Search Results for author:
Found 37 papers, 12 papers with code
Classification Tree-based Active Learning: A Wrapper Approach
A classification tree constructed on an initial set of labeled samples is considered to decompose the space into low-entropy regions.
Exploring Contrastive Learning for Long-Tailed Multi-Label Text Classification
In this paper, we conduct an in-depth study of supervised contrastive learning and its influence on representation in MLTC context.
Pool-Based Active Learning with Proper Topological Regions
Machine learning methods usually rely on large sample size to have good performance, while it is difficult to provide labeled set in many applications.
Deep Learning with Partially Labeled Data for Radio Map Reconstruction
In this paper, we address the problem of Received Signal Strength map reconstruction based on location-dependent radio measurements and utilizing side knowledge about the local region; for example, city plan, terrain height, gateway position.
Generalization bounds for learning under graph-dependence: A survey
Traditional statistical learning theory relies on the assumption that data are identically and independently distributed (i. i. d.).
Learning over No-Preferred and Preferred Sequence of Items for Robust Recommendation (Extended Abstract)
This paper is an extended version of [Burashnikova et al., 2021, arXiv: 2012. 06910], where we proposed a theoretically supported sequential strategy for training a large-scale Recommender System (RS) over implicit feedback, mainly in the form of clicks.
Self-Training: A Survey
Semi-supervised algorithms aim to learn prediction functions from a small set of labeled observations and a large set of unlabeled observations.
Self Semi Supervised Neural Architecture Search for Semantic Segmentation
In this paper, we propose a Neural Architecture Search strategy based on self supervision and semi-supervised learning for the task of semantic segmentation.
Recommender systems: when memory matters
In this paper, we study the effect of long memory in the learnability of a sequential recommender system including users' implicit feedback.
Bilingual Topic Models for Comparable Corpora
However, this assumption is strong for comparable corpora that consist of documents thematically similar to an extent only, which are, in turn, the most commonly available or easy to obtain.
Self-Training of Halfspaces with Generalization Guarantees under Massart Mislabeling Noise Model
The pseudo-labeled examples are then added to the training set, and a new classifier is learned.
A Large Scale Benchmark for Individual Treatment Effect Prediction and Uplift Modeling
Individual Treatment Effect (ITE) prediction is an important area of research in machine learning which aims at explaining and estimating the causal impact of an action at the granular level.
Multi-class Probabilistic Bounds for Self-learning
First, we derive a transductive bound over the risk of the multi-class majority vote classifier.
Self-Learning for Received Signal Strength Map Reconstruction with Neural Architecture Search
In this paper, we present a Neural Network (NN) model based on Neural Architecture Search (NAS) and self-learning for received signal strength (RSS) map reconstruction out of sparse single-snapshot input measurements, in the case where data-augmentation by side deterministic simulations cannot be performed.
Treatment Targeting by AUUC Maximization with Generalization Guarantees
In real life scenarios, when we do not have access to ground-truth individual treatment effect, the capacity of models to do so is generally measured by the Area Under the Uplift Curve (AUUC), a metric that differs from the learning objectives of most of the Individual Treatment Effect (ITE) models.
Learning over no-Preferred and Preferred Sequence of items for Robust Recommendation
In this paper, we propose a theoretically founded sequential strategy for training large-scale Recommender Systems (RS) over implicit feedback, mainly in the form of clicks.
Quels corpus d'entraînement pour l'expansion de requêtes par plongement de mots : application à la recherche de microblogs culturels
We describe here an experimental framework and the results obtained on microblogs retrieval.
Semi-supervised Wrapper Feature Selection by Modeling Imperfect Labels
In this paper, we propose a new wrapper feature selection approach with partially labeled training examples where unlabeled observations are pseudo-labeled using the predictions of an initial classifier trained on the labeled training set.
Biconditional Generative Adversarial Networks for Multiview Learning with Missing Views
In this paper, we present a conditional GAN with two generators and a common discriminator for multiview learning problems where observations have two views, but one of them may be missing for some of the training samples.
Sequential Learning over Implicit Feedback for Robust Large-Scale Recommender Systems
This is to prevent from an abnormal number of clicks over some targeted items, mainly due to bots; or very few user interactions.
Multiview Boosting by Controlling the Diversity and the Accuracy of View-specific Voters
Different experiments on three publicly available datasets show the efficiency of the proposed approach with respect to state-of-art models.
Concurrent Learning of Semantic Relations
Discovering whether words are semantically related and identifying the specific semantic relation that holds between them is of crucial importance for NLP as it is essential for tasks like query expansion in IR.
A Delay-tolerant Proximal-Gradient Algorithm for Distributed Learning
One of the main challenges is then to deal with heterogeneous machines and unreliable communications.
Multiview Learning of Weighted Majority Vote by Bregman Divergence Minimization
We tackle the issue of classifier combinations when observations have multiple views.
Cross-lingual Document Retrieval using Regularized Wasserstein Distance
Many information retrieval algorithms rely on the notion of a good distance that allows to efficiently compare objects of different nature.
CAp 2017 challenge: Twitter Named Entity Recognition
We begin by demonstrating why NER for tweets is a challenging problem especially when the number of entities increases.
Multitask Learning for Fine-Grained Twitter Sentiment Analysis
Traditional sentiment analysis approaches tackle problems like ternary (3-category) and fine-grained (5-category) classification by learning the tasks separately.
An Asynchronous Distributed Framework for Large-scale Learning Based on Parameter Exchanges
In many distributed learning problems, the heterogeneous loading of computing machines may harm the overall performance of synchronous strategies.
Representation Learning and Pairwise Ranking for Implicit Feedback in Recommendation Systems
The learning objective is based on three scenarios of ranking losses that control the ability of the model to maintain the ordering over the items induced from the users' preferences, as well as, the capacity of the dot-product defined in the learned embedded space to produce the ordering.
Aggressive Sampling for Multi-class to Binary Reduction with Applications to Text Classification
We address the problem of multi-class classification in the case where the number of classes is very large.
Rademacher Complexity Bounds for a Penalized Multiclass Semi-Supervised Algorithm
We propose Rademacher complexity bounds for multiclass classifiers trained with a two-step semi-supervised model.
PAC-Bayesian Analysis for a two-step Hierarchical Multiview Learning Approach
We study a two-level multiview learning with more than two views under the PAC-Bayesian framework.
An empirical study on large scale text classification with skip-gram embeddings
We investigate the integration of word embeddings as classification features in the setting of large scale text classification.
TwiSE at SemEval-2016 Task 4: Twitter Sentiment Classification
Specifically, we participated in Task 4, namely "Sentiment Analysis in Twitter" for which we implemented sentiment classification systems for subtasks A, B, C and D. Our approach consists of two steps.
On a Topic Model for Sentences
Probabilistic topic models are generative models that describe the content of documents by discovering the latent topics underlying them.
LSHTC: A Benchmark for Large-Scale Text Classification
LSHTC is a series of challenges which aims to assess the performance of classification systems in large-scale classification in a a large number of classes (up to hundreds of thousands).
Algorithmic Robustness for Learning via $(ε, γ, τ)$-Good Similarity Functions
The notion of metric plays a key role in machine learning problems such as classification, clustering or ranking.
