Wonyeol Lee

Influence maximization problem with applications to viral marketing has gained much attention. Underlying influence diffusion models affect influence maximizing nodes because they focus on difference aspect of influence diffusion. Nevertheless, existing diffusion models overlook two important aspects of real-world marketing - continuous trials and time restriction. This paper proposes a new realistic influence diffusion model called Continously activated and Time-restricted IC (CT-IC) model whic

Reasoning about floating-point is difficult and becomes only more so if there is an interplay between floating-point and bit-level operations. Even though real-world floating-point libraries use implementations that have such mixed computations, no systematic technique to verify the correctness of the implementations of such computations is known. In this paper, we present the first general technique for verifying the correctness of mixed binaries, which combines abstraction, analytical optimiza

Industry standard implementations of math.h claim (often without formal proof) tight bounds on floating-point errors. We demonstrate a novel static analysis that proves these bounds and verifies the correctness of these implementations. Our key insight is a reduction of this verification task to a set of mathematical optimization problems that can be solved by off-the-shelf computer algebra systems. We use this analysis to prove the correctness of implementations in Intel's math library automati

Probabilistic programming is the idea of writing models from statistics and machine learning using program notations and reasoning about these models using generic inference engines. Recently its combination with deep learning has been explored intensely, which led to the development of so called deep probabilistic programming languages, such as Pyro, Edward and ProbTorch. At the core of this development lie inference engines based on stochastic variational inference algorithms. When asked to fi

Separation logic is an extension of Hoare logic which is acknowledged as an enabling technology for large-scale program verification. It features two new logical connectives, separating conjunction and separating implication, but most of the applications of separation logic have exploited only separating conjunction without considering separating implication. Nevertheless the power of separating implication has been well recognized and there is a growing interest in its use for program verificat

Differentiation lies at the core of many machine-learning algorithms, and is\nwell-supported by popular autodiff systems, such as TensorFlow and PyTorch.\nOriginally, these systems have been developed to compute derivatives of\ndifferentiable functions, but in practice, they are commonly applied to\nfunctions with non-differentiabilities. For instance, neural networks using\nReLU define non-differentiable functions in general, but the gradients of\nlosses involving those functions are computed u

We present a static analysis for discovering differentiable or more generally smooth parts of a given probabilistic program, and show how the analysis can be used to improve the pathwise gradient estimator, one of the most popular methods for posterior inference and model learning. Our improvement increases the scope of the estimator from differentiable models to non-differentiable ones without requiring manual intervention of the user; the improved estimator automatically identifies differentia

We present a new algorithm for stochastic variational inference that targets at models with non-differentiable densities. One of the key challenges in stochastic variational inference is to come up with a low-variance estimator of the gradient of a variational objective. We tackle the challenge by generalizing the reparameterization trick, one of the most effective techniques for addressing the variance issue for differentiable models, so that the trick works for non-differentiable models as wel

Separation logic is an extension of Hoare logic which is acknowledged as an enabling technology for large-scale program verification. It features two new logical connectives, separating conjunction and separating implication, but most of the applications of separation logic have exploited only separating conjunction without considering separating implication. Nevertheless the power of separating implication has been well recognized and there is a growing interest in its use for program verificat

Industry standard implementations of {\tt math.h} claim (often without formal proof) tight bounds on floating-point errors. We demonstrate a novel static analysis that proves these bounds and verifies the correctness of these implementations. Our key insight is a reduction of this verification task to a set of mathematical optimization problems that can be solved by off-the-shelf computer algebra systems. We use this analysis to prove the correctness of implementations in Intel’s math library au

We present a new algorithm for stochastic variational inference that targets at models with non-differentiable densities. One of the key challenges in stochastic variational inference is to come up with a low-variance estimator of the gradient of a variational objective. We tackle the challenge by generalizing the reparameterization trick, one of the most effective techniques for addressing the variance issue for differentiable models, so that the trick works for non-differentiable models as wel

Edge detection is a significant step in image processing. Morphological edge detectors developed until now used a fixed structuring element (SE) on all the image pixels; however, they cannot consider the local features of an image due to the fixed SE and we should choose an appropriate SE by lots of experiments. In this paper, new morphological edge detectors using amoebas, dynamic structuring elements which adapt their shapes to image contours, are proposed. The experimental results show that a

We present a static analysis for discovering differentiable or more generally smooth parts of a given probabilistic program, and show how the analysis can be used to improve the pathwise gradient estimator, one of the most popular methods for posterior inference and model learning. Our improvement increases the scope of the estimator from differentiable models to non-differentiable ones without requiring manual intervention of the user; the improved estimator automatically identifies differentia