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Found 31 papers, 8 papers with code
Diffusion Model for Slate Recommendation
Slate recommendation is a technique commonly used on streaming platforms and e-commerce sites to present multiple items together.
In-context Exploration-Exploitation for Reinforcement Learning
In-context learning is a promising approach for online policy learning of offline reinforcement learning (RL) methods, which can be achieved at inference time without gradient optimization.
Automatic Music Playlist Generation via Simulation-based Reinforcement Learning
In this paper, we present a reinforcement learning framework that solves for such limitations by directly optimizing for user satisfaction metrics via the use of a simulated playlist-generation environment.
A Strong Baseline for Batch Imitation Learning
Imitation of expert behaviour is a highly desirable and safe approach to the problem of sequential decision making.
Intrinsic Gaussian Process on Unknown Manifolds with Probabilistic Metrics
We use the the probabilistic metric tensor to simulate Brownian Motion paths on the unknown manifold.
Model Selection for Production System via Automated Online Experiments
A challenge that machine learning practitioners in the industry face is the task of selecting the best model to deploy in production.
Making Differentiable Architecture Search less local
Neural architecture search (NAS) is a recent methodology for automating the design of neural network architectures.
The ELBO of Variational Autoencoders Converges to a Sum of Three Entropies
Here we show that for standard (i. e., Gaussian) VAEs the ELBO converges to a value given by the sum of three entropies: the (negative) entropy of the prior distribution, the expected (negative) entropy of the observable distribution, and the average entropy of the variational distributions (the latter is already part of the ELBO).
Black-box density function estimation using recursive partitioning
We present a novel approach to Bayesian inference and general Bayesian computation that is defined through a sequential decision loop.
Intrinsic Gaussian Processes on Manifolds and Their Accelerations by Symmetry
The introduction of our strip algorithm, tailored for manifolds with extra symmetries, and the ball algorithm, designed for arbitrary manifolds, constitutes our significant contribution.
ProSper -- A Python Library for Probabilistic Sparse Coding with Non-Standard Priors and Superpositions
The library widens the scope of dictionary learning approaches beyond implementations of standard approaches such as ICA, NMF or standard L1 sparse coding.
Modulating Surrogates for Bayesian Optimization
Bayesian optimization (BO) methods often rely on the assumption that the objective function is well-behaved, but in practice, this is seldom true for real-world objectives even if noise-free observations can be collected.
Meta-Surrogate Benchmarking for Hyperparameter Optimization
Despite the recent progress in hyperparameter optimization (HPO), available benchmarks that resemble real-world scenarios consist of a few and very large problem instances that are expensive to solve.
Modular Deep Probabilistic Programming
A probabilistic module consists of a set of random variables with associated probabilistic distributions and dedicated inference methods.
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.
Structured Variationally Auto-encoded Optimization
We tackle the problem of optimizing a black-box objective function defined over a highly-structured input space.
Intrinsic Gaussian processes on complex constrained domains
in-GPs respect the potentially complex boundary or interior conditions as well as the intrinsic geometry of the spaces.
Truncated Variational Sampling for "Black Box" Optimization of Generative Models
We investigate the optimization of two probabilistic generative models with binary latent variables using a novel variational EM approach.
Auto-Differentiating Linear Algebra
Development systems for deep learning (DL), such as Theano, Torch, TensorFlow, or MXNet, are easy-to-use tools for creating complex neural network models.
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.
Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes
Often in machine learning, data are collected as a combination of multiple conditions, e. g., the voice recordings of multiple persons, each labeled with an ID.
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.
Spatio-temporal Gaussian processes modeling of dynamical systems in systems biology
The spatio-temporal field of protein concentration and mRNA expression are reconstructed without explicitly solving the partial differential equation.
Unsupervised Learning with Imbalanced Data via Structure Consolidation Latent Variable Model
Unsupervised learning on imbalanced data is challenging because, when given imbalanced data, current model is often dominated by the major category and ignores the categories with small amount of data.
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.
Batch Bayesian Optimization via Local Penalization
The approach assumes that the function of interest, $f$, is a Lipschitz continuous function.
Spike and Slab Gaussian Process Latent Variable Models
The Gaussian process latent variable model (GP-LVM) is a popular approach to non-linear probabilistic dimensionality reduction.
GP-select: Accelerating EM using adaptive subspace preselection
We propose a nonparametric procedure to achieve fast inference in generative graphical models when the number of latent states is very large.
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.
What Are the Invariant Occlusive Components of Image Patches? A Probabilistic Generative Approach
By far most approaches to unsupervised learning learning of visual features, such as sparse coding or ICA, account for translations by representing the same features at different positions.
