Inductive Semi-supervised Learning Through Optimal Transport

• URL: http://arxiv.org/abs/2112.07262v1
• Date: Tue, 14 Dec 2021 09:52:01 GMT
• Title: Inductive Semi-supervised Learning Through Optimal Transport
• Authors: Mourad El Hamri, Youn\`es Bennani, Issam Falih
• Abstract summary: The proposed approach, called Optimal Transport Induction (OTI), extends efficiently an optimal transport based transductive algorithm (OTP) to inductive tasks. A series of experiments are conducted on several datasets in order to compare the proposed approach with state-of-the-art methods.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we tackle the inductive semi-supervised learning problem that aims to obtain label predictions for out-of-sample data. The proposed approach, called Optimal Transport Induction (OTI), extends efficiently an optimal transport based transductive algorithm (OTP) to inductive tasks for both binary and multi-class settings. A series of experiments are conducted on several datasets in order to compare the proposed approach with state-of-the-art methods. Experiments demonstrate the effectiveness of our approach. We make our code publicly available (Code is available at: https://github.com/MouradElHamri/OTI).

Related papers

• Rethinking Optimal Transport in Offline Reinforcement Learning [64.56896902186126]
  In offline reinforcement learning, the data is provided by various experts and some of them can be sub-optimal. To extract an efficient policy, it is necessary to emphstitch the best behaviors from the dataset. We present an algorithm that aims to find a policy that maps states to a emphpartial distribution of the best expert actions for each given state.
  arXiv  Detail & Related papers  (2024-10-17T22:36:43Z)
• Denoising Pre-Training and Customized Prompt Learning for Efficient Multi-Behavior Sequential Recommendation [69.60321475454843]
  We propose DPCPL, the first pre-training and prompt-tuning paradigm tailored for Multi-Behavior Sequential Recommendation. In the pre-training stage, we propose a novel Efficient Behavior Miner (EBM) to filter out the noise at multiple time scales. Subsequently, we propose to tune the pre-trained model in a highly efficient manner with the proposed Customized Prompt Learning (CPL) module.
  arXiv  Detail & Related papers  (2024-08-21T06:48:38Z)
• Context-aware Pedestrian Trajectory Prediction with Multimodal Transformer [16.457778420360537]
  We propose a novel solution for predicting future trajectories of pedestrians. Our method uses a multimodal encoder-decoder transformer architecture, which takes as input both pedestrian locations and ego-vehicle speeds. We perform detailed experiments and evaluate our method on two popular datasets, PIE and JAAD.
  arXiv  Detail & Related papers  (2023-07-07T18:21:05Z)
• Learning Optimal Transport Between two Empirical Distributions with Normalizing Flows [12.91637880428221]
  We propose to leverage the flexibility of neural networks to learn an approximate optimal transport map. We show that a particular instance of invertible neural networks, namely the normalizing flows, can be used to approximate the solution of this OT problem.
  arXiv  Detail & Related papers  (2022-07-04T08:08:47Z)
• Label Propagation Through Optimal Transport [0.0]
  We tackle the transductive semi-supervised learning problem that aims to obtain label predictions for the given unlabeled data points. Our proposed approach is based on optimal transport, a mathematical theory that has been successfully used to address various machine learning problems.
  arXiv  Detail & Related papers  (2021-10-01T11:25:55Z)
• High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning [119.91679702854499]
  We introduce a method based on deep metric learning to perform Bayesian optimisation over high-dimensional, structured input spaces. We achieve such an inductive bias using just 1% of the available labelled data. As an empirical contribution, we present state-of-the-art results on real-world high-dimensional black-box optimisation problems.
  arXiv  Detail & Related papers  (2021-06-07T13:35:47Z)
• Divide-and-Conquer for Lane-Aware Diverse Trajectory Prediction [71.97877759413272]
  Trajectory prediction is a safety-critical tool for autonomous vehicles to plan and execute actions. Recent methods have achieved strong performances using Multi-Choice Learning objectives like winner-takes-all (WTA) or best-of-many. Our work addresses two key challenges in trajectory prediction, learning outputs, and better predictions by imposing constraints using driving knowledge.
  arXiv  Detail & Related papers  (2021-04-16T17:58:56Z)
• Incremental Semi-Supervised Learning Through Optimal Transport [0.0]
  We propose a novel approach for the transductive semi-supervised learning, using a complete bipartite edge-weighted graph. The proposed approach uses the regularized optimal transport between empirical measures defined on labelled and unlabelled data points in order to obtain an affinity matrix from the optimal transport plan.
  arXiv  Detail & Related papers  (2021-03-22T15:31:53Z)
• Leveraging the Feature Distribution in Transfer-based Few-Shot Learning [2.922007656878633]
  Few-shot classification is a challenging problem due to the uncertainty caused by using few labelled samples. We propose a novel transfer-based method that builds on two successive steps: 1) preprocessing the feature vectors so that they become closer to Gaussian-like distributions, and 2) leveraging this preprocessing using an optimal-transport inspired algorithm. We prove the ability of the proposed methodology to achieve state-of-the-art accuracy with various datasets, backbone architectures and few-shot settings.
  arXiv  Detail & Related papers  (2020-06-06T07:32:08Z)
• Hierarchical Optimal Transport for Robust Multi-View Learning [97.21355697826345]
  Two assumptions may be questionable in practice, which limits the application of multi-view learning. We propose a hierarchical optimal transport (HOT) method to mitigate the dependency on these two assumptions. The HOT method is applicable to both unsupervised and semi-supervised learning, and experimental results show that it performs robustly on both synthetic and real-world tasks.
  arXiv  Detail & Related papers  (2020-06-04T22:24:45Z)
• Adaptive Learning of the Optimal Batch Size of SGD [52.50880550357175]
  We propose a method capable of learning the optimal batch size adaptively throughout its iterations for strongly convex and smooth functions. Our method does this provably, and in our experiments with synthetic and real data robustly exhibits nearly optimal behaviour. We generalize our method to several new batch strategies not considered in the literature before, including a sampling suitable for distributed implementations.
  arXiv  Detail & Related papers  (2020-05-03T14:28:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.