arXiv research

New adaptive importance samplers improve stability and accuracy.

We propose algorithms for online principal component analysis (PCA) and variance minimization for adaptive settings. Previous literature has focused on upper bounding the static adversarial regret, whose comparator is the optimal fixed action in hindsight. However, static regret is not an appropriate metric when the un…

A Bayesian estimation of a GARCH model is performed for US Dollar/Japanese Yen exchange rate by the Metropolis-Hastings algorithm with a proposal density given by the adaptive construction scheme. In the adaptive construction scheme the proposal density is assumed to take a form of a multivariate Student's t-distributi…

Automation of machine learning model development is increasingly becoming an established research area. While automated model selection and automated data pre-processing have been studied in depth, there is, however, a gap concerning automated model adaptation strategies when multiple strategies are available. Manually…

We propose a method to construct a proposal density for the Metropolis-Hastings algorithm in Markov Chain Monte Carlo (MCMC) simulations of the GARCH model. The proposal density is constructed adaptively by using the data sampled by the MCMC metho d itself. It turns out that autocorrelations between the data generated …

Proposes a new adaptive gradient method based on gradient differences.

A new federated learning method reduces communication costs and improves adaptivity.

Stochastic methods with coordinate-wise adaptive stepsize (such as RMSprop and Adam) have been widely used in training deep neural networks. Despite their fast convergence, they can generalize worse than stochastic gradient descent. In this paper, by revisiting the design of Adagrad, we propose to split the network par…

We introduce a gradient-based learning method to automatically adapt Markov chain Monte Carlo (MCMC) proposal distributions to intractable targets. We define a maximum entropy regularised objective function, referred to as generalised speed measure, which can be robustly optimised over the parameters of the proposal di…

In this paper, we propose a novel adaptive kernel for the radial basis function (RBF) neural networks. The proposed kernel adaptively fuses the Euclidean and cosine distance measures to exploit the reciprocating properties of the two. The proposed framework dynamically adapts the weights of the participating kernels us…

Adaptive linear bandit algorithm with best-of-three-worlds regret bounds.

The present paper proposes generalized Gaussian kernel adaptive filtering, where the kernel parameters are adaptive and data-driven. The Gaussian kernel is parametrized by a center vector and a symmetric positive definite (SPD) precision matrix, which is regarded as a generalization of the scalar width parameter. These…

Adaptive importance sampling is a class of techniques for finding good proposal distributions for importance sampling. Often the proposal distributions are standard probability distributions whose parameters are adapted based on the mismatch between the current proposal and a target distribution. In this work, we prese…

Adapts to estimate functions from noisy ERT data.

This paper proposes a new architecture for speaker adaptation of multi-speaker neural-network speech synthesis systems, in which an unseen speaker's voice can be built using a relatively small amount of speech data without transcriptions. This is sometimes called "unsupervised speaker adaptation". More specifically, we…

An adaptive clustering algorithm learns from evolving data without manual tuning.

Paper proposes adaptive parameter selection for KGD algorithms.

The paper proposes an efficient method for estimating ATEs using adaptive experiments.

Paper proposes a new DR estimator for adaptive experiments with improved performance.

The paper proposes a learning algorithm that improves adaptability and generalization.

Proposes ATM method to improve domain adaptation.

Learn to automatically plug domain-specific modules into a common network.

Improved privacy and utility in machine learning with adaptive differential privacy.

Method generates intermediate domains to align source and target domains.

Paper introduces new PAC-Bayesian bounds for multi-view domain adaptation.

We propose a novel adaptive empirical Bayesian method for sparse deep learning, where the sparsity is ensured via a class of self-adaptive spike-and-slab priors. The proposed method works by alternatively sampling from an adaptive hierarchical posterior distribution using stochastic gradient Markov Chain Monte Carlo (M…

MaxVA improves Adam's step sizes by maximizing gradient variance.

Adaptive variational Bayes framework improves inference adaptively.

Proposes novel losses for fine-grained categorical domain adaptation.

Proposes first method for continuously indexed domain adaptation.

Proposes a method to refine PDE-driven high-dimensional rare-event simulation.

Paper proposes a new method to aggregate multiple sources with different label distributions.

Sequential Monte Carlo (SMC), or particle filtering, is a popular class of methods for sampling from an intractable target distribution using a sequence of simpler intermediate distributions. Like other importance sampling-based methods, performance is critically dependent on the proposal distribution: a bad proposal c…

Adaptive sequential decision making is one of the central challenges in machine learning and artificial intelligence. In such problems, the goal is to design an interactive policy that plans for an action to take, from a finite set of $n$ actions, given some partial observations. It has been shown that in many applicat…

Proposes a graph embedding framework for domain adaptation.

New adaptive SGD algorithms for federated learning over physical channels.

Bayesian approach improves speech recognition with limited speaker data.

Adaptive methods improve gradient descent and proximal gradient for convex optimization.

Adaptive sampling method improves efficiency in complex target distributions.

Adaptive HMC improves sampling efficiency by optimizing mass matrix.

Paper proposes a probabilistic alignment method for domain adaptation.

A new method uses normalizing flows for gradual domain adaptation.

FedDuA adapts global learning rate for federated learning.

Federated learning method improves covariate shift adaptation for missing target values.

Proposes MEDM to balance entropy minimization and diversity maximization for better domain adaptation.

Adaptive designs achieve strong Neyman regret guarantees for ATE estimation.

Proposes a label propagation framework for domain adaptation.

Adaptive gradient methods, which adopt historical gradient information to automatically adjust the learning rate, despite the nice property of fast convergence, have been observed to generalize worse than stochastic gradient descent (SGD) with momentum in training deep neural networks. This leaves how to close the gene…