Search Results for author:
Found 11 papers, 3 papers with code
PASTO: Strategic Parameter Optimization in Recommendation Systems -- Probabilistic is Better than Deterministic
In this setting with multiple and constrained goals, this paper discovers that a probabilistic strategic parameter regime can achieve better value compared to the standard regime of finding a single deterministic parameter.
Short Video-based Advertisements Evaluation System: Self-Organizing Learning Approach
In this paper, we proposed a novel end-to-end self-organizing framework for user behavior prediction.
A Simple Approach to Learn Polysemous Word Embeddings
Evaluating these methods is also problematic, as rigorous quantitative evaluations in this space is limited, especially when compared with single-sense embeddings.
Matrix Completion via Factorizing Polynomials
While there has been recent research on incorporating explicit side information in the low-rank matrix factorization setting, often implicit information can be gleaned from the data, via higher-order interactions among entities.
Dynamic Word Embeddings for Evolving Semantic Discovery
Word evolution refers to the changing meanings and associations of words throughout time, as a byproduct of human language evolution.
Node Embedding via Word Embedding for Network Community Discovery
Neural node embeddings have recently emerged as a powerful representation for supervised learning tasks involving graph-structured data.
Necessary and Sufficient Conditions and a Provably Efficient Algorithm for Separable Topic Discovery
We develop necessary and sufficient conditions and a novel provably consistent and efficient algorithm for discovering topics (latent factors) from observations (documents) that are realized from a probabilistic mixture of shared latent factors that have certain properties.
Learning Mixed Membership Mallows Models from Pairwise Comparisons
Our key algorithmic insight for estimation is to establish a statistical connection between M4 and topic models by viewing pairwise comparisons as words, and users as documents.
A Topic Modeling Approach to Ranking
We propose a topic modeling approach to the prediction of preferences in pairwise comparisons.
Sensing-Aware Kernel SVM
We propose a novel approach for designing kernels for support vector machines (SVMs) when the class label is linked to the observation through a latent state and the likelihood function of the observation given the state (the sensing model) is available.
Necessary and Sufficient Conditions for Novel Word Detection in Separable Topic Models
The simplicial condition and other stronger conditions that imply it have recently played a central role in developing polynomial time algorithms with provable asymptotic consistency and sample complexity guarantees for topic estimation in separable topic models.
