8 papers
On the optimality of antithetic randomization for cross-validation
Srijan Chattopadhyay, Sifan Liu, Snigdha Panigrahi
In the classical normal means problem, independent train--test folds can be constructed by perturbing the data with normal randomization. Averaging over such folds yields a cro…
Markov Chain Monte Carlo with Diffusion Paths
Han Chen, Sifan Liu, Jun Yang
Sampling from multimodal distributions is a longstanding challenge for classical local Markov chain Monte Carlo (MCMC) methods. A popular remedy is to introduce a sequence of inter…
Sub-Cauchy Sampling: Escaping the Dark Side of the Moon
Sebastiano Grazzi, Sifan Liu, Gareth O. Roberts +1
We introduce a Markov chain Monte Carlo algorithm based on Sub-Cauchy Projection, a geometric transformation that generalizes stereographic projection by mapping Euclidean space in…
Rotated Mean-Field Variational Inference and Iterative Gaussianization
Yifan Chen, Sifan Liu
We propose an iterative Gaussianization method for sampling from unnormalized densities by repeatedly applying mean-field variational inference (MFVI) in rotated coordinate systems…
Flexible Selective Inference with Flow-based Transport Maps
Sifan Liu, Snigdha Panigrahi
Data-carving methods perform selective inference by conditioning the distribution of data on the observed selection event. However, existing data-carving approaches typically requi…
Towards Understanding Camera Motions in Any Video
Zhiqiu Lin, Siyuan Cen, Daniel Jiang +12
We introduce CameraBench, a large-scale dataset and benchmark designed to assess and improve camera motion understanding. CameraBench consists of ~3,000 diverse internet videos, an…
MultiConIR: Towards multi-condition Information Retrieval
Xuan Lu, Sifan Liu, Bochao Yin +6
Multi-condition information retrieval (IR) presents a significant, yet underexplored challenge for existing systems. This paper introduces MultiConIR, a benchmark specifically desi…
Cross-Validation with Antithetic Gaussian Randomization
Sifan Liu, Snigdha Panigrahi, Jake A. Soloff
We introduce a new cross-validation method based on an equicorrelated Gaussian randomization scheme. Our method is well-suited for problems where sample splitting is infeasible, ei…