| Nome |
# |
|
Fast Ranking with Additive Ensembles of Oblivious and Non-Oblivious Regression Trees, file e4239ddd-8fdf-7180-e053-3705fe0a3322
|
443
|
|
A Constraint-based Querying System for Exploratory Pattern Discovery, file e4239ddb-2bbc-7180-e053-3705fe0a3322
|
396
|
|
QuickScorer: A Fast Algorithm to Rank Documents with Additive Ensembles of Regression Trees, file e4239ddd-feee-7180-e053-3705fe0a3322
|
339
|
|
Quality versus efficiency in document scoring with learning-to-rank models, file e4239ddd-8fdd-7180-e053-3705fe0a3322
|
213
|
|
Selective Gradient Boosting for Effective Learning to Rank, file e4239ddd-f998-7180-e053-3705fe0a3322
|
185
|
|
QuickRank: a C++ Suite of Learning to Rank Algorithms, file e4239ddb-9c7c-7180-e053-3705fe0a3322
|
153
|
|
Electoral Predictions with Twitter: A Machine-Learning approach, file e4239ddb-9c7e-7180-e053-3705fe0a3322
|
145
|
|
SEL: A unified algorithm for salient entity linking, file e4239ddd-874f-7180-e053-3705fe0a3322
|
120
|
|
Learning Early Exit Strategies for Additive Ranking Ensembles, file e4239dde-5a8c-7180-e053-3705fe0a3322
|
112
|
|
GAM Forest Explanation, file f063c138-6a53-41cc-b5d9-6de0df6e431c
|
97
|
|
Detecting avoidance behaviors between moving object trajectories, file e4239ddd-feda-7180-e053-3705fe0a3322
|
86
|
|
SPARQL/T: A query language with SPARQL’s syntax for semantic mining of textual complaints, file e4239ddd-40c5-7180-e053-3705fe0a3322
|
80
|
|
Parallel Traversal of Large Ensembles of Decision Trees, file e4239ddd-f827-7180-e053-3705fe0a3322
|
73
|
|
SEL: A unified algorithm for entity linking and saliency detection, file e4239ddd-fef0-7180-e053-3705fe0a3322
|
67
|
|
A Supervised Learning Approach to Protect Client Authentication on the Web, file e4239ddd-f739-7180-e053-3705fe0a3322
|
65
|
|
Query-level Early Exit for Additive Learning-to-Rank Ensembles, file e4239dde-0786-7180-e053-3705fe0a3322
|
60
|
|
Method to rank documents by a computer, using additive ensembles of regression trees and cache optimisation, and search engine using such a method, file e4239dde-6a6c-7180-e053-3705fe0a3322
|
59
|
|
Proceedings of the 20th International Conference on Extending Database Technology, {EDBT} 2017, Venice, Italy, March 21-24, 2017, file e4239ddc-6b0a-7180-e053-3705fe0a3322
|
52
|
|
X-CLEAVER: Learning Ranking Ensembles by Growing and Pruning Trees, file e4239ddd-f6cb-7180-e053-3705fe0a3322
|
52
|
|
Interpretable Ranking Using LambdaMART (Abstract), file 3f2a7d6f-dda4-4933-9136-d42b7bca63db
|
39
|
|
EiFFFeL: Enforcing Fairness in Forests by Flipping Leaves, file e4239dde-987b-7180-e053-3705fe0a3322
|
39
|
|
ILMART: Interpretable Ranking with Constrained LambdaMART, file f4b565cc-971e-4522-8489-faa11735d1c5
|
38
|
|
SOUR: an Outliers Detection Algorithm in Learning to Rank (Abstract), file cca53ca7-bbe9-4118-96e9-a3be3f8b99c4
|
36
|
|
Filtering out Outliers in Learning to Rank, file eaf4c3d1-9339-4dc5-9d1f-060c1e912c93
|
29
|
|
Feature partitioning for robust tree ensembles and their certification in adversarial scenarios, file e4239dde-8e71-7180-e053-3705fe0a3322
|
26
|
|
null, file e4239ddb-4538-7180-e053-3705fe0a3322
|
25
|
|
Treant: training evasion-aware decision trees, file f024f587-edb9-4807-a888-dd634a91bb26
|
18
|
|
Does LambdaMART Do What You Expect?, file 5b76eae3-9dbf-4c34-b329-b0b262047e7e
|
12
|
|
T-Warehouse: Visual OLAP Analysis on Trajectory Data, file e4239ddb-3271-7180-e053-3705fe0a3322
|
8
|
|
