Search Results for author:
Found 13 papers, 1 papers with code
Mean estimation in the add-remove model of differential privacy
We propose a new algorithm and show that it is min-max optimal, achieving the best possible constant in the leading term of the mean squared error for all $\epsilon$, and that this constant is the same as the optimal algorithm under the swap model.
Hierarchical Reinforcement Learning for Modeling User Novelty-Seeking Intent in Recommender Systems
Hierarchical structure, on the other hand, exists in a user's novelty-seeking intent, which is manifested as a static and intrinsic user preference for seeking novelty along with a dynamic session-based propensity.
Hierarchical Reinforcement Learning
Recommendation Systems
+1
Prompt Tuning Large Language Models on Personalized Aspect Extraction for Recommendations
For the aspect-based recommendation component, the extracted aspects are concatenated with the usual user and item features used by the recommendation model.
URM4DMU: an user represention model for darknet markets users
Traditional user representation methods mainly rely on modeling the text information of posts and cannot capture the temporal content and the forum interaction of posts.
Latent User Intent Modeling for Sequential Recommenders
We demonstrate the effectiveness of the latent user intent modeling via offline analyses as well as live experiments on a large-scale industrial recommendation platform.
Learning to Augment for Casual User Recommendation
Users who come to recommendation platforms are heterogeneous in activity levels.
Robust Online Correlation Clustering
In correlation clustering we are given a set of points along with recommendations whether each pair of points should be placed in the same cluster or into separate clusters.
Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learning
This presents a multi-dimensional Pareto frontier on (1) the trade-off between group fairness and accuracy with respect to each task, as well as (2) the trade-offs across multiple tasks.
An Objective for Hierarchical Clustering in Euclidean Space and its Connection to Bisecting K-means
The paper builds a theoretical connection between this objective and the bisecting k-means algorithm.
Beyond Point Estimate: Inferring Ensemble Prediction Variation from Neuron Activation Strength in Recommender Systems
Despite deep neural network (DNN)'s impressive prediction performance in various domains, it is well known now that a set of DNN models trained with the same model specification and the same data can produce very different prediction results.
Small Towers Make Big Differences
A delicate balance between multi-task generalization and multi-objective optimization is therefore needed for finding a better trade-off between efficiency and generalization.
Relational Algorithms for k-means Clustering
Few relational algorithms are known and this paper offers techniques for designing relational algorithms as well as characterizing their limitations.
Fair Hierarchical Clustering
As machine learning has become more prevalent, researchers have begun to recognize the necessity of ensuring machine learning systems are fair.
