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Publications
A Distributional Analogue to the Successor Representation
This paper contributes a new approach for distributional reinforcement learning which elucidates
a clean separation of transition structure … (voir plus)and reward in the learning process. Analogous to how
the successor representation (SR) describes the expected consequences of behaving according to a
given policy, our distributional successor measure
(SM) describes the distributional consequences of
this behaviour. We formulate the distributional
SM as a distribution over distributions and provide theory connecting it with distributional and
model-based reinforcement learning. Moreover,
we propose an algorithm that learns the distributional SM from data by minimizing a two-level
maximum mean discrepancy. Key to our method
are a number of algorithmic techniques that are
independently valuable for learning generative
models of state. As an illustration of the usefulness of the distributional SM, we show that it
enables zero-shot risk-sensitive policy evaluation
in a way that was not previously possible.
Establishing an accurate model of dynamic systems poses a challenge for complex industrial processes. Due to the ability to handle complex t… (voir plus)asks, modular neural networks (MNN) have been widely applied to industrial process modeling. However, the phenomenon of domain drift caused by operating conditions may lead to a cold start of the model, which affects the performance of MNN. For this reason, a multisource transfer learning-based MNN (MSTL-MNN) is proposed in this study. First, the knowledge-driven transfer learning process is performed with domain similarity evaluation, knowledge extraction, and fusion, aiming to form an initial subnetwork in the target domain. Then, the positive transfer process of effective knowledge can avoid the cold start problem of MNN. Second, during the data-driven fine-tuning process, a regularized self-organizing long short-term memory algorithm is designed to fine-tune the structure and parameters of the initial subnetwork, which can improve the prediction performance of MNN. Meanwhile, relevant theoretical analysis is given to ensure the feasibility of MSTL-MNN. Finally, the effectiveness of the proposed method is confirmed by two benchmark simulations and a real industrial dataset of a municipal solid waste incineration process. Experimental results demonstrate the merits of MSTL-MNN for industrial applications.
2024-05-01
IEEE Transactions on Industrial Informatics (publié)
Ensembling multiple models enhances predictive performance by utilizing the varied learned features of the different models but incurs signi… (voir plus)ficant computational and storage costs. Model fusion, which combines parameters from multiple models into one, aims to mitigate these costs but faces practical challenges due to the complex, non-convex nature of neural network loss landscapes, where learned minima are often separated by high loss barriers. Recent works have explored using permutations to align network features, reducing the loss barrier in parameter space. However, permutations are restrictive since they assume a one-to-one mapping between the different models' neurons exists. We propose a new model merging algorithm, CCA Merge, which is based on Canonical Correlation Analysis and aims to maximize the correlations between linear combinations of the model features. We show that our method of aligning models leads to better performances than past methods when averaging models trained on the same, or differing data splits. We also extend this analysis into the harder many models setting where more than 2 models are merged, and we find that CCA Merge works significantly better in this setting than past methods.
In this work, we investigate the interplay between memorization and learning in the context of \emph{stochastic convex optimization} (SCO). … (voir plus)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
