SRN – Racing Thompson by Zhou et al.

While browsing the accepted papers list of ICML 2018, I discovered this paper ‘Racing Thompson: an Efficient Algorithm for Thompson Sampling with Non-conjugate Priors‘ by Zhou, Zhu, and Zhuo. Thompson sampling is a popular algorithm for exploration-exploitation tradeoff problems and is also known as Bayesian bandits. I decided to write my Sunday Reading Notes post on this paper because have been interested in the exploration-exploitation tradeoff for a while and explored this topic through Bayesian optimization and my WSDM’19 paper on sequential A/B testing.

Suppose we want to identify the best arm among K arms and we have some prior knowledge about their rewards \mu \sim \pi. Thompson sampling (TS) balances exploring unexplored arms getting rewards from arms already yielding high rewards by choosing the kth arm according to its the posterior probability of being the optimal arm P_{it} = \pi\left( \mu_i = \max_j \mu_j \right). The computational challenge is to compute the probabilities $P_{it}$. Because TS is often used as an online algorithm, efficient calculation of the posterior probabilities is very important. In the conjugate prior case, this calculation is done in O(K). With non-conjugate priors, I have seen in the literature that people using Markov Chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC). The authors recognize this probability as an expectation and propose to use an Importance Sampling (IS) step combined with Gumbel-Max trick, which transforms the sampling problem to an optimization problem, to sample k at time k according to probabilities \pi\left( \mu_i = \max_j \mu_j \right) =  \mathbb{E}_{\mu\sim \pi(\cdot|X(1:t))}\left[\mathbb{I}[\mu_k = \max_j {\mu_j}] \right] = \mathbb{E}_{\mu\sim B_t}\left[\mathbb{I}[\mu_k = \max_j {\mu_j}] \frac{\pi(\mu|X(1:t)}{B_t(\mu}\right].

The benefits of this IS step comes from flexibility to choose $B_t$ at each time step and also the authors leveraged the stopping rule of racing algorithms to deterime the number of IS samples needed to approximate the expectation.

The resulting algorithm, which combines benefits from Importance Sampling, Gumble-Max trick, and the racing algorithm, is proved to be (\delta,\sigma)-PAC, which is asymptotic good in the sense the total variance distance between the true value P_{it} and its estimate converges to zero.

Screen Shot 2018-11-11 at 3.47.24 PM.png

The regret of bandits with (b) Bernoulli bandits & non-conjugate prior and (c) Gaussian bandits & non-conjugate prior. Source: Figure 2 from Zhou et al.

What I find very interesting from the regret analysis section is the fact that the racing TS in this paper can provide much lower regret compared to Thompson sampling and prior-swapping (PS) even though it uses much few particles than SMC and PS. It is not intuitive to me why this should happen. But upon a little further investigation, I found that the priors used for TS and PS & Racing are different in both plots. For ease of implementation, the authors have chosen a conjugate prior for TS. This leaves me wondering what the results would be if we were to use MCMC or SMC with more particles as the baseline for the regret analysis.

References:

  • Zhou, Y., Zhu, J. & Zhuo, J.. (2018). Racing Thompson: an Efficient Algorithm for Thompson Sampling with Non-conjugate Priors. Proceedings of the 35th International Conference on Machine Learning, in PMLR 80:6000-6008

SRN – Scalable Bayesian Inference for Excitatory Point Process Networks by Linderman and Adams

The topic of this week’s SRN is latent network discovery. The paper ‘Scalable bayesian inference for excitatory point process networks‘ by Scott Linderman and Ryan Adams is suggested to me by Gonzalo Mena.

example_hawkes_poisson.png

Hawkes process v.s. Poisson process, by Steven Mores. Source: https://stmorse.github.io/journal/Hawkes-python.html.

 

In a multivariate Hawkes process, there are K individual point processes and events on one constituent process are more likely to happen right after an event from a neighboring process. To put it in other words, events are ‘induce’ subsequent events according to a network describing their interactions. If we imagine a network of neurons, if neuron A and B are connected and when neuron A fires it can excite neuron B and neuron B has a higher chance of firing. When we want to infer connectivity, if neuron B consistently after neuron A fires, we might  infer that there is likely an excitatory connection between them.

