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Found 30 papers, 5 papers with code
Black-Box Variational Inference as a Parametric Approximation to Langevin Dynamics
Variational inference (VI) and Markov chain Monte Carlo (MCMC) are approximate posterior inference algorithms that are often said to have complementary strengths, with VI being fast but biased and MCMC being slower but asymptotically unbiased.
Multi-Fidelity Residual Neural Processes for Scalable Surrogate Modeling
Multi-fidelity surrogate modeling aims to learn an accurate surrogate at the highest fidelity level by combining data from multiple sources.
Diffusion Models as Constrained Samplers for Optimization with Unknown Constraints
To constrain the optimization process to the data manifold, we reformulate the original optimization problem as a sampling problem from the product of the Boltzmann distribution defined by the objective function and the data distribution learned by the diffusion model.
Learning Granger Causality from Instance-wise Self-attentive Hawkes Processes
In particular, we are interested in discovering instance-level causal structures in an unsupervised manner.
Discovering Mixtures of Structural Causal Models from Time Series Data
In this work, we relax this assumption and perform causal discovery from time series data originating from a mixture of causal models.
Optimization on Pareto sets: On a theory of multi-objective optimization
But as the set of Pareto optimal vectors can be very large, we further consider a more practically significant Pareto-constrained optimization problem, where the goal is to optimize a preference function constrained to the Pareto set.
Langevin Thompson Sampling with Logarithmic Communication: Bandits and Reinforcement Learning
Thompson sampling (TS) is widely used in sequential decision making due to its ease of use and appealing empirical performance.
On the Convergence of Black-Box Variational Inference
We provide the first convergence guarantee for full black-box variational inference (BBVI), also known as Monte Carlo variational inference.
Statistical and Computational Trade-offs in Variational Inference: A Case Study in Inferential Model Selection
Focusing on Gaussian inferential models (or variational approximating families) with diagonal plus low-rank precision matrices, we initiate a theoretical study of the trade-offs in two aspects, Bayesian posterior inference error and frequentist uncertainty quantification error.
Multi-fidelity Hierarchical Neural Processes
MF-HNP is flexible enough to handle non-nested high dimensional data at different fidelity levels with varying input and output dimensions.
Dimension Independent Generalization of DP-SGD for Overparameterized Smooth Convex Optimization
This paper considers the generalization performance of differentially private convex learning.
On Optimal Early Stopping: Over-informative versus Under-informative Parametrization
We demonstrate experimentally that our theoretical results on optimal early stopping time corresponds to the training process of deep neural networks.
On Convergence of Federated Averaging Langevin Dynamics
We develop theoretical guarantees for FA-LD for strongly log-concave distributions with non-i. i. d data and study how the injected noise and the stochastic-gradient noise, the heterogeneity of data, and the varying learning rates affect the convergence.
When is the Convergence Time of Langevin Algorithms Dimension Independent? A Composite Optimization Viewpoint
There has been a surge of works bridging MCMC sampling and optimization, with a specific focus on translating non-asymptotic convergence guarantees for optimization problems into the analysis of Langevin algorithms in MCMC sampling.
Deep Bayesian Active Learning for Accelerating Stochastic Simulation
We propose Interactive Neural Process (INP), a deep Bayesian active learning framework for learning deep surrogate models to accelerate stochastic simulations.
Quantifying Uncertainty in Deep Spatiotemporal Forecasting
Deep learning is gaining increasing popularity for spatiotemporal forecasting.
DeepGLEAM: A hybrid mechanistic and deep learning model for COVID-19 forecasting
We introduce DeepGLEAM, a hybrid model for COVID-19 forecasting.
Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors
Bayesian neural networks (BNNs) demonstrate promising success in improving the robustness and uncertainty quantification of modern deep learning.
On Thompson Sampling with Langevin Algorithms
The resulting approximate Thompson sampling algorithm has logarithmic regret and its computational complexity does not scale with the time horizon of the algorithm.
High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm
We propose a Markov chain Monte Carlo (MCMC) algorithm based on third-order Langevin dynamics for sampling from distributions with log-concave and smooth densities.
Bayesian Robustness: A Nonasymptotic Viewpoint
We study the problem of robustly estimating the posterior distribution for the setting where observed data can be contaminated with potentially adversarial outliers.
Is There an Analog of Nesterov Acceleration for MCMC?
We formulate gradient-based Markov chain Monte Carlo (MCMC) sampling as optimization on the space of probability measures, with Kullback-Leibler (KL) divergence as the objective functional.
Sampling Can Be Faster Than Optimization
Optimization algorithms and Monte Carlo sampling algorithms have provided the computational foundations for the rapid growth in applications of statistical machine learning in recent years.
Stochastic Gradient MCMC for State Space Models
However, inference in SSMs is often computationally prohibitive for long time series.
Deep Mixture of Experts via Shallow Embedding
Larger networks generally have greater representational power at the cost of increased computational complexity.
On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo
We provide convergence guarantees in Wasserstein distance for a variety of variance-reduction methods: SAGA Langevin diffusion, SVRG Langevin diffusion and control-variate underdamped Langevin diffusion.
Estimate exponential memory decay in Hidden Markov Model and its applications
Inference in hidden Markov model has been challenging in terms of scalability due to dependencies in the observation data.
Stochastic Gradient MCMC Methods for Hidden Markov Models
Stochastic gradient MCMC (SG-MCMC) algorithms have proven useful in scaling Bayesian inference to large datasets under an assumption of i. i. d data.
A Complete Recipe for Stochastic Gradient MCMC
That is, any continuous Markov process that provides samples from the target distribution can be written in our framework.
