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- Yi-An Ma, Tianqi Chen, Emily B. Fox
- NIPS
- 2015

Many recent Markov chain Monte Carlo (MCMC) samplers leverage continuous dynamics to define a transition kernel that efficiently explores a target distribution. In tandem, a focus has been on devising scalable variants that subsample the data and use stochastic gradients in place of full-data gradients in the dynamic simulations. However, such stochastic… (More)

- Xiaojie Qiu, Andrew John Hill, Jonathan S. Packer, Dejun Lin, Yi-An Ma, Cole Trapnell
- Nature methods
- 2017

Single-cell gene expression studies promise to reveal rare cell types and cryptic states, but the high variability of single-cell RNA-seq measurements frustrates efforts to assay transcriptional differences between cells. We introduce the Census algorithm to convert relative RNA-seq expression levels into relative transcript counts without the need for… (More)

t=1 p(yt|xt), where y = (y1, · · · yT ) denotes the data as real valued vector, and x = (x1, · · ·xT ) as discrete valued vector with xt ∈ {1, · · ·K},∀t. We can directly marginalize out the hidden variables, x, with matrix multiplication as p(y|θ) = 1T P (yT )A · · ·P (y1)A π0, where P (yT ) is a diagonal matrix and Pi,j(yt) = p(yt|xt = i)δi,j; 1T = (1, ·… (More)

- Yi-An Ma, Nicholas J. Foti, Emily B. Fox
- ICML
- 2017

Stochastic gradient MCMC (SG-MCMC) algorithms have proven useful in scaling Bayesian inference to large datasets under an assumption of i.i.d data. We instead develop an SGMCMC algorithm to learn the parameters of hidden Markov models (HMMs) for time-dependent data. There are two challenges to applying SGMCMC in this setting: The latent discrete states, and… (More)

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