact that neural architectures require a huge amount of parameters to train, thus increasing the online inference latency and negatively influencing the user experience in a live video streaming event. To address the problem of the high online inference of a vast number of parameters, we propose a knowledge distillation strategy. In particular, we design a distillation loss function, aiming to first pretrain a teacher model on offline data, and then transfer the knowledge from the teacher to a smaller student model with less parameters. We evaluate our proposed model on the link prediction task on three real-world datasets, generated by live video streaming events. The events lasted 80 minutes and each viewer exploited the distribution solution provided by the company Hive Streaming AB. The experiments demonstrate the effectiveness of the proposed model in terms of link prediction accuracy and number of required parameters, when evaluated against state-of-the-art approaches. In addition, we study the distillation performance of the proposed model in terms of compression ratio for different distillation strategies, where we show that the proposed model can achieve a compression ratio up to 15:100, preserving high link prediction accuracy. For reproduction purposes, our evaluation datasets and implementation are publicly available at https://stefanosantaris.github.io/EGAD.
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| 4. |
- Antaris, Stefanos, 1988-, et al.
(författare)
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Knowledge distillation on neural networks for evolving graphs
- 2021
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Ingår i: Social Network Analysis and Mining. - : SPRINGER WIEN. - 1869-5450 .- 1869-5469. ; 11:1
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Tidskriftsartikel (refereegranskat)abstract
- Graph representation learning on dynamic graphs has become an important task on several real-world applications, such as recommender systems, email spam detection, and so on. To efficiently capture the evolution of a graph, representation learning approaches employ deep neural networks, with large amount of parameters to train. Due to the large model size, such approaches have high online inference latency. As a consequence, such models are challenging to deploy to an industrial setting with vast number of users/nodes. In this study, we propose DynGKD, a distillation strategy to transfer the knowledge from a large teacher model to a small student model with low inference latency, while achieving high prediction accuracy. We first study different distillation loss functions to separately train the student model with various types of information from the teacher model. In addition, we propose a hybrid distillation strategy for evolving graph representation learning to combine the teacher's different types of information. Our experiments with five publicly available datasets demonstrate the superiority of our proposed model against several baselines, with average relative drop 40.60% in terms of RMSE in the link prediction task. Moreover, our DynGKD model achieves a compression ratio of 21: 100, accelerating the inference latency with a speed up factor x30, when compared with the teacher model. For reproduction purposes, we make our datasets and implementation publicly available at https://github.com/stefanosantaris/DynGKD.
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| 5. |
- Antaris, Stefanos, et al.
(författare)
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Meta-reinforcement learning via buffering graph signatures for live video streaming events
- 2021
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Ingår i: Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2021. - New York, NY, USA : Association for Computing Machinery (ACM). ; , s. 385-392
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Konferensbidrag (refereegranskat)abstract
- In this study, we present a meta-learning model to adapt the predictions of the network's capacity between viewers who participate in a live video streaming event. We propose the MELANIE model, where an event is formulated as a Markov Decision Process, performing meta-learning on reinforcement learning tasks. By considering a new event as a task, we design an actor-critic learning scheme to compute the optimal policy on estimating the viewers' high-bandwidth connections. To ensure fast adaptation to new connections or changes among viewers during an event, we implement a prioritized replay memory buffer based on the Kullback-Leibler divergence of the reward/throughput of the viewers' connections. Moreover, we adopt a model-agnostic meta-learning framework to generate a global model from past events. As viewers scarcely participate in several events, the challenge resides on how to account for the low structural similarity of different events. To combat this issue, we design a graph signature buffer to calculate the structural similarities of several streaming events and adjust the training of the global model accordingly. We evaluate the proposed model on the link weight prediction task on three real-world datasets of live video streaming events. Our experiments demonstrate the effectiveness of our proposed model, with an average relative gain of 25% against state-of-the-art strategies. For reproduction purposes, our evaluation datasets and implementation are publicly available at https://github.com/stefanosantaris/melanie
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| 6. |
- Antaris, Stefanos, 1988-, et al.
