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Found 18 papers, 7 papers with code
Differentially Private Gradient Flow based on the Sliced Wasserstein Distance for Non-Parametric Generative Modeling
Safeguarding privacy in sensitive training data is paramount, particularly in the context of generative modeling.
Unifying GANs and Score-Based Diffusion as Generative Particle Models
Particle-based deep generative models, such as gradient flows and score-based diffusion models, have recently gained traction thanks to their striking performance.
Learning from Multiple Sources for Data-to-Text and Text-to-Data
Our model is designed to handle the tasks of D2T and T2D jointly.
Scalable MCMC Sampling for Nonsymmetric Determinantal Point Processes
In this work, we develop a scalable MCMC sampling algorithm for $k$-NDPPs with low-rank kernels, thus enabling runtime that is sublinear in $n$.
Scalable Sampling for Nonsymmetric Determinantal Point Processes
However, existing work leaves open the question of scalable NDPP sampling.
Combining Reward and Rank Signals for Slate Recommendation
In this paper, we formulate several Bayesian models that incorporate the reward signal (Reward model), the rank signal (Rank model), or both (Full model), for non-personalized slate recommendation.
Wasserstein Learning of Determinantal Point Processes
Through an evaluation on a real-world dataset, we show that our Wasserstein learning approach provides significantly improved predictive performance on a generative task compared to DPPs trained using MLE.
Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes
Determinantal point processes (DPPs) have attracted significant attention in machine learning for their ability to model subsets drawn from a large item collection.
Embedding models for recommendation under contextual constraints
For example, applying constraints a posteriori can result in incomplete recommendations or low-quality results for the tail of the distribution (i. e., less popular items).
Learning Nonsymmetric Determinantal Point Processes
Our method imposes a particular decomposition of the nonsymmetric kernel that enables such tractable learning algorithms, which we analyze both theoretically and experimentally.
Partially Mutual Exclusive Softmax for Positive and Unlabeled data
This is often the case for applications such as language modeling, next event prediction and matrix factorization, where many of the potential outcomes are not mutually exclusive, but are more likely to be independent conditionally on the state.
GEVR: An Event Venue Recommendation System for Groups of Mobile Users
In this paper, we present GEVR, the first Group Event Venue Recommendation system that incorporates mobility via individual location traces and context information into a "social-based" group decision model to provide venue recommendations for groups of mobile users.
Deep Determinantal Point Processes
While DPPs have substantial expressive power, they are fundamentally limited by the parameterization of the kernel matrix and their inability to capture nonlinear interactions between items within sets.
Multi-Task Determinantal Point Processes for Recommendation
Determinantal point processes (DPPs) have received significant attention in the recent years as an elegant model for a variety of machine learning tasks, due to their ability to elegantly model set diversity and item quality or popularity.
Adversarial Training of Word2Vec for Basket Completion
In recent years, the Word2Vec model trained with the Negative Sampling loss function has shown state-of-the-art results in a number of machine learning tasks, including language modeling tasks, such as word analogy and word similarity, and in recommendation tasks, through Prod2Vec, an extension that applies to modeling user shopping activity and user preferences.
Learning Determinantal Point Processes by Corrective Negative Sampling
To address this issue, which reduces the quality of the learned model, we introduce a novel optimization problem, Contrastive Estimation (CE), which encodes information about "negative" samples into the basic learning model.
The Bayesian Low-Rank Determinantal Point Process Mixture Model
Determinantal point processes (DPPs) are an elegant model for encoding probabilities over subsets, such as shopping baskets, of a ground set, such as an item catalog.
Low-Rank Factorization of Determinantal Point Processes for Recommendation
In this work we present a new method for learning the DPP kernel from observed data using a low-rank factorization of this kernel.
