1.1k citations · 4k across the 47 of their papers we have counts for
6 papers · 1 filter
Score-based Continuous-time Discrete Diffusion Models
Haoran Sun, Lijun Yu, Bo Dai +2
Score-based modeling through stochastic differential equations (SDEs) has provided a new perspective on diffusion models, and demonstrated superior performance on continuous data.…
Learning to Optimize with Stochastic Dominance Constraints
Hanjun Dai, Yuan Xue, Niao He +4
In real-world decision-making, uncertainty is important yet difficult to handle. Stochastic dominance provides a theoretically sound approach for comparing uncertain quantities, bu…
Optimal Scaling for Locally Balanced Proposals in Discrete Spaces
Haoran Sun, Hanjun Dai, Dale Schuurmans
Optimal scaling has been well studied for Metropolis-Hastings (M-H) algorithms in continuous spaces, but a similar understanding has been lacking in discrete spaces. Recently, a fa…
Annealed Training for Combinatorial Optimization on Graphs
Haoran Sun, Etash K. Guha, Hanjun Dai
The hardness of combinatorial optimization (CO) problems hinders collecting solutions for supervised learning. However, learning neural networks for CO problems is notoriously diff…
Discrete Langevin Sampler via Wasserstein Gradient Flow
Haoran Sun, Hanjun Dai, Bo Dai +2
It is known that gradient-based MCMC samplers for continuous spaces, such as Langevin Monte Carlo (LMC), can be derived as particle versions of a gradient flow that minimizes KL di…
CrossBeam: Learning to Search in Bottom-Up Program Synthesis
Kensen Shi, Hanjun Dai, Kevin Ellis +1
Many approaches to program synthesis perform a search within an enormous space of programs to find one that satisfies a given specification. Prior works have used neural models to…