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Minjie Xu

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5 papers

2 author rows

ICML Conference 2020 Conference Paper

Neural Datalog Through Time: Informed Temporal Modeling via Logical Specification

Hongyuan Mei
Guanghui Qin
Minjie Xu
Jason Eisner

Learning how to predict future events from patterns of past events is difficult when the set of possible event types is large. Training an unrestricted neural model might overfit to spurious patterns. To exploit domain-specific knowledge of how past events might affect an event’s present probability, we propose using a temporal deductive database to track structured facts over time. Rules serve to prove facts from other facts and from past events. Each fact has a time-varying state—a vector computed by a neural net whose topology is determined by the fact’s provenance, including its experience of past events. The possible event types at any time are given by special facts, whose probabilities are neurally modeled alongside their states. In both synthetic and real-world domains, we show that neural probabilistic models derived from concise Datalog programs improve prediction by encoding appropriate domain knowledge in their architecture.

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NeurIPS Conference 2014 Conference Paper

Distributed Bayesian Posterior Sampling via Moment Sharing

Minjie Xu
Balaji Lakshminarayanan
Yee Whye Teh
Jun Zhu
Bo Zhang

We propose a distributed Markov chain Monte Carlo (MCMC) inference algorithm for large scale Bayesian posterior simulation. We assume that the dataset is partitioned and stored across nodes of a cluster. Our procedure involves an independent MCMC posterior sampler at each node based on its local partition of the data. Moment statistics of the local posteriors are collected from each sampler and propagated across the cluster using expectation propagation message passing with low communication costs. The moment sharing scheme improves posterior estimation quality by enforcing agreement among the samplers. We demonstrate the speed and inference quality of our method with empirical studies on Bayesian logistic regression and sparse linear regression with a spike-and-slab prior.

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ICML Conference 2013 Conference Paper

Fast Max-Margin Matrix Factorization with Data Augmentation

Minjie Xu
Jun Zhu 0001
Bo Zhang 0010

Existing max-margin matrix factorization (M3F) methods either are computationally inefficient or need a model selection procedure to determine the number of latent factors. In this paper we present a probabilistic M3F model that admits a highly efficient Gibbs sampling algorithm through data augmentation. We further extend our approach to incorporate Bayesian nonparametrics and build accordingly a truncation-free nonparametric M3F model where the number of latent factors is literally unbounded and inferred from data. Empirical studies on two large real-world data sets verify the efficacy of our proposed methods.

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NeurIPS Conference 2012 Conference Paper

Nonparametric Max-Margin Matrix Factorization for Collaborative Prediction

Minjie Xu
Jun Zhu
Bo Zhang

We present a probabilistic formulation of max-margin matrix factorization and build accordingly a nonparametric Bayesian model which automatically resolves the unknown number of latent factors. Our work demonstrates a successful example that integrates Bayesian nonparametrics and max-margin learning, which are conventionally two separate paradigms and enjoy complementary advantages. We develop an efcient variational algorithm for posterior inference, and our extensive empirical studies on large-scale MovieLens and EachMovie data sets appear to justify the aforementioned dual advantages.

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IROS Conference 2005 Conference Paper

Flocking coordination of multiple mobile autonomous agents with asymmetric interactions and switching topology

Hong Shi
Long Wang 0001
Tianguang Chu
Minjie Xu

This paper considers a group of mobile autonomous agents moving in the space with point mass dynamics and with asymmetric coupling matrix. We investigate the dynamic properties of the group for the case that the topology of the neighboring relations between agents varies with time. Under the assumption that the neighboring graphs are always connected, we show that stable flocking motion can be achieved by using a set of switching control laws. The control laws are a combination of attractive/repulsive and alignment forces. By using the control laws, all agent velocities become asymptotically the same, collisions can be avoided between the agents, and the final tight formation minimizes all agent potentials. Moreover, we show that the velocity of the center of mass is invariant and is equal to the final common velocity. Finally, we study the motion of the group when the velocity damping is taken into account. We prove that the common velocity asymptotically approaches zero. In this case, we can properly modify the control laws to generate the same stable flocking motion.

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