MTLV

Omics data such as RNA gene expression, methylation and micro RNA expression are valuable sources of information for various clinical predictive tasks. For example, predicting survival outcomes, response to drugs, cancer histology type and other patients related information is possible using not only clinical data but molecular data as well. Moreover, using these data sources together, for example in multitask learning, might boost the performance. However, in practice, there are many missing data points which leads to significantly lower patient numbers when analysing full cases, which in our setting refers to all modalities being present. In this paper we investigate how imputing data with missing values using deep learning coupled with multitask learning can help to reach state-of-the-art performance results using combined omics modalities - RNA, micro RNA and methylation. We propose a generalised deep imputation method to impute values where a patient has data for one modality missing. Interestingly, deep imputation by itself outperforms multitask learning in classification and regression tasks across most combinations of modalities. In contrast, when using all available modalities for survival prediction we observe that multitask learning by itself significantly outperforms deep imputation (adjusted p-value of 0.03). Thus, both approaches are complementary when optimising performance for downstream predictive tasks.

Multi-task feature learning has been proposed to improve the generalization performance by learning the shared features among multiple related tasks and it has been successfully applied to many real-world problems in machine learning, data mining, computer vision and bioinformatics. Most existing multi-task feature learning models simply assume a common noise level for all tasks, which may not be the case in real applications. Recently, a Calibrated Multivariate Regression (CMR) model has been proposed, which calibrates different tasks with respect to their noise levels and achieves superior prediction performance over the non-calibrated one. A major challenge is how to solve the CMR model efficiently as it is formulated as a composite optimization problem consisting of two non-smooth terms. In this paper, we propose a variant of the calibrated multi-task feature learning formulation by including a squared norm regularizer. We show that the dual problem of the proposed formulation is a smooth optimization problem with a piecewise sphere constraint. The simplicity of the dual problem enables us to develop fast dual optimization algorithms with low per-iteration cost. We also provide a detailed convergence analysis for the proposed dual optimization algorithm. Empirical studies demonstrate that, the dual optimization algorithm quickly converges and it is much more efficient than the primal optimization algorithm. Moreover, the calibrated multi-task feature learning algorithms with and without the squared norm regularizer achieve similar prediction performance and both outperform the non-calibrated ones. Thus, the proposed variant not only enables us to develop fast optimization algorithms, but also keeps the superior prediction performance of the calibrated multi-task feature learning over the non-calibrated one.

Multi-task learning (MTL) is a machine learning paradigm that improves the performance of each task by exploiting useful information contained in multiple related tasks. However, the relatedness of tasks can be exploited by attackers to launch data poisoning attacks, which has been demonstrated a big threat to single-task learning. In this paper, we provide the first study on the vulnerability of MTL. Specifically, we focus on multi-task relationship learning (MTRL) models, a popular subclass of MTL models where task relationships are quantized and are learned directly from training data. We formulate the problem of computing optimal poisoning attacks on MTRL as a bilevel program that is adaptive to arbitrary choice of target tasks and attacking tasks. We propose an efficient algorithm called PATOM for computing optimal attack strategies. PATOM leverages the optimality conditions of the subproblem of MTRL to compute the implicit gradients of the upper level objective function. Experimental results on real-world datasets show that MTRL models are very sensitive to poisoning attacks and the attacker can significantly degrade the performance of target tasks, by either directly poisoning the target tasks or indirectly poisoning the related tasks exploiting the task relatedness. We also found that the tasks being attacked are always strongly correlated, which provides a clue for defending against such attacks.

