DeepAI AI Chat
Log In Sign Up

Learning Determinantal Point Processes by Sampling Inferred Negatives

02/15/2018
by   Zelda Mariet, et al.
Criteo
MIT
0

Determinantal Point Processes (DPPs) have attracted significant interest from the machine-learning community due to their ability to elegantly and tractably model the delicate balance between quality and diversity of sets. We consider learning DPPs from data, a key task for DPPs; for this task, we introduce a novel optimization problem, Contrastive Estimation (CE), which encodes information about "negative" samples into the basic learning model. CE is grounded in the successful use of negative information in machine-vision and language modeling. Depending on the chosen negative distribution (which may be static or evolve during optimization), CE assumes two different forms, which we analyze theoretically and experimentally. We evaluate our new model on real-world datasets; on a challenging dataset, CE learning delivers a considerable improvement in predictive performance over a DPP learned without using contrastive information.

READ FULL TEXT

page 1

page 2

page 3

page 4

05/30/2019

Learning Nonsymmetric Determinantal Point Processes

Determinantal point processes (DPPs) have attracted substantial attentio...
10/09/2020

Contrastive Learning with Hard Negative Samples

We consider the question: how can you sample good negative examples for ...
04/13/2021

Probing Negative Sampling Strategies to Learn GraphRepresentations via Unsupervised Contrastive Learning

Graph representation learning has long been an important yet challenging...
06/17/2020

Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes

Determinantal point processes (DPPs) have attracted significant attentio...
11/17/2018

Deep Determinantal Point Processes

Determinantal point processes (DPPs) have attracted significant attentio...
06/30/2020

SCE: Scalable Network Embedding from Sparsest Cut

Large-scale network embedding is to learn a latent representation for ea...
01/28/2023

Unbiased and Efficient Self-Supervised Incremental Contrastive Learning

Contrastive Learning (CL) has been proved to be a powerful self-supervis...