It is widely recognized that the data quality affects machine learning (ML) model performances, and data scientists spend considerable amount of time…

Deep learning methods exhibit promising performance for predictive modeling in healthcare, but two important challenges remain: -Data insufficiency:O…

A variety of real-world processes (over networks) produce sequences of data whose complex temporal dynamics need to be studied. More especially, the …

The dominant cost in solving least-square problems using Newton's method is often that of factorizing the Hessian matrix over multiple values of the …

We study the performance of linear solvers for graph Laplacians based on the combinatorial cycle adjustment methodology proposed by [Kelner-Orecchia-…

Stackelberg Pricing Games is a two-level combinatorial pricing problem studied in the Economics, Operation Research, and Computer Science communities…

The spread of invasive species to new areas threatens the stability of ecosystems and causes major economic losses in agriculture and forestry. We pr…

A new approach to understanding evolution [Val09], namely viewing it through the lens of computation, has already started yielding new insights, e.g.…

Deep reinforcement learning has seen great success across a breadth of tasks, such as in game playing and robotic manipulation. However, the modern p…

In this paper, we consider the problem of finding the feature correspondences among a collection of feature sets, by using their point-wise unary fea…

Large volume of networked streaming event data are becoming increasingly available in a wide variety of applications, such as social network analysis…

Current approaches for visual-inertial odometry (VIO) are able to attain highly accurate state estimation via nonlinear optimization. However, real-t…

Locomotion for legged robots poses considerable challenges when confronted by obstacles and adverse environments. Footstep planners are typically onl…

Many programmable matter systems have been proposed and realized recently, each often tailored to a specific task or physical setting. In our work on…

Scan (or prefix sum) is a fundamental and widely used primitive in parallel computing. In this paper, we present LightScan, a faster parallel scan pr…

This paper introduces the factorial marked temporal point process model and presents efficient learning methods. In conventional (multi-dimensional) …

The knowledge base completion problem is the problem of inferring missing information from existing facts in knowledge bases. Path-ranking based meth…

We present an approach for identifying picturesque highlights from large amounts of egocentric video data. Given a set of egocentric videos captured …

We introduce a functional gradient descent trajectory optimization algorithm for robot motion planning in Reproducing Kernel Hilbert Spaces (RKHSs). …

Active learning agents typically employ a query selection algorithm which solely considers the agent's learning objectives. However, this may be insu…

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