2023

Apr 21 (Wei Zhou): A Two-Sample Conditional Distribution Test Using Conformal Prediction and Weighted Rank Sum (JASA, 2023)

Apr 14 (Xianru Wang): High-dimensional, multiscale online changepoint detection (JRSS-B, 2022)

Mar 31 (Xianru Wang): High-dimensional, multiscale online changepoint detection (JRSS-B, 2022)

Mar 24 (Ruixuan Zhao): Causal inference with confounders missing not at random (Bka, 2019)

Mar 10 (Yaoming Zhen): Nonparametric Prediction Distribution from Resolution-Wise Regression with Heterogeneous Data (JBES, 2022)

Feb 24 (Yuzhao Zhang): Bias-Adjusted Spectral Clustering in Multi-Layer Stochastic Block Models (JASA, 2022)

Feb 17 (Changyu Liu): Generative Modeling by Estimating Gradients of the Data Distribution (NeurIPS, 2019)

Feb 10 (Mingyang Ren): Transfer learning for high-dimensional linear regression: Prediction, estimation and minimax optimality (JRSS-B, 2021)

Feb 3 (Haoran Zhang): Causal Inference for Social Network Data (JASA, 2022)

2021

Nov 26 (Ruixuan Zhao): Spectral Deconfounding via Perturbed Sparse Linear Models (JMLR, 2020)

Nov 19 (Wei Zhou): Causal Dantzig: Fast inference in linear structural equation models with hidden variables under additive interventions (AoS, 2019)

Nov 12 (Jingyi Yao): Asymmetry helps: Eigenvalue and eigenvector analyses of asymmetrically perturbed low-rank matrices (AoS, 2021)

Nov 5 (Yaoming Zhen): Partially Observed Dynamic Tensor Response Regression (JASA, 2021)

Oct 28 (Chengye Li): A sparse Ising model with covariates (Biometrics, 2014)

Oct 14 (Jingnan Zhang): Optimal network online change point localisation (AoS, 2021)

Oct 7 (Jingnan Zhang): Change Point Estimation in a Dynamic Stochastic Block Model (JMLR, 2020)

Sep 17 (Haoran Zhang): Panning for gold: ‘model-X’ knockoffs for high dimensional controlled variable selection (JRSS-B, 2018)

Apr 26 (Shirong Xu): Neural Tangent Kernel: Convergence and Generalization in Neural Networks (NeuIPS, 2018)

Apr 12 (Wei Zhou): Casual discovery in heavy-tailed models (AoS, 2021)

Mar 22 (Jingyi Yao): Instrumented Principal Component Analysis (SSRN, 2020)

Mar 15 (Yaoming Zhen): Provable Convex Co-clustering of Tensors (JMLR, 2018)

Mar 8 (Jingnan Zhang): Targeted sampling from massive block model graphs with personalized PageRank (JRSS-B, 2020)

Feb 22 (Chengye Li): Rare feature selection (JASA, 2021)

Feb 8 (Satie Yao): Convergence of Sparse Variational Inference in Gaussian Processes Regression (JMLR, 2020)

Feb 1 (Shirong Xu): Training Neural Networks as Learning Data Adaptive Kernels: Provable Representation and Approximation Benefits. (JASA, 2021)

Jan 25 (Wei Zhou): continue from last presentation

Jan 11 (Haoran Zhang): Unfolding-Model-Based Visualization: Theory, Method and Applications (JMLR, 2020) 

2020

Dec 5 (Huiling Yuan): High-dimensional vector autoregressive time series modeling via tensor decomposition (JASA, 2020)

Nov 25 (Wei Zhou): Local Causal Network Learning for Finding Pairs of Total and Direct Effects (JMLR, 2018)

Nov 18 (Jingyi Yao): Ridge Fusion in Statistical Learning (JCGS, 2015)

Nov 11 (Yaoming Zhen): Dynamic Tensor Recommender Systems (ArXiv, 2020)

Nov 4 (Jingnan Zhang): Multilayer tensor factorization with applications to recommender systems (AoS, 2018)

Oct 28 (Chengye Li): Estimating a Change Point in a Sequence of Very High-Dimensional Covariance Matrices (JASA, 2020)

Oct 21 (Satie Yao): Efficient sampling functions from Gaussian process posteriors (ICML, 2020)

Oct 7 (Shirong Xu): A Statistical Learning Approach to Modal Regression (JMLR, 2020)

Sep 23 (Jingyi Yao): Large Dynamic Covariance Matrices (JBES, 2019)

Sep 16 (Yaoming Zhen): Non-Convex Projected Gradient Descent for Generalized Low-Rank Tensor Regression (JMLR, 2019)

Sep 9 (Jingnan Zhang): Personalized Dose Finding Using Outcome Weighted Learning (JASA, 2016)

Sep 2 (Ben Dai): Domain-Adversarial Training of Neural Networks (JMLR, 2017)