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08/04/2023
Optimization on Pareto sets: On a theory of multi-objective optimization
In multi-objective optimization, a single decision vector must balance t...
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06/15/2023
Langevin Thompson Sampling with Logarithmic Communication: Bandits and Reinforcement Learning
Thompson sampling (TS) is widely used in sequential decision making due ...
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06/09/2023
A Central Limit Theorem for Stochastic Saddle Point Optimization
In this work, we study the Uncertainty Quantification (UQ) of an algorit...
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06/05/2023
Aiming towards the minimizers: fast convergence of SGD for overparametrized problems
Modern machine learning paradigms, such as deep learning, occur in or cl...
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06/10/2022
Multi-fidelity Hierarchical Neural Processes
Science and engineering fields use computer simulation extensively. Thes...
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02/20/2022
On Optimal Early Stopping: Over-informative versus Under-informative Parametrization
Early stopping is a simple and widely used method to prevent over-traini...
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12/09/2021
On Convergence of Federated Averaging Langevin Dynamics
We propose a federated averaging Langevin algorithm (FA-LD) for uncertai...
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06/05/2021
Accelerating Stochastic Simulation with Interactive Neural Processes
Stochastic simulations such as large-scale, spatiotemporal, age-structur...
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05/25/2021
Quantifying Uncertainty in Deep Spatiotemporal Forecasting
Deep learning is gaining increasing popularity for spatiotemporal foreca...
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05/14/2020
Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors
Bayesian neural networks (BNNs) demonstrate promising success in improvi...
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02/23/2020
On Thompson Sampling with Langevin Algorithms
Thompson sampling is a methodology for multi-armed bandit problems that ...
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08/28/2019
High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm
We propose a Markov chain Monte Carlo (MCMC) algorithm based on third-or...
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07/27/2019
Bayesian Robustness: A Nonasymptotic Viewpoint
We study the problem of robustly estimating the posterior distribution f...
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02/04/2019
Is There an Analog of Nesterov Acceleration for MCMC?
We formulate gradient-based Markov chain Monte Carlo (MCMC) sampling as ...
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11/20/2018
Sampling Can Be Faster Than Optimization
Optimization algorithms and Monte Carlo sampling algorithms have provide...
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10/22/2018
Stochastic Gradient MCMC for State Space Models
State space models (SSMs) are a flexible approach to modeling complex ti...
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06/05/2018
Deep Mixture of Experts via Shallow Embedding
Larger networks generally have greater representational power at the cos...
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02/15/2018
On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo
We provide convergence guarantees in Wasserstein distance for a variety ...
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10/17/2017
Estimate exponential memory decay in Hidden Markov Model and its applications
Inference in hidden Markov model has been challenging in terms of scalab...
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06/14/2017
Stochastic Gradient MCMC Methods for Hidden Markov Models
Stochastic gradient MCMC (SG-MCMC) algorithms have proven useful in scal...
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06/15/2015