In the work by Linderman and Adams, they extend previous works on continuous time Hawkes process and study the discrete time network Hawkes model. This discretization is motivated by computational concerns. In Simma and Jordan (2010), the authors invoke auxiliary variable z_{k,n} that which denotes the origin of the n-th event on process k. So the number of auxiliary variables needed is large for highly active networks, which could be a computational bottleneck. But working in the discrete model assuming a time scale \Delta t, we can ‘bin’ events and ignore interactions within the same bin. This reduction in number of auxiliary variables improves the run time of inference algorithms such as Gibbs sampler.

Screen Shot 2018-11-04 at 4.43.49 PM.png

Comparison of run time per Gibbs sweep. Source: Fig 2 of Linderman and Adams (2015) https://arxiv.org/pdf/1507.03228.pdf

Besides Gibbs sampler, Linderman and Adams also derived stochastic variational inference algorithm for the discrete time network Hawkes model. This work focuses on scaling to long duration T and the problem of scaling with size of network K remains open.

References:

  • Linderman, S. W., & Adams, R. P. (2015). Scalable bayesian inference for excitatory point process networks. arXiv preprint arXiv:1507.03228.
  • Linderman, S., & Adams, R. (2014, January). Discovering latent network structure in point process data. In International Conference on Machine Learning (pp. 1413-1421).
  • Simma, A., & Jordan, M. I. (2012). Modeling events with cascades of Poisson processes. arXiv preprint arXiv:1203.3516.
  • Steven Morse (2017, June). Python class for Hawkes Process. [Blog post] https://stmorse.github.io/journal/Hawkes-python.html.

SRN – Bayesian Calibration of Microsimulation Models by Rutter et al.

The Sunday Reading Notes paper for this week is ‘Bayesian Calibration of Microsimulation Models’ by Carolyn Rutter, Diana Miglioretti and James Savarino. This is a 2009 JASA Applications and Case Studies paper.

According to a 2012 Review paper by Rutter et al,

Microsimulation models (MSMs) for health outcomes simulate individual event histories associated with key components of a disease process; these simulated life histories can be aggregated to estimate population-level effects of treatment on disease outcomes and the comparative effectiveness of treatments. Although MSMs are used to address a wide range of research questions, methodological improvements in MSM approaches have been slowed by the lack of communication among modelers. In addition, there are few resources to guide individuals who may wish to use MSM projections to inform decisions.

In this paper, the authors propose a Bayesian method to calibrate microsimulations models, using Markov Chain Monte Carlo. The case study in this paper is the history of colorectal cancer (CRC). In this paper, the authors assume all CRCs arise from an adenoma and the history of CRC consists of four components: 1) adenoma risk, 2) adenoma growth, 3) transition from adenoma to preclinical cancer and 4) transition from preclinical cancel to clinical cancer. These four components are not observed directly and the calibration data consists of prevalence of adenomas and preclinical cancers and the size and/or number of adenomas from many (independent) studies from different years and about different subpopulations and using different colonoscopy methods.

The authors use \theta to denote MSM parameters and these parameters can be separated from K components with independent priors: \pi(\theta) = \prod_{i=1}^k \pi_i(\theta_i). The calibration data come from N independent sources and each follows some distribution  y_j|\theta \sim f_j(g_j(\theta)) parametrized by some unknown functions of the MSM parameters. The likelihood is therefore f(Y | \theta) = f(Y|g(\theta) )= \prod_{j=1}^n f_j(y_j|g_j(\theta)).

What makes the calibration problem hard is the unknown function g(\theta). Suppose we want to simulate from the posterior distribution of \theta|Y using a Metropolis-Hastings(MH) algorithm, we need to know r(\theta,\theta^*) = \frac{\pi(\theta)f(y|g(\theta)}{\pi(\theta^*)f(y|g(\theta^*)}. But because g is unknown, the MH step cannot be performed.

