arXiv research

Paper improves conversion prediction models for online advertising.

Click-through rate (CTR) prediction is a critical task in online display advertising. The data involved in CTR prediction are typically multi-field categorical data, i.e., every feature is categorical and belongs to one and only one field. One of the interesting characteristics of such data is that features from one fi…

Proposes FEFM and DeepFEFM for CTR prediction, outperforming state-of-the-art models.

Determinantal point processes (DPPs) have received significant attention in the recent years as an elegant model for a variety of machine learning tasks, due to their ability to elegantly model set diversity and item quality or popularity. Recent work has shown that DPPs can be effective models for product recommendati…

A new method reduces inference cost for FwFM by allowing it to scale with item fields only.

SOFARI improves inference on multi-task learning latent factors.

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 differen…

New method generates continuous Pareto sets for multi-task learning.

Survey on multi-task learning for deep neural networks.

Motivated by multi-task machine learning with Banach spaces, we propose the notion of vector-valued reproducing kernel Banach spaces (RKBS). Basic properties of the spaces and the associated reproducing kernels are investigated. We also present feature map constructions and several concrete examples of vector-valued RK…

The paper proposes a machine learning method to detect drivers' affective states using physiological signals.

Develops a method for fairness in multi-task learning using Wasserstein barycenters.

A new method classifies multiple correlated data streams simultaneously.

Factorization machines and polynomial networks are supervised polynomial models based on an efficient low-rank decomposition. We extend these models to the multi-output setting, i.e., for learning vector-valued functions, with application to multi-class or multi-task problems. We cast this as the problem of learning a …

We propose methods for distributed graph-based multi-task learning that are based on weighted averaging of messages from other machines. Uniform averaging or diminishing stepsize in these methods would yield consensus (single task) learning. We show how simply skewing the averaging weights or controlling the stepsize a…

We study the problem of distributed multi-task learning with shared representation, where each machine aims to learn a separate, but related, task in an unknown shared low-dimensional subspaces, i.e. when the predictor matrix has low rank. We consider a setting where each task is handled by a different machine, with sa…

Currently there is no validated objective measure of pain. Recent neuroimaging studies have explored the feasibility of using functional near-infrared spectroscopy (fNIRS) to measure alterations in brain function in evoked and ongoing pain. In this study, we applied multi-task machine learning methods to derive a pract…

Unified MTL framework for heterogeneous data integrates shared and task-specific encoders.

New research shows Multi-Task Learning does not behave like Multi-Objective Optimization.

Online field experiments are the gold-standard way of evaluating changes to real-world interactive machine learning systems. Yet our ability to explore complex, multi-dimensional policy spaces - such as those found in recommendation and ranking problems - is often constrained by the limited number of experiments that c…

Agents solving multi-task classification learn disentangled representations.

Small parameterized towers improve multi-task learning efficiency and generalization.

Paper proposes MIM-DRCFR to learn disentangled factors for better treatment effect estimation.

This paper simplifies MTGP derivations for Gaussian processes.

Proposes a multi-task learning model using variational information bottleneck.

New method for high-dimensional manifold-based inference tackles latent responses.

Flexible multi-task learning framework using summary statistics.

Study multi-task learning with low-rank representation in stochastic linear bandits.

Federated learning poses new statistical and systems challenges in training machine learning models over distributed networks of devices. In this work, we show that multi-task learning is naturally suited to handle the statistical challenges of this setting, and propose a novel systems-aware optimization method, MOCHA,…

Research explores the trade-off between multi-task learning and multitasking in deep neural networks.

Humans and animals show remarkable flexibility in adjusting their behaviour when their goals, or rewards in the environment change. While such flexibility is a hallmark of intelligent behaviour, these multi-task scenarios remain an important challenge for machine learning algorithms and neurobiological models alike. We…

Improves shared encoder representations for better multi-task learning performance.

Survey on negative transfer in machine learning.

REFIL learns from imagined sub-group interactions to improve multi-agent reinforcement learning.

Deep learning models predict postoperative complications more accurately than random forests.

The automated recognition of music genres from audio information is a challenging problem, as genre labels are subjective and noisy. Artist labels are less subjective and less noisy, while certain artists may relate more strongly to certain genres. At the same time, at prediction time, it is not guaranteed that artist …

We consider multi-task learning, which simultaneously learns related prediction tasks, to improve generalization performance. We factorize a coefficient matrix as the product of two matrices based on a low-rank assumption. These matrices have sparsities to simultaneously perform variable selection and learn and overlap…

Multi-task clustering (MTC) has attracted a lot of research attentions in machine learning due to its ability in utilizing the relationship among different tasks. Despite the success of traditional MTC models, they are either easy to stuck into local optima, or sensitive to outliers and noisy data. To alleviate these p…

We consider multi-task regression models where observations are assumed to be a linear combination of several latent node and weight functions, all drawn from Gaussian process (GP) priors that allow nonzero covariance between grouped latent functions. We show that when these grouped functions are conditionally independ…

Framework for multi-task learning with semiparametric models and nuisance parameters.

Study finds multi-task learning and pre-training can improve healthcare models on EHR data.

New algorithms minimize regret in multi-task and lifelong linear bandits with shared representation.

Study tackles contamination and heterogeneity in multi-task learning, improving robustness and personalization.

GeoERM learns shared representations on Riemannian manifolds for multi-task learning.

In a variety of application domains the content to be recommended to users is associated with text. This includes research papers, movies with associated plot summaries, news articles, blog posts, etc. Recommendation approaches based on latent factor models can be extended naturally to leverage text by employing an exp…

Improved MTL-LSSVM for better multi-task learning performance.

SMART transfers knowledge across related studies for multi-task learning.

A distributed algorithm for online multi-task learning reduces communication and runtime costs.