(författare)
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Multi-task Learning for User Engagement and Adoption in Live Video Streaming Events
- 2021
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Ingår i: MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2021. - Cham : Springer Nature. ; , s. 463-478
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Konferensbidrag (refereegranskat)abstract
- Nowadays, live video streaming events have become a mainstay in viewer's communication in large international enterprises. Provided that viewers are distributed worldwide, the main challenge resides on how to schedule the optimal event's time so as to improve both the viewer's engagement and adoption. In this paper we present a multitask deep reinforcement learning model to select the time of a live video streaming event, aiming to optimize the viewer's engagement and adoption at the same time. We consider the engagement and adoption of the viewers as independent tasks and formulate a unified loss function to learn a common policy. In addition, we account for the fact that each task might have different contribution to the training strategy of the agent. Therefore, to determine the contribution of each task to the agent's training, we design a Transformer's architecture for the state-action transitions of each task. We evaluate our proposed model on four real-world datasets, generated by the live video streaming events of four large enterprises spanning from January 2019 until March 2021. Our experiments demonstrate the effectiveness of the proposed model when compared with several state-of-the-art strategies. For reproduction purposes, our evaluation datasets and implementation are publicly available at https://github.com/stefanosantaris/merlin.
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| 7. |
- Antaris, Stefanos, 1988-, et al.
(författare)
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Sequence Adaptation via Reinforcement Learning in Recommender Systems
- 2021
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Ingår i: 15Th ACM Conference On Recommender Systems (RECSYS 2021). - New York, NY, USA : Association for Computing Machinery (ACM). ; , s. 714-718
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Konferensbidrag (refereegranskat)abstract
- Accounting for the fact that users have different sequential patterns, the main drawback of state-of-the-art recommendation strategies is that a fixed sequence length of user-item interactions is required as input to train the models. This might limit the recommendation accuracy, as in practice users follow different trends on the sequential recommendations. Hence, baseline strategies might ignore important sequential interactions or add noise to the models with redundant interactions, depending on the variety of users' sequential behaviours. To overcome this problem, in this study we propose the SAR model, which not only learns the sequential patterns but also adjusts the sequence length of user-item interactions in a personalized manner. We first design an actor-critic framework, where the RL agent tries to compute the optimal sequence length as an action, given the user's state representation at a certain time step. In addition, we optimize a joint loss function to align the accuracy of the sequential recommendations with the expected cumulative rewards of the critic network, while at the same time we adapt the sequence length with the actor network in a personalized manner. Our experimental evaluation on four real-world datasets demonstrates the superiority of our proposed model over several baseline approaches. Finally, we make our implementation publicly available at https://github.com/stefanosantaris/sar.
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| 8. |
- Antaris, Stefanos, 1988-, et al.
(författare)
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VStreamDRLS : Dynamic Graph Representation Learning with Self-Attention for Enterprise Distributed Video Streaming Solutions
- 2020
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Ingår i: 2020 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 486-493
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Konferensbidrag (refereegranskat)abstract
- Live video streaming has become a mainstay as a standard communication solution for several enterprises worldwide. To efficiently stream high-quality live video content to a large amount of offices, companies employ distributed video streaming solutions which rely on prior knowledge of the underlying evolving enterprise network. However, such networks are highly complex and dynamic. Hence, to optimally coordinate the live video distribution, the available network capacity between viewers has to be accurately predicted. In this paper we propose a graph representation learning technique on weighted and dynamic graphs to predict the network capacity, that is the weights of connections/links between viewers/nodes. We propose VStreamDRLS, a graph neural network architecture with a self-attention mechanism to capture the evolution of the graph structure of live video streaming events. VStreamDRLS employs the graph convolutional network (GCN) model over the duration of a live video streaming event and introduces a self-attention mechanism to evolve the GCN parameters. In doing so, our model focuses on the GCN weights that are relevant to the evolution of the graph and generate the node representation, accordingly. We evaluate our proposed approach on the link prediction task on two real-world datasets, generated by enterprise live video streaming events. The duration of each event lasted an hour. The experimental results demonstrate the effectiveness of VStreamDRLS when compared with state-of-the-art strategies. Our evaluation datasets and implementation are publicly available at https://github.com/stefanosantaris/vstreamdrls.
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