30,573 research outputs found
An Ensemble Method of Deep Reinforcement Learning for Automated Cryptocurrency Trading
We propose an ensemble method to improve the generalization performance of
trading strategies trained by deep reinforcement learning algorithms in a
highly stochastic environment of intraday cryptocurrency portfolio trading. We
adopt a model selection method that evaluates on multiple validation periods,
and propose a novel mixture distribution policy to effectively ensemble the
selected models. We provide a distributional view of the out-of-sample
performance on granular test periods to demonstrate the robustness of the
strategies in evolving market conditions, and retrain the models periodically
to address non-stationarity of financial data. Our proposed ensemble method
improves the out-of-sample performance compared with the benchmarks of a deep
reinforcement learning strategy and a passive investment strategy
An Integrative Paradigm for Enhanced Stroke Prediction: Synergizing XGBoost and xDeepFM Algorithms
Stroke prediction plays a crucial role in preventing and managing this
debilitating condition. In this study, we address the challenge of stroke
prediction using a comprehensive dataset, and propose an ensemble model that
combines the power of XGBoost and xDeepFM algorithms. Our work aims to improve
upon existing stroke prediction models by achieving higher accuracy and
robustness. Through rigorous experimentation, we validate the effectiveness of
our ensemble model using the AUC metric. Through comparing our findings with
those of other models in the field, we gain valuable insights into the merits
and drawbacks of various approaches. This, in turn, contributes significantly
to the progress of machine learning and deep learning techniques specifically
in the domain of stroke prediction
Towards Robust Neural Networks via Random Self-ensemble
Recent studies have revealed the vulnerability of deep neural networks: A
small adversarial perturbation that is imperceptible to human can easily make a
well-trained deep neural network misclassify. This makes it unsafe to apply
neural networks in security-critical applications. In this paper, we propose a
new defense algorithm called Random Self-Ensemble (RSE) by combining two
important concepts: {\bf randomness} and {\bf ensemble}. To protect a targeted
model, RSE adds random noise layers to the neural network to prevent the strong
gradient-based attacks, and ensembles the prediction over random noises to
stabilize the performance. We show that our algorithm is equivalent to ensemble
an infinite number of noisy models without any additional memory
overhead, and the proposed training procedure based on noisy stochastic
gradient descent can ensure the ensemble model has a good predictive
capability. Our algorithm significantly outperforms previous defense techniques
on real data sets. For instance, on CIFAR-10 with VGG network (which has 92\%
accuracy without any attack), under the strong C\&W attack within a certain
distortion tolerance, the accuracy of unprotected model drops to less than
10\%, the best previous defense technique has accuracy, while our method
still has prediction accuracy under the same level of attack. Finally,
our method is simple and easy to integrate into any neural network.Comment: ECCV 2018 camera read
Combining Pessimism with Optimism for Robust and Efficient Model-Based Deep Reinforcement Learning
In real-world tasks, reinforcement learning (RL) agents frequently encounter
situations that are not present during training time. To ensure reliable
performance, the RL agents need to exhibit robustness against worst-case
situations. The robust RL framework addresses this challenge via a worst-case
optimization between an agent and an adversary. Previous robust RL algorithms
are either sample inefficient, lack robustness guarantees, or do not scale to
large problems. We propose the Robust Hallucinated Upper-Confidence RL
(RH-UCRL) algorithm to provably solve this problem while attaining near-optimal
sample complexity guarantees. RH-UCRL is a model-based reinforcement learning
(MBRL) algorithm that effectively distinguishes between epistemic and aleatoric
uncertainty and efficiently explores both the agent and adversary decision
spaces during policy learning. We scale RH-UCRL to complex tasks via neural
networks ensemble models as well as neural network policies. Experimentally, we
demonstrate that RH-UCRL outperforms other robust deep RL algorithms in a
variety of adversarial environments
RMSE-ELM: Recursive Model based Selective Ensemble of Extreme Learning Machines for Robustness Improvement
Extreme learning machine (ELM) as an emerging branch of shallow networks has
shown its excellent generalization and fast learning speed. However, for
blended data, the robustness of ELM is weak because its weights and biases of
hidden nodes are set randomly. Moreover, the noisy data exert a negative
effect. To solve this problem, a new framework called RMSE-ELM is proposed in
this paper. It is a two-layer recursive model. In the first layer, the
framework trains lots of ELMs in different groups concurrently, then employs
selective ensemble to pick out an optimal set of ELMs in each group, which can
be merged into a large group of ELMs called candidate pool. In the second
layer, selective ensemble is recursively used on candidate pool to acquire the
final ensemble. In the experiments, we apply UCI blended datasets to confirm
the robustness of our new approach in two key aspects (mean square error and
standard deviation). The space complexity of our method is increased to some
degree, but the results have shown that RMSE-ELM significantly improves
robustness with slightly computational time compared with representative
methods (ELM, OP-ELM, GASEN-ELM, GASEN-BP and E-GASEN). It becomes a potential
framework to solve robustness issue of ELM for high-dimensional blended data in
the future.Comment: Accepted for publication in Mathematical Problems in Engineering,
09/22/201
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