So the authors propose an approximate MH algorithm that includes a step to estimate $g(\theta)$ for both the current value \theta and the proposed value \theta^*. To me this feels like an ‘EM’ step: simulate m copies of \tilde{y}_{ji} from f_j(y_j| g_j(\theta)) and calculate the MLE \hat{g}_j(\theta). With this approximation, the resulting transition probability function \hat{\alpha}(\theta,\theta^*)for the Metropolis-within-Gibbs step on \theta_i is based on \frac{\pi(\theta_i)}{\pi(\theta_i^*)}\cdot \frac{\prod_{j=1}^N f_j(y_j|\hat{g}_j(\theta^*_i, \theta_{(-i)})}{\prod_{j=1}^n f_j(y_j|\hat{g}_j(\theta))}. In the Appendix, the authors prove that this approximation satisfies the detailed-balanced condition in the limit of m goes to infinity.

I think this paper provides an interesting example of how to incorporate data from multiple sources. As the authors point out,

how closely the model should calibrate to observed data is unclear, especially when calibration data are variable and many provide conflicting interest. […] It depends on how modelers trade-off concerns about possibly overparameterizing and overfitting calibration data relative to the importance of exactly replicating observed or expected results.

 

References:

  • Rutter, C. M., Miglioretti, D. L., & Savarino, J. E. (2009). Bayesian calibration of microsimulation models. Journal of the American Statistical Association104(488), 1338-1350.
  • Rutter, C. M., Zaslavsky, A. M., & Feuer, E. J. (2011). Dynamic microsimulation models for health outcomes: a review. Medical Decision Making31(1), 10-18.

SRN – A Geometric Interpretation of the Metropolis-Hastings Algorithm by Billera and Diaconis

Coming back to the Sunday Reading Notes, this week I discuss the paper ‘A Geometric Interpretation of the Metropolis-Hastings Algorithm’ by Louis J. Billera and Persi Diaconis from Statistical Science. This paper is suggested to me by Joe Blitzstein.

In Section 4 of ‘Informed proposals for local MCMC in discrete spaces’ by Giacomo Zanella (see my SRN Part I and II), Zanella mentions that the Metropolis-Hasting acceptance probability function(APF) \min\left(1,\frac{\pi(y)p(x,y)}{\pi(x)p(y,x)}\right) is not the only APF that makes the resulting kernel \pi-reversible as long as detailed-balance is satisfied. This comes first as a ‘surprise’ to me as I have never seen another APF in practice. But very quickly I realize that this fact was mentioned in both Stat 213 & Stat 220 at Harvard and I have read about it from Section 5.3 – ‘Why Does the Metropolis Algorithm Work?‘ of ‘Monte Carlo Strategies in Scientific Computing‘ by Jun S. Liu. Unfortunately, I did not pay enough attention. Joe suggested this article to me after I posted on Facebook about being upset with not knowing such a basic fact.

In this Billera and Diaconis paper, the authors focus on the finite state space case X and considers the MH kernel as the projection of stochastic matrices (row sums are all 1 and all entries are non-negative, denoted by\mathcal{s}(X)) onto the set of \pi-reversible Markov chains (stochastic matrices that satisfy detailed balance \pi(x)M(x,y) = \pi(y)M(y,x), denoted by R(\pi)). If we introduce a metric on the stochastic matrices: d(K,K') = \sum_{x} \sum_{x\not=y} \pi(x) |K(x,y)-K'(x,y)|.

The key result in this paper is Theorem 1. The authors prove that the Metropolis maps M := M(K)(x,y) = \min\left( K(x,y), \frac{\pi(y}{\pi(x)}K(y,x)\right) minimizes the distance d from the proposal kernel K to R(\pi). This means that M(K) is the unique closest element in R(\pi) that is coordinate-wise smaller than K on its off-diagonal entries. So M is in a sense the closest reversible kernel to the original kernel K.

