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Shali Jiang

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5 papers

1 author row

NeurIPS Conference 2020 Conference Paper

Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees

Shali Jiang
Daniel Jiang
Maximilian Balandat
Brian Karrer
Jacob Gardner
Roman Garnett

Bayesian optimization is a sequential decision making framework for optimizing expensive-to-evaluate black-box functions. Computing a full lookahead policy amounts to solving a highly intractable stochastic dynamic program. Myopic approaches, such as expected improvement, are often adopted in practice, but they ignore the long-term impact of the immediate decision. Existing nonmyopic approaches are mostly heuristic and/or computationally expensive. In this paper, we provide the first efficient implementation of general multi-step lookahead Bayesian optimization, formulated as a sequence of nested optimization problems within a multi-step scenario tree. Instead of solving these problems in a nested way, we equivalently optimize all decision variables in the full tree jointly, in a "one-shot" fashion. Combining this with an efficient method for implementing multi-step Gaussian process "fantasization, " we demonstrate that multi-step expected improvement is computationally tractable and exhibits performance superior to existing methods on a wide range of benchmarks.

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NeurIPS Conference 2019 Conference Paper

Cost Effective Active Search

Shali Jiang
Roman Garnett
Benjamin Moseley

We study a special paradigm of active learning, called cost effective active search, where the goal is to find a given number of positive points from a large unlabeled pool with minimum labeling cost. Most existing methods solve this problem heuristically, and few theoretical results have been established. We adopt a principled Bayesian approach for the first time. We first derive the Bayesian optimal policy and establish a strong hardness result: the optimal policy is hard to approximate, with the best-possible approximation ratio lower bounded by $\Omega(n^{0. 16})$. We then propose an efficient and nonmyopic policy using the negative Poisson binomial distribution. We propose simple and fast approximations for computing its expectation, which serves as an essential role in our proposed policy. We conduct comprehensive experiments on various domains such as drug and materials discovery, and demonstrate that our proposed search procedure is superior to the widely used greedy baseline.

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NeurIPS Conference 2019 Conference Paper

D-VAE: A Variational Autoencoder for Directed Acyclic Graphs

Muhan Zhang
Shali Jiang
Zhicheng Cui
Roman Garnett
Yixin Chen

Graph structured data are abundant in the real world. Among different graph types, directed acyclic graphs (DAGs) are of particular interest to machine learning researchers, as many machine learning models are realized as computations on DAGs, including neural networks and Bayesian networks. In this paper, we study deep generative models for DAGs, and propose a novel DAG variational autoencoder (D-VAE). To encode DAGs into the latent space, we leverage graph neural networks. We propose an asynchronous message passing scheme that allows encoding the computations on DAGs, rather than using existing simultaneous message passing schemes to encode local graph structures. We demonstrate the effectiveness of our proposed DVAE through two tasks: neural architecture search and Bayesian network structure learning. Experiments show that our model not only generates novel and valid DAGs, but also produces a smooth latent space that facilitates searching for DAGs with better performance through Bayesian optimization.

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AAAI Conference 2018 Conference Paper

Beyond Link Prediction: Predicting Hyperlinks in Adjacency Space

Muhan Zhang
Zhicheng Cui
Shali Jiang
Yixin Chen

This paper addresses the hyperlink prediction problem in hypernetworks. Different from the traditional link prediction problem where only pairwise relations are considered as links, our task here is to predict the linkage of multiple nodes, i. e. , hyperlink. Each hyperlink is a set of an arbitrary number of nodes which together form a multiway relationship. Hyperlink prediction is challenging – since the cardinality of a hyperlink is variable, existing classiﬁers based on a ﬁxed number of input features become infeasible. Heuristic methods, such as the common neighbors and Katz index, do not work for hyperlink prediction, since they are restricted to pairwise similarities. In this paper, we formally deﬁne the hyperlink prediction problem, and propose a new algorithm called Coordinated Matrix Minimization (CMM), which alternately performs nonnegative matrix factorization and least square matching in the vertex adjacency space of the hypernetwork, in order to infer a subset of candidate hyperlinks that are most suitable to ﬁll the training hypernetwork. We evaluate CMM on two novel tasks: predicting recipes of Chinese food, and ﬁnding missing reactions of metabolic networks. Experimental results demonstrate the superior performance of our method over many seemingly promising baselines.

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NeurIPS Conference 2018 Conference Paper

Efficient nonmyopic batch active search

Shali Jiang
Gustavo Malkomes
Matthew Abbott
Benjamin Moseley
Roman Garnett

Active search is a learning paradigm for actively identifying as many members of a given class as possible. A critical target scenario is high-throughput screening for scientific discovery, such as drug or materials discovery. In these settings, specialized instruments can often evaluate \emph{multiple} points simultaneously; however, all existing work on active search focuses on sequential acquisition. We bridge this gap, addressing batch active search from both the theoretical and practical perspective. We first derive the Bayesian optimal policy for this problem, then prove a lower bound on the performance gap between sequential and batch optimal policies: the ``cost of parallelization. '' We also propose novel, efficient batch policies inspired by state-of-the-art sequential policies, and develop an aggressive pruning technique that can dramatically speed up computation. We conduct thorough experiments on data from three application domains: a citation network, material science, and drug discovery, testing all proposed policies (14 total) with a wide range of batch sizes. Our results demonstrate that the empirical performance gap matches our theoretical bound, that nonmyopic policies usually significantly outperform myopic alternatives, and that diversity is an important consideration for batch policy design.

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