ADMET property prediction via multi-task graph learning under adaptive auxiliary task selection

Multi-task feature selection with sparse regularization to extract common and task-specific features

Prompting techniques unlock valuable insights from pre-trained models, leading to a remarkable performance in Natural Language Processing (NLP). Inspired by NLP advancements, various graph prompt methods have emerged to transfer knowledge from pre-trained graph models to diverse downstream graph tasks. However, designing appropriate prompts for different downstream tasks remains a significant challenge due to substantial differences between pre-training graph tasks and downstream graph tasks. Moreover, existing graph prompt methods prevent transferability across different domains as they independently pre-train a model for each dataset. To address these challenges, we propose Graph Pretraining and Task-Specific Prompting (GTSP). To the best of our knowledge, GTSP is the first unified pre-training framework to handle cross-domain graph data and multiple types of graph tasks simultaneously. The key idea of our framework is to utilize similar label features between pre-training tasks and downstream tasks to seamlessly integrate the downstream task prompt into the pretrained model. We then propose a task-specific prompting architecture and design a label similarity matrix as prompt input. With GTSP, the pre-trained model can adapt to various types of downstream graph tasks, effectively avoiding the challenges of forcibly altering graph task forms. Experimental analysis conducted on benchmark datasets demonstrates the superiority of our method.

Multi-task learning (MTL) supports simultaneous training over multiple related tasks and learns the shared representation. While improving the generalization ability of training on a single task, MTL has higher privacy risk than traditional single-task learning because more sensitive information is extracted and learned in a correlated manner. Unfortunately, very few works have attempted to address the privacy risks posed by MTL. In this paper, we first investigate such risk by designing model extraction attack (MEA) and membership inference attack (MIA) in MTL. Then we evaluate the privacy risks on six MTL model architectures and two popular MTL datasets, whose results show that both the number of tasks and the complexity of training data play an important role in the attack performance. Our investigation shows that MTL is more vulnerable than traditional single-task learning under both attacks.

The automatic semantic structuring of scientific text allows for more efficient reading of research articles and is an important indexing step for academic search engines. Sequential sentence classification is an essential structuring task and targets the categorisation of sentences based on their content and context. However, the potential of transfer learning for sentence classification across different scientific domains and text types, such as full papers and abstracts, has not yet been explored in prior work. In this paper, we present a systematic analysis of transfer learning for scientific sequential sentence classification. For this purpose, we derive seven research questions and present several contributions to address them: (1) We suggest a novel uniform deep learning architecture and multi-task learning for cross-domain sequential sentence classification in scientific text. (2) We tailor two transfer learning methods to deal with the given task, namely sequential transfer learning and multi-task learning. (3) We compare the results of the two best models using qualitative examples in a case study. (4) We provide an approach for the semi-automatic identification of semantically related classes across annotation schemes and analyse the results for four annotation schemes. The clusters and underlying semantic vectors are validated using k-means clustering. (5) Our comprehensive experimental results indicate that when using the proposed multi-task learning architecture, models trained on datasets from different scientific domains benefit from one another. Our approach significantly outperforms state of the art on full paper datasets while being on par for datasets consisting of abstracts.

Face recognition with wearable items has been a challenging task in computer vision and involves the problem of identifying humans wearing a face mask. Masked face analysis via multi-task learning could effectively improve performance in many fields of face analysis. In this paper, we propose a unified framework for predicting the age, gender, and emotions of people wearing face masks. We first construct FGNET-MASK, a masked face dataset for the problem. Then, we propose a multi-task deep learning model to tackle the problem. In particular, the multi-task deep learning model takes the data as inputs and shares their weight to yield predictions of age, expression, and gender for the masked face. Through extensive experiments, the proposed framework has been found to provide a better performance than other existing methods.

Pre-trained language models (e.g., BERT) significantly alleviate two traditional challenging problems for Chinese word segmentation (CWS): segmentation ambiguity and out-ofvocabulary (OOV) words. However, such improvements are usually achieved on traditional benchmark datasets and not close to an important goal of CWS: practicability (i.e., low complexity as a standalone task and high beneficiality to downstream tasks). To make a trade-off between traditional evaluation and practicability for CWS, we propose a semisupervised neural method via pseudo labels. The neural method consists of a teacher model and a student model, which distills knowledge from unlabeled data to the student model so as to improve both in-domain and out-ofdomain CWS. Experiments show that our proposed method can not only keep the practicability of the lightweight student model but also improve the performance of segmentation effectively. We also evaluate a range of heterogeneous neural architectures of CWS on downstream Chinese NLP tasks. Results of further experiments demonstrate that our proposed segmenter is reliable and practical as a pre-processing step of the downstream NLP tasks at the minimum cost.

