Data generation for machine learning interatomic potentials and beyond
The field of data-driven chemistry is undergoing an evolution, driven by innovations in
machine learning models for predicting molecular properties and behavior. Recent strides in …
machine learning models for predicting molecular properties and behavior. Recent strides in …
Active learning strategies for atomic cluster expansion models
The atomic cluster expansion (ACE) was proposed recently as a new class of data-driven
interatomic potentials with a formally complete basis set. Since the development of any …
interatomic potentials with a formally complete basis set. Since the development of any …
Preference elicitation as an optimization problem
The new user coldstart problem arises when a recommender system does not yet have any
information about a user. A common solution to it is to generate a profile by asking the user …
information about a user. A common solution to it is to generate a profile by asking the user …
Reduced-order modeling of deep neural networks
We introduce a new method for speeding up the inference of deep neural networks. It is
somewhat inspired by the reduced-order modeling techniques for dynamical systems. The …
somewhat inspired by the reduced-order modeling techniques for dynamical systems. The …
Cold-start Recommendation by Personalized Embedding Region Elicitation
Rating elicitation is a success element for recommender systems to perform well at cold-
starting, in which the systems need to recommend items to a newly arrived user with no prior …
starting, in which the systems need to recommend items to a newly arrived user with no prior …
On Reducing User Interaction Data for Personalization
Most recommender systems rely on user interaction data for personalization. Usually, the
recommendation quality improves with more data. In this work, we study the quality …
recommendation quality improves with more data. In this work, we study the quality …
Deep rating elicitation for new users in collaborative filtering
Recent recommender systems started to use rating elicitation, which asks new users to rate
a small seed itemset for inferring their preferences, to improve the quality of initial …
a small seed itemset for inferring their preferences, to improve the quality of initial …
Greedy SLIM: A SLIM-Based Approach For Preference Elicitation
C Proissl, A Vatic, H Waldschmidt - arXiv preprint arXiv:2406.06061, 2024 - arxiv.org
Preference elicitation is an active learning approach to tackle the cold-start problem of
recommender systems. Roughly speaking, new users are asked to rate some carefully …
recommender systems. Roughly speaking, new users are asked to rate some carefully …
Low-rank kernel matrix approximation using skeletonized interpolation with endo-or exo-vertices
Z Xu, L Cambier, FH Rouet, P L'Eplatennier… - arXiv preprint arXiv …, 2018 - arxiv.org
The efficient compression of kernel matrices, for instance the off-diagonal blocks of
discretized integral equations, is a crucial step in many algorithms. In this paper, we study …
discretized integral equations, is a crucial step in many algorithms. In this paper, we study …
Weighted least-squares approximation with determinantal point processes and generalized volume sampling
We consider the problem of approximating a function from $ L^ 2$ by an element of a given
$ m $-dimensional space $ V_m $, associated with some feature map $\varphi $, using …
$ m $-dimensional space $ V_m $, associated with some feature map $\varphi $, using …