I think this geometric interpretation offers great intuition about how the MH algorithm works: we start with a kernel K and change it to another kernel with stationary distribution \pi. And the change must occur as follows:

from x, choose y from K(x,y) and decide to accept x or stay at y; this last choice may be stochastic with acceptance probabilty F(x,y) \in [0,1]. This gives the new chain with transition probabilities: K(x,y) F(x,y), x \not =y$. The diagonal entries are changed so that each row sums to 1.

Indeed the above procedure describes how the MH algorithm works. If we insist on \pi-reversibility, we must have 0 \leq 0 \leq \min(1,R(x,y) where R(x,y) =  \frac{\pi(y)K(y,x)}{\pi(x)K(x,y)}. So the MH choice of APF is one that maximizes the chance of moving from x to y. The resulting MH kernel M has the largest spectral gap (1 – second largest eigenvalue) and by Peksun’s theorem must have the minimum asymptotic variance estimating additive functionals.

In Remark 3.2, the authors point out if we consider only APF that are functions of R(x,y), then the function must satisfy g(x) = x g(1/x) which is the characteristic of balancing functions in Zanella’s ‘informed proposals’ paper.

This paper allows me to study Metropolis-Hastings algorithm from another angle and review facts I have neglected in my coursework.

References:

  • Billera, L. J., & Diaconis, P. (2001). A geometric interpretation of the Metropolis-Hastings algorithm. Statistical Science, 335-339.
  • Zanella, G. (2017). Informed proposals for local MCMC in discrete spaces. arXiv preprint arXiv:1711.07424.
  • Liu, J. S. (2008). Monte Carlo strategies in scientific computing. Springer Science & Business Media.

SRN – Informed proposals for local MCMC in discrete spaces by Zanella (Part II)

This is a super delayed Sunday Reading Notes post and today I come back to finish discussing the informed proposals paper by Giacomo Zanella. In Part I of my discussions, I reviewed point-wise informed proposals and locally-informed criterion. In Part II, I hope to focus on asymptotical optimality of locally informed proposals and their simulation studies.

The author uses Peskun ordering to compare efficiency of MH schemes. He deduces that ‘locally-balanced proposals are asymptotically optimal in terms of Peskun ordering’ as dimensionality of the underlying state space increases. Conditions for asymptotic optimality are indeed mild (Proposition 1) for the three illustrative examples: 1) independent binary components, 2) weighted permutation and 3) Ising model.

In Section 4, Zanella points out a connection between the balancing function and acceptance probability function (APF) for MH algorithms, which I find very interesting. He also shows that the optimal proposal for independent binary variables is Barker choice g(t) = \frac{t}{1+t}. The proof goes by finding the limiting continuous-time process of the MH chain and finding the optimal g for the limiting process.

The simulation studies use the illustrative examples: weighted permutations and Ising models. The comparisons are in terms of 1) acceptance rate, 2) number of successful flips per computation time and 3) mixing of some summary statistics. The second criterion concerns the trade-off between computational cost (of calculating the informed proposal) and statistical accuracy (by producing efficient moves). For simple target distributions (such as Uniform), using locally-balanced proposals does not bring much benefits but it achieves a much higher number of flips per unit time for more complicated and ‘rough’ targets. See second row of Figure 1 of the paper.

Screen Shot 2018-10-14 at 5.37.28 PM

I find it interesting that globally-balanced proposals (aka ‘naively-informed’ or g(t) = t) are extremely sensitive to initialization. Looking at the effective sample size (ESS) per time, the chains from GB has much more stable behavior if initialized from stationarity. See GB v.s. GB( station.) in Figure 2. But in Figure 5, this phenomenon does not show up for Ising models: initializing from stationarity does not yield ESS performance comparable to that of LBs.

Screen Shot 2018-10-14 at 5.44.23 PM.png

In the simulation studies section, the author emphasizes the cost-vs-efficiency trade-off, which I find very important. I feel I have ignored this aspect of designing MCMC algorithms and should think more about it in my future studies. The author indicates that ‘computations required to sample from locally-balanced proposals are trivially parallelizable’. This is also something very interesting to me and I hope to learn more about multi-core computations during my PhD.

