Given a publicly available pool of machine learning models constructed for various tasks, when a user plans to build a model for her own machine learning application, is it possible to build upon models in the pool such that the previous efforts on these existing models can be reused rather than starting from scratch? Here, a grand challenge is how to find models that are helpful for the current application, without accessing the raw training data for the models in the pool. In this paper, we present a two-phase framework. In the upload phase, when a model is uploading into the pool, we construct a reduced kernel mean embedding (RKME) as aspecificationfor the model. Then in the deployment phase, the relatedness of the current task and pre-trained models will be measured based on the value of the RKME specification. Theoretical results and extensive experiments validate the effectiveness of our approach.

We study {\it model reusability evaluation} (MRE) for source pre-trained models: evaluating their transfer learning performance to new target tasks. In special, we focus on the setting under which the target training datasets are small, making it difficult to produce reliable MRE scores using them. Under this situation, we propose {\it synergistic learning} for building the task-model metric, which can be realized by collecting a set of pre-trained models and asking a group of data providers to participate. We provide theoretical guarantees to show that the learned task-model metric distances can serve as trustworthy MRE scores, and propose synergistic learning algorithms and models for general learning tasks. Experiments show that the MRE models learned by synergistic learning can generate significantly more reliable MRE scores than existing approaches for small-data transfer learning.

In multi-label learning, each instance is associated with multiple labels, and the crucial task is how to leverage label correlations in building models. The deep forest is a recent deep learning framework based on decision tree ensembles, which has a cascade structure that can do representation learning like deep neural models and does not rely on backpropagation. Though deep forests have been found useful in classification tasks, the potential of applying it into multi-label learning has not been studied. We consider that the layer-by-layer processing structure of the deep forest is appropriate for solving multi-label problems. Therefore we design the Multi-Label Deep Forest (MLDF) method, including two mechanisms: measure-aware feature reuse and measure-aware layer growth. The measure-aware feature reuse mechanism enables MLDF to reuse better representation in the previous layer. The measure-aware layer growth mechanism ensures MLDF gradually increase the model complexity guided by performance measure. MLDF handles two challenging problems at the same time: one is restricting the model complexity to ease the overfitting issue; another is optimizing the performance measure on user's demand since there are many different measures in the multi-label evaluation. Experiments demonstrate that our proposal not only beats the compared methods over six measures on benchmark datasets but also enjoys label correlation discovery and other desired properties in multi-label learning.

In many real tasks, we care about how to make decisions rather than mere predictions on an event, e.g. how to increase the revenue next month instead of merely knowing it will drop. The key is to identify the causal effects on the desired event. It is achievable with do-calculus if the causal structure is known; however, in many real tasks it is not easy to infer the whole causal structure with the observational data. Introducing external interventions is needed to achieve it. In this paper, we study the situation where only the response variable is observable under intervention. We propose a novel approach which is able to cost-effectively identify the causal effects, by an active strategy introducing limited interventions, and thus guide decision-making. Theoretical analysis and empirical studies validate the effectiveness of the proposed approach.

Nowadays, many problems require learning a model from data owned by different participants who are restricted to share their examples due to privacy concerns, which is referred to as multiparty learning in the literature. In conventional multiparty learning, a global model is usually trained from scratch via a communication protocol, ignoring the fact that each party may already have a local model trained on her own dataset. In this paper, we define a multiparty multiclass margin to measure the global behavior of a set of heterogeneous local models, and propose a general learning method called HMR (Heterogeneous Model Reuse) to optimize the margin. Our method reuses local models to approximate a global model, even when data are non-i.i.d distributed among parties, by exchanging few examples under predefined budget. Experiments on synthetic and real-world data covering different multiparty scenarios show the effectiveness of our proposal.

Multi-label classification deals with the problem where each instance is associated with multiple class labels. Because evaluation in multi-label classification is more complicated than single-label setting, a number of performance measures have been proposed. It is noticed that an algorithm usually performs differently on different measures. Therefore, it is important to understand which algorithms perform well on which measure(s) and why. In this paper, we propose a unified margin view to revisit eleven performance measures in multi-label classification. In particular, we define label-wise margin and instance-wise margin, and prove that through maximizing these margins, different corresponding performance measures are to be optimized. Based on the defined margins, a max-margin approach called LIMO is designed and empirical results validate our theoretical findings.