Matthew D. Hoffman
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Sequential Monte Carlo Learning for Time Series Structure Discovery
Feras Saad
Vikash Mansinghka
Proceedings of the 40th International Conference on Machine Learning (2023), pp. 29473-29489
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This paper presents a new approach to automatically discovering accurate
models of complex time series data. Working within a Bayesian nonparametric
prior over a symbolic space of Gaussian process time series models, we
present a novel structure learning algorithm that integrates sequential
Monte Carlo (SMC) and involutive MCMC for highly effective posterior
inference. Our method can be used both in "online'' settings, where new
data is incorporated sequentially in time, and in "offline'' settings, by
using nested subsets of historical data to anneal the posterior. Empirical
measurements on a variety of real-world time series show that our method
can deliver 10x--100x runtime speedups over previous MCMC and greedy-search
structure learning algorithms for the same model family. We use our method
to perform the first large-scale evaluation of Gaussian process time series
structure learning on a widely used benchmark of 1,428 monthly econometric
datasets, showing that our method discovers sensible models that deliver
more accurate point forecasts and interval forecasts over multiple horizons
as compared to prominent statistical and neural baselines that struggle on
this challenging data.
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Estimating the Changing Infection Rate of COVID-19 Using Bayesian Models of Mobility
Xue Ben
Shawn O'Banion
medRxiv, https://www.medrxiv.org/content/10.1101/2020.08.06.20169664v1.full (2020)
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In order to prepare for and control the continued spread of the COVID-19 pandemic while minimizing its economic impact, the world needs to be able to estimate and predict COVID-19’s spread.
Unfortunately, we cannot directly observe the prevalence or growth rate of COVID-19; these must be inferred using some kind of model.
We propose a hierarchical Bayesian extension to the classic susceptible-exposed-infected-removed (SEIR) compartmental model that adds compartments to account for isolation and death and allows the infection rate to vary as a function of both mobility data collected from mobile phones and a latent time-varying factor that accounts for changes in behavior not captured by mobility data. Since confirmed-case data is unreliable, we infer the model’s parameters conditioned on deaths data. We replace the exponential-waiting-time assumption of classic compartmental models with Erlang distributions, which allows for a more realistic model of the long lag between exposure and death. The mobility data gives us a leading indicator that can quickly detect changes in the pandemic’s local growth rate and forecast changes in death rates weeks ahead of time. This is an analysis of observational data, so any causal interpretations of the model's inferences should be treated as suggestive at best; nonetheless, the model’s inferred relationship between different kinds of trips and the infection rate do suggest some possible hypotheses about what kinds of activities might contribute most to COVID-19’s spread.
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Automatically batching control-intensive programs for modern accelerators
Alexey Radul
Dougal Maclaurin
Third Conference on Systems and Machine Learning, Austin, TX (2020)
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We present a general approach to batching arbitrary computations for
GPU and TPU accelerators. We demonstrate the effectiveness of our
method with orders-of-magnitude speedups on the No U-Turn Sampler
(NUTS), a workhorse algorithm in Bayesian statistics. The central
challenge of batching NUTS and other Markov chain Monte Carlo
algorithms is data-dependent control flow and recursion. We overcome
this by mechanically transforming a single-example implementation into
a form that explicitly tracks the current program point for each batch
member, and only steps forward those in the same place. We present
two different batching algorithms: a simpler, previously published one
that inherits recursion from the host Python, and a more complex,
novel one that implmenents recursion directly and can batch across it.
We implement these batching methods as a general program
transformation on Python source. Both the batching system and the
NUTS implementation presented here are available as part of the
popular TensorFlow Probability software package.
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Underspecification Presents Challenges for Credibility in Modern Machine Learning
Dan Moldovan
Ben Adlam
Babak Alipanahi
Alex Beutel
Christina Chen
Jon Deaton
Shaobo Hou
Neil Houlsby
Ghassen Jerfel
Yian Ma
Akinori Mitani
Andrea Montanari
Christopher Nielsen
Thomas Osborne
Rajiv Raman
Kim Ramasamy
Martin Gamunu Seneviratne
Shannon Sequeira
Harini Suresh
Victor Veitch
Journal of Machine Learning Research (2020)
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ML models often exhibit unexpectedly poor behavior when they are deployed in real-world domains. We identify underspecification as a key reason for these failures. An ML pipeline is underspecified when it can return many predictors with equivalently strong held-out performance in the training domain. Underspecification is common in modern ML pipelines, such as those based on deep learning. Predictors returned by underspecified pipelines are often treated as equivalent based on their training domain performance, but we show here that such predictors can behave very differently in deployment domains. This ambiguity can lead to instability and poor model behavior in practice, and is a distinct failure mode from previously identified issues arising from structural mismatch between training and deployment domains. We show that this problem appears in a wide variety of practical ML pipelines, using examples from computer vision, medical imaging, natural language processing, clinical risk prediction based on electronic health records, and medical genomics. Our results show the need to explicitly account for underspecification in modeling pipelines that are intended for real-world deployment in any domain.
