English
Related papers

Related papers: Learning Choice Functions via Pareto-Embeddings

200 papers

Node embedding methods find latent lower-dimensional representations which are used as features in machine learning models. In the last few years, these methods have become extremely popular as a replacement for manual feature engineering.…

Social and Information Networks · Computer Science 2020-06-01 Christoph Martin , Meike Riebeling

Deep learning methods capable of handling relational data have proliferated over the last years. In contrast to traditional relational learning methods that leverage first-order logic for representing such data, these deep learning methods…

Machine Learning · Computer Science 2020-03-25 Sebastijan Dumancic , Tias Guns , Wannes Meert , Hendrik Blockeel

Top-$N$ sequential recommendation models each user as a sequence of items interacted in the past and aims to predict top-$N$ ranked items that a user will likely interact in a `near future'. The order of interaction implies that sequential…

Information Retrieval · Computer Science 2018-09-21 Jiaxi Tang , Ke Wang

A major factor contributing to the success of modern representation learning is the ease of performing various vector operations. Recently, objects with geometric structures (eg. distributions, complex or hyperbolic vectors, or regions such…

Computation and Language · Computer Science 2021-09-13 Tejas Chheda , Purujit Goyal , Trang Tran , Dhruvesh Patel , Michael Boratko , Shib Sankar Dasgupta , Andrew McCallum

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.…

Machine Learning · Computer Science 2016-03-08 Jialei Wang , Mladen Kolar , Nathan Srebro

Finding specific preference-guided Pareto solutions that represent different trade-offs among multiple objectives is critical yet challenging in multi-objective problems. Existing methods are restrictive in preference definitions and/or…

Machine Learning · Computer Science 2025-03-28 Lisha Chen , AFM Saif , Yanning Shen , Tianyi Chen

We address the challenging problem of deep representation learning--the efficient adaption of a pre-trained deep network to different tasks. Specifically, we propose to explore gradient-based features. These features are gradients of the…

Machine Learning · Computer Science 2020-04-14 Fangzhou Mu , Yingyu Liang , Yin Li

The goal of metric learning is to learn a function that maps samples to a lower-dimensional space where similar samples lie closer than dissimilar ones. Particularly, deep metric learning utilizes neural networks to learn such a mapping.…

Computer Vision and Pattern Recognition · Computer Science 2021-06-14 Jenny Seidenschwarz , Ismail Elezi , Laura Leal-Taixé

High-dimensional representations, such as radial basis function networks or tile coding, are common choices for policy evaluation in reinforcement learning. Learning with such high-dimensional representations, however, can be expensive,…

Machine Learning · Computer Science 2017-08-07 Yangchen Pan , Erfan Sadeqi Azer , Martha White

Manifold learning techniques have become increasingly valuable as data continues to grow in size. By discovering a lower-dimensional representation (embedding) of the structure of a dataset, manifold learning algorithms can substantially…

Neural and Evolutionary Computing · Computer Science 2020-01-31 Andrew Lensen , Mengjie Zhang , Bing Xue

We consider the problem of learning a loss function which, when minimized over a training dataset, yields a model that approximately minimizes a validation error metric. Though learning an optimal loss function is NP-hard, we present an…

Machine Learning · Computer Science 2019-07-02 Matthew Streeter

The amount of information in the form of features and variables avail- able to machine learning algorithms is ever increasing. This can lead to classifiers that are prone to overfitting in high dimensions, high di- mensional models do not…

Machine Learning · Computer Science 2014-02-12 Aaron Karper

Existing architectures for imitation learning using image-to-action policy networks perform poorly when presented with an input image containing multiple instances of the object of interest, especially when the number of expert…

Robotics · Computer Science 2021-01-05 Sagar Gubbi Venkatesh , Raviteja Upadrashta , Shishir Kolathaya , Bharadwaj Amrutur

With an increase of dataset availability, the potential for learning from a variety of data sources has increased. One particular method to improve learning from multiple data sources is to embed the data source during training. This allows…

Computation and Language · Computer Science 2021-12-08 Rob van der Goot , Miryam de Lhoneux

Identifying meaningful relationships between the price movements of financial assets is a challenging but important problem in a variety of financial applications. However with recent research, particularly those using machine learning and…

Statistical Finance · Quantitative Finance 2022-02-21 Rian Dolphin , Barry Smyth , Ruihai Dong

We propose a novel sparse preference learning/ranking algorithm. Our algorithm approximates the true utility function by a weighted sum of basis functions using the squared loss on pairs of data points, and is a generalization of the kernel…

Machine Learning · Statistics 2013-07-04 Evgeni Tsivtsivadze , Tom Heskes

In the context of pose-invariant object recognition and retrieval, we demonstrate that it is possible to achieve significant improvements in performance if both the category-based and the object-identity-based embeddings are learned…

Computer Vision and Pattern Recognition · Computer Science 2024-03-04 Rohan Sarkar , Avinash Kak

Proposing new materials by atom substitution based on periodic table similarity is a conventional strategy of searching for materials with desired property. We introduce a machine learning frame work that promotes this paradigm to be…

Materials Science · Physics 2019-04-19 Lei Gu , Ruqian Wu

Many real-world decision processes are modeled by optimization problems whose defining parameters are unknown and must be inferred from observable data. The Predict-Then-Optimize framework uses machine learning models to predict unknown…

Machine Learning · Computer Science 2023-11-23 James Kotary , Vincenzo Di Vito , Jacob Christopher , Pascal Van Hentenryck , Ferdinando Fioretto

The vast majority of work in self-supervised learning, both theoretical and empirical (though mostly the latter), have largely focused on recovering good features for downstream tasks, with the definition of "good" often being intricately…

Machine Learning · Computer Science 2022-02-21 Bingbin Liu , Daniel Hsu , Pradeep Ravikumar , Andrej Risteski
‹ Prev 1 8 9 10 Next ›