216 citations · 394 across the 33 of their papers we have counts for
34 papers
From In Silico to In Vitro: Evaluating Molecule Generative Models for Hit Generation
Nagham Osman, Vittorio Lembo, Giovanni Bottegoni +1
Hit identification is a critical yet resource-intensive step in the drug discovery pipeline, traditionally relying on high-throughput screening of large compound libraries. Despite…
Online Network Source Optimization with Graph-Kernel MAB
Laura Toni, Pascal Frossard
We propose Grab-UCB, a graph-kernel multi-arms bandit algorithm to learn online the optimal source placement in large scale networks, such that the reward obtained from a priori un…
MiDi: Mixed Graph and 3D Denoising Diffusion for Molecule Generation
Clement Vignac, Nagham Osman, Laura Toni +1
This work introduces MiDi, a novel diffusion model for jointly generating molecular graphs and their corresponding 3D arrangement of atoms. Unlike existing methods that rely on pre…
Information-theoretic Characterizations of Generalization Error for the Gibbs Algorithm
Gholamali Aminian, Yuheng Bu, Laura Toni +2
Various approaches have been developed to upper bound the generalization error of a supervised learning algorithm. However, existing bounds are often loose and even vacuous when ev…
Learning Algorithm Generalization Error Bounds via Auxiliary Distributions
Gholamali Aminian, Saeed Masiha, Laura Toni +1
Generalization error bounds are essential for comprehending how well machine learning models work. In this work, we suggest a novel method, i.e., the Auxiliary Distribution Method,…
Semi-supervised Batch Learning From Logged Data
Gholamali Aminian, Armin Behnamnia, Roberto Vega +5
Off-policy learning methods are intended to learn a policy from logged data, which includes context, action, and feedback (cost or reward) for each sample point. In this work, we b…
An Information-theoretical Approach to Semi-supervised Learning under Covariate-shift
Gholamali Aminian, Mahed Abroshan, Mohammad Mahdi Khalili +2
A common assumption in semi-supervised learning is that the labeled, unlabeled, and test data are drawn from the same distribution. However, this assumption is not satisfied in man…
Characterizing and Understanding the Generalization Error of Transfer Learning with Gibbs Algorithm
Yuheng Bu, Gholamali Aminian, Laura Toni +2
We provide an information-theoretic analysis of the generalization ability of Gibbs-based transfer learning algorithms by focusing on two popular transfer learning approaches, -…
Characterizing the Generalization Error of Gibbs Algorithm with Symmetrized KL information
Gholamali Aminian, Yuheng Bu, Laura Toni +2
Bounding the generalization error of a supervised learning algorithm is one of the most important problems in learning theory, and various approaches have been developed. However,…
Spatio-temporal Graph-RNN for Point Cloud Prediction
Pedro Gomes, Silvia Rossi, Laura Toni
In this paper, we propose an end-to-end learning network to predict future frames in a point cloud sequence. As main novelty, an initial layer learns topological information of poi…
Information-Theoretic Bounds on the Moments of the Generalization Error of Learning Algorithms
Gholamali Aminian, Laura Toni, Miguel R. D. Rodrigues
Generalization error bounds are critical to understanding the performance of machine learning models. In this work, building upon a new bound of the expected value of an arbitrary…
Jensen-Shannon Information Based Characterization of the Generalization Error of Learning Algorithms
Gholamali Aminian, Laura Toni, Miguel R. D. Rodrigues
Generalization error bounds are critical to understanding the performance of machine learning models. In this work, we propose a new information-theoretic based generalization erro…
Graph signal processing for machine learning: A review and new perspectives
Xiaowen Dong, Dorina Thanou, Laura Toni +2
The effective representation, processing, analysis, and visualization of large-scale structured data, especially those related to complex domains such as networks and graphs, are o…
Differentiable Linear Bandit Algorithm
Kaige Yang, Laura Toni
Upper Confidence Bound (UCB) is arguably the most commonly used method for linear multi-arm bandit problems. While conceptually and computationally simple, this method highly relie…
State2vec: Off-Policy Successor Features Approximators
Sephora Madjiheurem, Laura Toni
A major challenge in reinforcement learning (RL) is the design of agents that are able to generalize across tasks that share common dynamics. A viable solution is meta-reinforcemen…
Laplacian-regularized graph bandits: Algorithms and theoretical analysis
Kaige Yang, Xiaowen Dong, Laura Toni
We consider a stochastic linear bandit problem with multiple users, where the relationship between users is captured by an underlying graph and user preferences are represented as…
Error Analysis on Graph Laplacian Regularized Estimator
Kaige Yang, Xiaowen Dong, Laura Toni
We provide a theoretical analysis of the representation learning problem aimed at learning the latent variables (design matrix) of observations with the knowledge of the co…
Representation Learning on Graphs: A Reinforcement Learning Application
Sephora Madjiheurem, Laura Toni
In this work, we study value function approximation in reinforcement learning (RL) problems with high dimensional state or action spaces via a generalized version of representation…
Spherical clustering of users navigating 360° content
Silvia Rossi, Francesca De Simone, Pascal Frossard +1
In Virtual Reality (VR) applications, understanding how users explore the omnidirectional content is important to optimize content creation, to develop user-centric services, or ev…
Graph-Based Recommendation System
Kaige Yang, Laura Toni
In this work, we study recommendation systems modelled as contextual multi-armed bandit (MAB) problems. We propose a graph-based recommendation system that learns and exploits the…
The Sum-Rate-Distortion Region of Correlated Gauss-Markov Sources
Giuseppe Cocco, Laura Toni
Efficient low-delay video encoders are of fundamental importance to provide timely feedback in remotely controlled platforms such as drones. In order to fully understand the theore…
Adaptive Streaming in Interactive Multiview Video Systems
Xue Zhang, Laura Toni, Pascal Frossard +2
Multiview applications endow final users with the possibility to freely navigate within 3D scenes with minimum-delay. A real feeling of scene navigation is enabled by transmitting…
IRSA Transmission Optimization via Online Learning
Laura Toni, Pascal Frossard
In this work, we propose a new learning framework for optimising transmission strategies when irregular repetition slotted ALOHA (IRSA) MAC protocol is considered. We cast the onli…
Price-based Controller for Quality-Fair HTTP Adaptive Streaming (Extended Version)
Stefano D'Aronco, Laura Toni, Pascal Frossard
HTTP adaptive streaming (HAS) has become the universal technology for video streaming over the Internet. Many HAS system designs aim at sharing the network bandwidth in a rate-fair…