Combining Task Predictors via Enhancing Joint Predictability
- URL: http://arxiv.org/abs/2007.08012v1
- Date: Wed, 15 Jul 2020 21:58:39 GMT
- Title: Combining Task Predictors via Enhancing Joint Predictability
- Authors: Kwang In Kim, Christian Richardt, Hyung Jin Chang
- Abstract summary: We present a new predictor combination algorithm that improves the target by i) measuring the relevance of references based on their capabilities in predicting the target, and ii) strengthening such estimated relevance.
Our algorithm jointly assesses the relevance of all references by adopting a Bayesian framework.
Based on experiments on seven real-world datasets from visual attribute ranking and multi-class classification scenarios, we demonstrate that our algorithm offers a significant performance gain and broadens the application range of existing predictor combination approaches.
- Score: 53.46348489300652
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Predictor combination aims to improve a (target) predictor of a learning task
based on the (reference) predictors of potentially relevant tasks, without
having access to the internals of individual predictors. We present a new
predictor combination algorithm that improves the target by i) measuring the
relevance of references based on their capabilities in predicting the target,
and ii) strengthening such estimated relevance. Unlike existing predictor
combination approaches that only exploit pairwise relationships between the
target and each reference, and thereby ignore potentially useful dependence
among references, our algorithm jointly assesses the relevance of all
references by adopting a Bayesian framework. This also offers a rigorous way to
automatically select only relevant references. Based on experiments on seven
real-world datasets from visual attribute ranking and multi-class
classification scenarios, we demonstrate that our algorithm offers a
significant performance gain and broadens the application range of existing
predictor combination approaches.
Related papers
- Weighted Aggregation of Conformity Scores for Classification [9.559062601251464]
Conformal prediction is a powerful framework for constructing prediction sets with valid coverage guarantees.
We propose a novel approach that combines multiple score functions to improve the performance of conformal predictors.
arXiv Detail & Related papers (2024-07-14T14:58:03Z) - Top-K Pairwise Ranking: Bridging the Gap Among Ranking-Based Measures for Multi-Label Classification [120.37051160567277]
This paper proposes a novel measure named Top-K Pairwise Ranking (TKPR)
A series of analyses show that TKPR is compatible with existing ranking-based measures.
On the other hand, we establish a sharp generalization bound for the proposed framework based on a novel technique named data-dependent contraction.
arXiv Detail & Related papers (2024-07-09T09:36:37Z) - Unifying Feature and Cost Aggregation with Transformers for Semantic and Visual Correspondence [51.54175067684008]
This paper introduces a Transformer-based integrative feature and cost aggregation network designed for dense matching tasks.
We first show that feature aggregation and cost aggregation exhibit distinct characteristics and reveal the potential for substantial benefits stemming from the judicious use of both aggregation processes.
Our framework is evaluated on standard benchmarks for semantic matching, and also applied to geometric matching, where we show that our approach achieves significant improvements compared to existing methods.
arXiv Detail & Related papers (2024-03-17T07:02:55Z) - Provable Offline Preference-Based Reinforcement Learning [95.00042541409901]
We investigate the problem of offline Preference-based Reinforcement Learning (PbRL) with human feedback.
We consider the general reward setting where the reward can be defined over the whole trajectory.
We introduce a new single-policy concentrability coefficient, which can be upper bounded by the per-trajectory concentrability.
arXiv Detail & Related papers (2023-05-24T07:11:26Z) - Improving Link Prediction in Social Networks Using Local and Global
Features: A Clustering-based Approach [0.0]
We propose an approach based on the combination of first and second group methods to tackle the link prediction problem.
Our two-phase developed method firstly determines new features related to the position and dynamic behavior of nodes.
Then, a subspace clustering algorithm is applied to group social objects based on the computed similarity measures.
arXiv Detail & Related papers (2023-05-17T14:45:02Z) - Parallel Reasoning Network for Human-Object Interaction Detection [53.422076419484945]
We propose a new transformer-based method named Parallel Reasoning Network(PR-Net)
PR-Net constructs two independent predictors for instance-level localization and relation-level understanding.
Our PR-Net has achieved competitive results on HICO-DET and V-COCO benchmarks.
arXiv Detail & Related papers (2023-01-09T17:00:34Z) - Discriminative, Generative and Self-Supervised Approaches for
Target-Agnostic Learning [8.666667951130892]
generative and self-supervised learning models are shown to perform well at the task.
Our derived theorem for the pseudo-likelihood theory also shows that they are related for inferring a joint distribution model.
arXiv Detail & Related papers (2020-11-12T15:03:40Z) - Deep Goal-Oriented Clustering [25.383738675621505]
Clustering and prediction are two primary tasks in the fields of unsupervised and supervised learning.
We introduce Deep Goal-Oriented Clustering (DGC), a probabilistic framework that clusters the data by jointly using supervision via side-information.
We show the effectiveness of our model on a range of datasets by achieving prediction accuracies comparable to the state-of-the-art.
arXiv Detail & Related papers (2020-06-07T20:41:08Z) - Hierarchical forecast reconciliation with machine learning [0.34998703934432673]
This paper proposes a novel hierarchical forecasting approach based on machine learning.
It structurally combines the objectives of improved post-sample empirical forecasting accuracy and coherence.
Our results suggest that the proposed method gives superior point forecasts than existing approaches.
arXiv Detail & Related papers (2020-06-03T04:49:39Z) - Novel Human-Object Interaction Detection via Adversarial Domain
Generalization [103.55143362926388]
We study the problem of novel human-object interaction (HOI) detection, aiming at improving the generalization ability of the model to unseen scenarios.
The challenge mainly stems from the large compositional space of objects and predicates, which leads to the lack of sufficient training data for all the object-predicate combinations.
We propose a unified framework of adversarial domain generalization to learn object-invariant features for predicate prediction.
arXiv Detail & Related papers (2020-05-22T22:02:56Z)
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.