Search Results for author:
Found 25 papers, 9 papers with code
Towards Graph Foundation Models for Personalization
In the realm of personalization, integrating diverse information sources such as consumption signals and content-based representations is becoming increasingly critical to build state-of-the-art solutions.
Personalized Audiobook Recommendations at Spotify Through Graph Neural Networks
While promising, this move presents significant challenges for personalized recommendations.
Fast Adaptation with Linearized Neural Networks
The inductive biases of trained neural networks are difficult to understand and, consequently, to adapt to new settings.
Tomographic Auto-Encoder: Unsupervised Bayesian Recovery of Corrupted Data
We propose a new probabilistic method for unsupervised recovery of corrupted data.
Empirical Bayes Transductive Meta-Learning with Synthetic Gradients
The evidence lower bound of the marginal log-likelihood of empirical Bayes decomposes as a sum of local KL divergences between the variational posterior and the true posterior on the query set of each task.
Ranked #12 on
Few-Shot Image Classification
on CIFAR-FS 5-way (1-shot)
Online Constrained Model-based Reinforcement Learning
The environment's dynamics are learned from limited training data and can be reused in new task instances without retraining.
Similarity of Neural Networks with Gradients
A suitable similarity index for comparing learnt neural networks plays an important role in understanding the behaviour of the highly-nonlinear functions, and can provide insights on further theoretical analysis and empirical studies.
ORL: Reinforcement Learning Benchmarks for Online Stochastic Optimization Problems
Reinforcement Learning (RL) has achieved state-of-the-art results in domains such as robotics and games.
Variational Information Distillation for Knowledge Transfer
We further demonstrate the strength of our method on knowledge transfer across heterogeneous network architectures by transferring knowledge from a convolutional neural network (CNN) to a multi-layer perceptron (MLP) on CIFAR-10.
Deep Gaussian Processes for Multi-fidelity Modeling
Multi-fidelity methods are prominently used when cheaply-obtained, but possibly biased and noisy, observations must be effectively combined with limited or expensive true data in order to construct reliable models.
Transferring Knowledge across Learning Processes
Approaches that transfer information contained only in the final parameters of a source model will therefore struggle.
Deep Gaussian Processes with Convolutional Kernels
This has hindered the application of DGPs in computer vision tasks, an area where deep parametric models (i. e. CNNs) have made breakthroughs.
Leveraging Crowdsourcing Data For Deep Active Learning - An Application: Learning Intents in Alexa
Experiments show that our framework can accurately learn annotator expertise, infer true labels, and effectively reduce the amount of annotations in model training as compared to state-of-the-art approaches.
Preferential Bayesian Optmization
We present a new framework for this scenario that we call Preferential Bayesian Optimization (PBO) and that allows to find the optimum of a latent function that can only be queried through pairwise comparisons, so-called duels.
DAC-h3: A Proactive Robot Cognitive Architecture to Acquire and Express Knowledge About the World and the Self
This paper introduces a cognitive architecture for a humanoid robot to engage in a proactive, mixed-initiative exploration and manipulation of its environment, where the initiative can originate from both the human and the robot.
Preferential Bayesian Optimization
Bayesian optimization (BO) has emerged during the last few years as an effective approach to optimizing black-box functions where direct queries of the objective are expensive.
Manifold Alignment Determination: finding correspondences across different data views
We present Manifold Alignment Determination (MAD), an algorithm for learning alignments between data points from multiple views or modalities.
Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis
Inter-battery factor analysis extends this notion to multiple views of the data.
Inverse Reinforcement Learning via Deep Gaussian Process
We propose a new approach to inverse reinforcement learning (IRL) based on the deep Gaussian process (deep GP) model, which is capable of learning complicated reward structures with few demonstrations.
Recurrent Gaussian Processes
We define Recurrent Gaussian Processes (RGP) models, a general family of Bayesian nonparametric models with recurrent GP priors which are able to learn dynamical patterns from sequential data.
Variational Auto-encoded Deep Gaussian Processes
We develop a scalable deep non-parametric generative model by augmenting deep Gaussian processes with a recognition model.
Semi-described and semi-supervised learning with Gaussian processes
In this paper we refer to this task as "semi-described learning".
Gaussian Process Models with Parallelization and GPU acceleration
In this work, we present an extension of Gaussian process (GP) models with sophisticated parallelization and GPU acceleration.
Factorized Topic Models
In this paper we present a modification to a latent topic model, which makes the model exploit supervision to produce a factorized representation of the observed data.
Variational Gaussian Process Dynamical Systems
Our work builds on recent variational approximations for Gaussian process latent variable models to allow for nonlinear dimensionality reduction simultaneously with learning a dynamical prior in the latent space.
