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07/24/2023
Rethinking Data Distillation: Do Not Overlook Calibration
Neural networks trained on distilled data often produce over-confident o...
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05/06/2023
Analysis of Climate Campaigns on Social Media using Bayesian Model Averaging
Climate change is the defining issue of our time, and we are at a defini...
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03/10/2023
DP-Fast MH: Private, Fast, and Accurate Metropolis-Hastings for Large-Scale Bayesian Inference
Bayesian inference provides a principled framework for learning from com...
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03/10/2023
Long-tailed Classification from a Bayesian-decision-theory Perspective
Long-tailed classification poses a challenge due to its heavy imbalance ...
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02/27/2023
Efficient Informed Proposals for Discrete Distributions via Newton's Series Approximation
Gradients have been exploited in proposal distributions to accelerate th...
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02/18/2023
Calibrating the Rigged Lottery: Making All Tickets Reliable
Although sparse training has been successfully used in various resource-...
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01/09/2023
Balance is Essence: Accelerating Sparse Training via Adaptive Gradient Correction
Despite impressive performance on a wide variety of tasks, deep neural n...
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10/12/2022
Sampling in Constrained Domains with Orthogonal-Space Variational Gradient Descent
Sampling methods, as important inference and learning techniques, are ty...
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06/20/2022
A Langevin-like Sampler for Discrete Distributions
We propose discrete Langevin proposal (DLP), a simple and scalable gradi...
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06/20/2022
Low-Precision Stochastic Gradient Langevin Dynamics
While low-precision optimization has been widely used to accelerate deep...
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07/06/2020
Meta-Learning for Variational Inference
Variational inference (VI) plays an essential role in approximate Bayesi...
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06/20/2020
Asymptotically Optimal Exact Minibatch Metropolis-Hastings
Metropolis-Hastings (MH) is a commonly-used MCMC algorithm, but it can b...
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02/29/2020
AMAGOLD: Amortized Metropolis Adjustment for Efficient Stochastic Gradient MCMC
Stochastic gradient Hamiltonian Monte Carlo (SGHMC) is an efficient meth...
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11/21/2019
Poisson-Minibatching for Gibbs Sampling with Convergence Rate Guarantees
Gibbs sampling is a Markov chain Monte Carlo method that is often used f...
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02/11/2019