A general framework for trajectory data warehousing and visual OLAP, file e4239ddb-353d-7180-e053-3705fe0a3322
|
8
|
|
A Supervised Learning Approach to Protect Client Authentication on the Web, file e4239ddb-9c78-7180-e053-3705fe0a3322
|
7
|
|
QuickScorer: A Fast Algorithm to Rank Documents with Additive Ensembles of Regression Trees, file e4239ddb-9ace-7180-e053-3705fe0a3322
|
6
|
|
Detecting avoidance behaviors between moving object trajectories, file e4239ddb-e2ee-7180-e053-3705fe0a3322
|
6
|
|
Manycore GPU processing of repeated range queries over streams of moving objects observations, file e4239ddc-34f1-7180-e053-3705fe0a3322
|
6
|
|
Can Embeddings Analysis Explain Large Language Model Ranking?, file 59b2b418-5ac1-4a4e-a3cb-c7ae20e6b6ac
|
4
|
|
SEL: A unified algorithm for entity linking and saliency detection, file e4239ddd-f7da-7180-e053-3705fe0a3322
|
4
|
|
LambdaRank Gradients are Incoherent, file 4aa9133c-0ebc-4180-a2d8-e33c7d32459f
|
3
|
|
On the Effect of Low-Ranked Documents: A New Sampling Function for Selective Gradient Boosting, file a027bbaf-89f2-4061-b9be-76046ca75b02
|
3
|
|
Frequent Spatio-Temporal Patterns in Trajectory Data Warehouses, file e4239ddb-2c0b-7180-e053-3705fe0a3322
|
3
|
|
gpuDCI: Exploiting GPUs in Frequent Itemset Mining, file e4239ddb-4ab1-7180-e053-3705fe0a3322
|
3
|
|
Visual Mobility Analysis using T-Warehouse, file e4239ddb-4f59-7180-e053-3705fe0a3322
|
3
|
|
SEL: A unified algorithm for salient entity linking, file e4239ddc-98b4-7180-e053-3705fe0a3322
|
3
|
|
Parallel Traversal of Large Ensembles of Decision Trees, file e4239ddd-c103-7180-e053-3705fe0a3322
|
3
|
|
Quality versus efficiency in document scoring with learning-to-rank models, file e4239ddd-f7dc-7180-e053-3705fe0a3322
|
3
|
|
Approximate Mining of Frequent Patterns on Streams, file e4239ddb-2aa0-7180-e053-3705fe0a3322
|
2
|
|
A new algorithm for gap constrained sequence mining., file e4239ddb-531a-7180-e053-3705fe0a3322
|
2
|
|
Quite a mess in my cookie jar! Leveraging machine learning to protect web authentication, file e4239ddb-6dae-7180-e053-3705fe0a3322
|
2
|
|
Improving clustering quality by automatic text summarization, file e4239ddb-d6f2-7180-e053-3705fe0a3322
|
2
|
|
Manycore GPU processing of repeated range queries over streams of moving objects observations, file e4239ddb-e837-7180-e053-3705fe0a3322
|
2
|
|
Analysing Trajectories of Mobile Users: From Data Warehouses to Recommender Systems, file e4239ddc-6336-7180-e053-3705fe0a3322
|
2
|
|
Selective Gradient Boosting for Effective Learning to Rank, file e4239ddd-f5fb-7180-e053-3705fe0a3322
|
2
|
|
Processing streams of spatial k-NN queries and position updates on manycore GPUs, file e4239dde-2191-7180-e053-3705fe0a3322
|
2
|
|
(Query) History Teaches Everything, Including the Future, file e4239ddb-2bba-7180-e053-3705fe0a3322
|
1
|
|
Similarity Caching in Large-Scale Image Retrieval, file e4239ddb-2feb-7180-e053-3705fe0a3322
|
1
|
|
Warehousing and Mining Streams of Mobile Object Observations, file e4239ddb-3273-7180-e053-3705fe0a3322
|
1
|
|
Approximate Aggregations in Trajectory Data Warehouses, file e4239ddb-6ee1-7180-e053-3705fe0a3322
|
1
|
|
Introduction to Topic 5: Parallel and Distributed Data Management, file e4239ddb-c10c-7180-e053-3705fe0a3322
|
1
|
|
TPRED: A Spatio-temporal location predictor framework, file e4239ddc-0b6e-7180-e053-3705fe0a3322
|
1
|
|
Fast Ranking with Additive Ensembles of Oblivious and Non-Oblivious Regression Trees, file e4239ddd-f629-7180-e053-3705fe0a3322
|
1
|
| Totale |
3.155 |