Multi-task learning aims to learn multiple tasks jointly by exploiting their relatedness to improve the generalization performance for each task. Traditionally, to perform multi-task learning, one needs to centralize data from all the tasks to a single machine. However, in many real-world applications, data of different tasks may be geo-distributed over different local machines. Due to heavy communication caused by transmitting the data and the issue of data privacy and security, it is impossible to send data of different task to a master machine to perform multi-task learning. Therefore, in this paper, we propose a distributed multi-task learning framework that simultaneously learns predictive models for each task as well as task relationships between tasks alternatingly in the parameter server paradigm. In our framework, we first offer a general dual form for a family of regularized multi-task relationship learning methods. Subsequently, we propose a communication-efficient primal-dual distributed optimization algorithm to solve the dual problem by carefully designing local subproblems to make the dual problem decomposable. Moreover, we provide a theoretical convergence analysis for the proposed algorithm, which is specific for distributed multi-task relationship learning. We conduct extensive experiments on both synthetic and real-world datasets to evaluate our proposed framework in terms of effectiveness and convergence.

Plan recognition is a key component of player modeling. Player plan recognition focuses on modeling how and when players select goals and formulate action sequences to achieve their goals during gameplay. By occasionally asking players to describe their plans, it is possible to devise robust plan recognition models that jointly reason about player goals and action sequences in coordination with player input. In this work, we present a player plan recognition framework that leverages data from player interactions with a planning support tool embedded in an educational game for middle school science education, CRYSTAL ISLAND. Players are prompted to use the planning tool to describe their goals and planned actions in CRYSTAL ISLAND. We use this data to devise data-driven player plan recognition models using multi-label multi-task learning. Specifically, we compare single-task and multi-task learning approaches for both goal prediction and action sequence prediction. Results indicate that multi-task learning yields significant benefits for action sequence prediction. Additionally, we find that incorporating automated detectors of plan completion in plan recognition models improves predictive performance in both tasks.

Multi-task learning has been established as an important machine learning framework for leveraging shared knowledge among multiple different but related tasks, with the generalization performance of models enhanced. As a promising learning paradigm, multi-task learning has been widely adopted by various real-world applications, such as recommendation systems. Multi-gate Mixture-of-Experts (MMoE), a well-received multi-task learning method in industry, based on the classic and inspiring Mixture-of-Experts (MoE) structure, explicitly models task relationships and learns task-specific functionalities, generating significant improvements. However, in our applications, negative transfer, which confuses considerable existing multi-task learning methods, is still observed to happen to MMoE. In this paper, an in-depth empirical investigation into negative transfer is launched. And it reveals that, incompetent experts, which play fundamental roles under the learning framework of MoE, are the key technique bottleneck. To tackle this dilemma, we propose the Calibrated Mixture of Insightful Experts (CMoIE), with three novel modules (Conflict Resolution, Expert Communication, and Mixture Calibration), customed for multi-task learning. Hence a group of insightful experts are constructed with enhanced diversity, communication and specialization. To validate the proposed method CMoIE, experiments are conducted on three public datasets and one real-world click-through-rate prediction dataset we construct based on traffic logs collected from a large-scale online product recommendation system. Our approach yields best performance across all of these benchmarks, demonstrating the superiority of it.

Multi-task learning (MTL) exhibits improved performance in many problems in reality by utilizing the intrinsic features among multiple related tasks. In this paper, the problem of water quality monitoring is considered as multi-task learning, in which different tasks correspond to changes caused by new environment or different spectrometers. An improved learning model is presented describing the relationship between wavelengths and pollutant concentration as well as capturing the task relationships with a low-rank shared structure. Under the assumption that different tasks share some common wavelengths, an optimization problem is proposed with the predictors affected by these features and their corresponding coefficients that vary in different tasks. An alternating minimization algorithm is proposed to solve this problem. Experimental results demonstrate the effectiveness of the proposed algorithm in application.

WhisPAr: Transferring pre-trained audio models to fine-grained classification via Prompt and Adapter