In his discussions, the author makes reference to Multiple-Try Metropolis. The connection between LB proposals and MT proposals is not entirely obvious to me, but I intuitively agree with the author’s comment that the weight function in MT serves a very similar purpose as the balancing function term g\left(\frac{\pi(y)}{\pi(x)}\right) in LB.

Side note:
From my downloaded version of the paper, the transition kernel of the first equation on Page 1 is wrong, because the Q(x,dy) terms needs to be multiplied by its acceptance probability a(x,y).

References:

  •  Zanella, G. (2017). Informed proposals for local MCMC in discrete spaces. arXiv preprint arXiv:1711.07424.
  • Liu, J. S., Liang, F., & Wong, W. H. (2000). The multiple-try method and local optimization in Metropolis sampling. Journal of the American Statistical Association95(449), 121-134.

SRN – Informed proposals for local MCMC in discrete spaces by Zanella (Part I)

This week I am reading ‘Informed proposals for local MCMC in discrete spaces‘ by Giacomo Zanella. This paper is about designing MCMC algorithms for discrete-values high-dimensional parameters, and the goal is similar to the papers discussed in previous posts (Hamming ball sampler & auxiliary-variable HMC). I decide to split the Sunday Reading Notes on this paper into two parts, because I find many interesting ideas in this paper.

In this paper, Zanella come up with locally-balanced proposals. Suppose \pi(x) is the target density and K_{\sigma}(x,dy) is an uninformed proposal. We assume that as \sigma \to 0 the kernel K_{\sigma}(x,dy) converges to the delta measure. Zanella seeks to modify this uninformed proposal so that it incorporates information about the target \pi and is biased towards areas with higher density. An example of locally-balanced proposals is Q_{\sqrt{\pi}} (x,dy) = \frac{\sqrt{\pi(y) }K_{\sigma}(x,dy)}{(\sqrt{\pi} * K_{\sigma})(x)}. This kernel is reversible with respect to \sqrt{\pi(x)}(\sqrt{\pi} * K_{\sigma})(x), which converges to \pi(x)dx as x \to 0. [Note the normalizing constatn is the convolution \sqrt{\pi(x)}* K_{\sigma} = \int \sqrt{\pi(y)} K_{\sigma}(x,dy)].]

More generally, Zanella considers a class of pointwise informed proposals that has the structure Q_{g,\sigma} = \frac{1}{Z_{g}}\cdot g\left(\frac{\pi(y)}{\pi(x)}\right) K_{\sigma}(x,dy). It is suggested that the function g satisfy g(t) = t g(1/t).

I will save the discussion on locally-balanced proposals and Peskun optimality to Part II. In this part, I want to discuss Section 5: Connection to MALA and gradient-based MCMC. In continuous space, the point-wise informed proposal Q_{g,\sigma} would be infeasible to sample from because of the term g\left(\frac{\pi(y)}{\pi(x)}\right) . If we take a first-order Taylor expansion, we would have Q_{g,\sigma}^{(1)} \propto g \left( \exp ( \nabla  \log \pi(x) (y-x)) \right) K_{\sigma}(x,dy). If we choose g(t) = \sqrt{t} and K_{\sigma}(x,\cdot) =N(x,\sigma^2), this is the MALA proposal.

I find this connection very interesting, although I do not have a good intuition about where this connection comes from. One way to explain it is that gradient-based MCMC in continuous space is using local information to design informed proposals. In the conclusions, the author mentions that this connection should improve robustness of gradient-based MCMC schemes and help with parameter tuning.

References:(x)

  •  Zanella, G. (2017). Informed proposals for local MCMC in discrete spaces. arXiv preprint arXiv:1711.07424.

SRN – Auxiliary-variable Exact HMC samplers for Binary Distributions by Pakman and Paninski

It’s time for Sunday Reading Notes again! This week I am discussing another computational statistics paper: ‘Auxiliary-variable Exact Hamiltonian Monte Carlo Samplers for Binary Distributions’ by Ari Pakman and Liam Paninski from Columbia University. This paper is published at NIPS 2013.