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NeuTra-lizing Bad Geometry in Hamiltonian Monte Carlo Using Neural Transport
Josh Dillon
arXiv preprint (2019)
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Hamiltonian Monte Carlo is a powerful algorithm for sampling from difficult-to-normalize posterior distributions. However, when the geometry of the posterior is unfavorable, it may take many expensive evaluations of the target distribution and its gradient to converge and mix. We propose neural transport (NeuTra) HMC, a technique for learning to correct this sort of unfavorable geometry using inverse autoregressive flows (IAF), a powerful neural variational inference technique. The IAF is trained to minimize the KL divergence from an isotropic Gaussian to the warped posterior, and then HMC sampling is performed in the warped space. We evaluate NeuTra HMC on a variety of synthetic and real problems, and find that it significantly outperforms vanilla HMC both in time to reach the stationary distribution and asymptotic effective-sample-size rates.
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Music Transformer: Generating Music with Long-Term Structure
Ashish Vaswani
Jakob Uszkoreit
Noam Shazeer
Curtis Hawthorne
Monica Dinculescu
ICLR (2019)
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Music relies heavily on repetition to build structure and meaning. Self-reference occurs on multiple timescales, from motifs to phrases to reusing of entire sections of music, such as in pieces with ABA structure. The Transformer (Vaswani et al., 2017), a sequence model based on self-attention, has achieved compelling results in many generation tasks that require maintaining long-range coherence. This suggests that self-attention might also be well-suited to modeling music. In musical composition and performance, however, relative timing is critically important. Existing approaches for representing relative positional information in the Transformer modulate attention based on pairwise distance (Shaw et al., 2018). This is impractical for long sequences such as musical compositions since their memory complexity for intermediate relative information is quadratic in the sequence length. We propose an algorithm that reduces their intermediate memory requirement to linear in the sequence length. This enables us to demonstrate that a Transformer with our modified relative attention mechanism can generate minute-long compositions (thousands of steps, four times the length modeled in Oore et al., 2018) with compelling structure, generate continuations that coherently elaborate on a given motif, and in a seq2seq setup generate accompaniments conditioned on melodies. We evaluate the Transformer with our relative attention mechanism on two datasets, JSB Chorales and Piano-e-Competition, and obtain state-of-the-art results on the latter.
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Texture synthesis techniques based on matching the Gram matrix of feature activations in neural networks have achieved spectacular success in the image domain. In this paper we extend these techniques to the audio domain. We demonstrate that synthesizing diverse audio textures is challenging, and argue that this is because audio data is relatively low-dimensional. We therefore introduce two new terms to the original Grammian loss: an autocorrelation term that preserves rhythm, and a diversity term that encourages the optimization procedure to synthesize unique textures. We quantitatively study the impact of our design choices on the quality of the synthesized audio by introducing an audio analogue to the Inception loss which we term the VGGish loss. We show that there is a trade-off between the diversity and quality of the synthesized audio using this technique. Finally we perform a number of experiments to qualitatively study how these design choices impact the quality of the synthesized audio.
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In this paper we highlight the risks of relying on
mean-field variational inference to learn models
that are used as simulators for decision making.
We study the role of accurate inference for latent
variable models in terms of cumulative reward
performance. We show how naive mean-field
variational inference at test time can lead to poor
decisions in basic but fundamental quadratic control problems with continuous actions, as relevant
correlations in the latent space are ignored. We
then extend these examples to a more complex
non-linear scenario with asymmetric costs, where
regret is even more significant.
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Deriving conditional and marginal distributions using conjugacy relationships can be time consuming and error prone. In this paper, we propose a strategy for automating such derivations. Unlike previous systems which focus on relationships between pairs of random variables, our system (which we call Autoconj) operates directly on Python functions that compute log-joint distribution functions. Autoconj provides support for conjugacy-exploiting algorithms in any Python-embedded PPL. This paves the way for accelerating development of novel inference algorithms and structure-exploiting modeling strategies. The package can be downloaded at https://github.com/google-research/autoconj.
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Preview abstract
Deriving conditional and marginal distributions using conjugacy relationships can be time consuming and error prone. In this paper, we propose a strategy for automating such derivations. Unlike previous systems which focus on relationships between pairs of random variables, our system (which we call Autoconj) operates directly on Python functions that compute log-joint distribution functions. Autoconj provides support for conjugacy-exploiting algorithms in any Python-embedded PPL. This paves the way for accelerating development of novel inference algorithms and structure-exploiting modeling strategies. The package can be downloaded at https://github.com/google-research/autoconj.
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