| 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
|
|
Fast Connected Components Computation in Large Graphs by Vertex Pruning, file e4239dde-0065-7180-e053-3705fe0a3322
|
388
|
|
QuickScorer: A Fast Algorithm to Rank Documents with Additive Ensembles of Regression Trees, file e4239ddd-feee-7180-e053-3705fe0a3322
|
320
|
|
Adult content consumption in online social networks, file e4239ddd-f5b8-7180-e053-3705fe0a3322
|
234
|
|
Quality versus efficiency in document scoring with learning-to-rank models, file e4239ddd-8fdd-7180-e053-3705fe0a3322
|
213
|
|
On the behaviour of deviant communities in online social networks, file e4239ddd-fc25-7180-e053-3705fe0a3322
|
207
|
|
Selective Gradient Boosting for Effective Learning to Rank, file e4239ddd-f998-7180-e053-3705fe0a3322
|
184
|
|
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
|
144
|
|
Boosting learning to rank with user dynamics and continuation methods, file e4239ddd-fcd0-7180-e053-3705fe0a3322
|
134
|
|
SEL: A unified algorithm for salient entity linking, file e4239ddd-874f-7180-e053-3705fe0a3322
|
118
|
|
Learning Early Exit Strategies for Additive Ranking Ensembles, file e4239dde-5a8c-7180-e053-3705fe0a3322
|
112
|
|
GAM Forest Explanation, file f063c138-6a53-41cc-b5d9-6de0df6e431c
|
94
|
|
Parallel Traversal of Large Ensembles of Decision Trees, file e4239ddd-f827-7180-e053-3705fe0a3322
|
70
|
|
SEL: A unified algorithm for entity linking and saliency detection, file e4239ddd-fef0-7180-e053-3705fe0a3322
|
67
|
|
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
|
57
|
|
X-CLEAVER: Learning Ranking Ensembles by Growing and Pruning Trees, file e4239ddd-f6cb-7180-e053-3705fe0a3322
|
52
|
|
Adversarial training of gradient-boosted decision trees, file e4239ddd-701f-7180-e053-3705fe0a3322
|
47
|
|
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
|
|
Interpretable Ranking Using LambdaMART (Abstract), file 3f2a7d6f-dda4-4933-9136-d42b7bca63db
|
35
|
|
SOUR: an Outliers Detection Algorithm in Learning to Rank (Abstract), file cca53ca7-bbe9-4118-96e9-a3be3f8b99c4
|
34
|
|
Filtering out Outliers in Learning to Rank, file eaf4c3d1-9339-4dc5-9d1f-060c1e912c93
|
27
|
|
Feature partitioning for robust tree ensembles and their certification in adversarial scenarios, file e4239dde-8e71-7180-e053-3705fe0a3322
|
26
|
|
The Impact of Negative Samples on Learning to Rank, file e4239ddd-c10d-7180-e053-3705fe0a3322
|
18
|
|
Treant: training evasion-aware decision trees, file f024f587-edb9-4807-a888-dd634a91bb26
|
13
|
|
Does LambdaMART Do What You Expect?, file 5b76eae3-9dbf-4c34-b329-b0b262047e7e
|
10
|
|
QuickScorer: A Fast Algorithm to Rank Documents with Additive Ensembles of Regression Trees, file e4239ddb-9ace-7180-e053-3705fe0a3322
|
6
|
|
Fast Connected Components Computation in Large Graphs by Vertex Pruning, file e4239ddd-c106-7180-e053-3705fe0a3322
|
4
|
|
Boosting learning to rank with user dynamics and continuation methods, file e4239ddd-c109-7180-e053-3705fe0a3322
|
4
|
|
SEL: A unified algorithm for entity linking and saliency detection, file e4239ddd-f7da-7180-e053-3705fe0a3322
|
4
|
|
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
|
|
LambdaRank Gradients are Incoherent, file 4aa9133c-0ebc-4180-a2d8-e33c7d32459f
|
2
|
|
Can Embeddings Analysis Explain Large Language Model Ranking?, file 59b2b418-5ac1-4a4e-a3cb-c7ae20e6b6ac
|
2
|
|
On the Effect of Low-Ranked Documents: A New Sampling Function for Selective Gradient Boosting, file a027bbaf-89f2-4061-b9be-76046ca75b02
|
2
|
|
Selective Gradient Boosting for Effective Learning to Rank, file e4239ddd-f5fb-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
|
|
Adult content consumption in online social networks, file e4239ddd-c59f-7180-e053-3705fe0a3322
|
1
|
|
Fast Ranking with Additive Ensembles of Oblivious and Non-Oblivious Regression Trees, file e4239ddd-f629-7180-e053-3705fe0a3322
|
1
|
| Totale |
3.772 |