In the previous Hamming Ball sampler SRN post, the algorithm uses data augmentation to sample from discrete distributions. In this week’s paper, the goal is to sample from generic binary distributions with data augmentation into continuous variables.

Let’s say we want to sample from the distribution p(s) defined over s \in \{\pm 1 \}^d given an un-normalized density f(s). The authors propose augmenting with a continuous variable y \in \mathbb{R}^d with joint density p(s,y) = p(s)p(y|s) where p(y|s) is a density we can design but it must satisfy s_i = \mathrm{sgn}(y_i) for all i = 1,\cdots,d. The marginal distribution of $y$ is $p(y) = p(s)p(y|s)$ as a result of this constraint. It turns out that at this point we transformed a d-dimentional binary  problem on s into a d-dimensional continuous problem on y.

To sample from y, the authors suggest using Hamiltonian Monte Carlo, the potential energy is U(y) = - \log p(y) = -\log p(y|s) - log f(s) and the kinetic energy terms is K(q) = <q,q>/2. The HMC sampler involves simulating a trajectory of y that preserves the Hamiltonian H(y,q) = U(y) + K(q) and typically leap-frog simulation is used. With the constraint in p(y|s), the potential function is defined only piece-wise and we need to be careful when the trajectory crosses regions. o this end, the authors insist we choose p(y|s) such that \log p(y|s) is quadratic, so that the trajectory is deterministic and approximations methods are not necessary.

Because U(y) has a jump at y_i = 0, the value of momentum q_i should change when we cross boundaries. This is, in my opinion, the most interesting part of the paper. Suppose at time t_j we have y_j = 0, then a change in trajectory must happen and let’s say the momentum just before and after y_j = 0 are q_j(t_j^-) and q_j(t_j^+). Conservation of energy says we must have q_j^2(t_j^+)/2+ U(y_j = 0, s_j = -1) = q_j^2(t_j^-)/ 2+ U(y_j = 0, s_j = +1) if y_j <0 before the y_j = 0. From this equation, if q_j^2(t_j^+)>0 then we continue the trajectory with q_j(t_j^+) =q_j(t_j^-); however, if q_j^2(t_j^+)<0  then the particle is reflected from a wall at y_j = 0 and the trajectory gets reflected with q_j(t_j^+) = - q_j(t_j^-).

The two augmentations mentioned in this paper are Gaussian augmentation and exponential augmentation. Both results in quadratic log likelihood. The Gaussian augmentation is very interesting because there is a fixed order that each coordinate y_j reaches zero and the successive hits occur at t_j + n \pi. The authors makes an observation that:

Interestingly, the rate at which wall y_j = 0 is crossed coincides with the acceptance rate in a Metropolis algorithm that samples uniformly a value for i and makes a proposal of flipping the binary variable s_i.

To me this is a sanity check rather than a surprise because each coordinate hits the boundary the same number of times and making a decision to continue or to bounce back in y_j is the same as deciding whether we should flip the sign of s_i. But I think the authors give a very help comment pointing out that although the acceptance probability is the same,  the method proposed is still different from Metropolis because

in HMC the order in which the walls are hit is fixed given the initial velocity, and the values of q_i^2 at successive hits of y_i = 0 within the same iteration are not independent.

What’s interesting for the exponential augmentation method is that

particles moves away faster from areas of lower probability.

This is certainly a nice feature to have so that the sample mixes well.

In the simulation examples, the authors compared Gaussian HMC and Metropolis on 1d and 2d ising models and showed that:

  1. ‘the HMC sampler explores faster the samples space once chain has reached equilibrium distribution.’
  2. ‘the HMC sampler is faster in reaching the equilibrium distribution.’

I think the take away from this paper is the continuous data augmentation to sample discrete variables and their dealing with piece-wise defined potential function.

 

Reference:

  • Pakman, A., & Paninski, L. (2013). Auxiliary-variable exact Hamiltonian Monte Carlo samplers for binary distributions. In Advances in neural information processing systems (pp. 2490-2498).
  •  Neal, R. M. (2011). MCMC using Hamiltonian dynamics. Handbook of Markov Chain Monte Carlo2(11), 2.