41 citations · 148 across the 27 of their papers we have counts for
27 papers
Markovian Sliced Wasserstein Distances: Beyond Independent Projections
Khai Nguyen, Tongzheng Ren, Nhat Ho
Sliced Wasserstein (SW) distance suffers from redundant projections due to independent uniform random projecting directions. To partially overcome the issue, max K sliced Wasserste…
Latent Variable Representation for Reinforcement Learning
Tongzheng Ren, Chenjun Xiao, Tianjun Zhang +5
Deep latent variable models have achieved significant empirical successes in model-based reinforcement learning (RL) due to their expressiveness in modeling complex transition dyna…
Designing Robust Transformers using Robust Kernel Density Estimation
Xing Han, Tongzheng Ren, Tan Minh Nguyen +3
Recent advances in Transformer architectures have empowered their empirical success in a variety of tasks across different domains. However, existing works mainly focus on predicti…
Hierarchical Sliced Wasserstein Distance
Khai Nguyen, Tongzheng Ren, Huy Nguyen +3
Sliced Wasserstein (SW) distance has been widely used in different application scenarios since it can be scaled to a large number of supports without suffering from the curse of di…
Spectral Decomposition Representation for Reinforcement Learning
Tongzheng Ren, Tianjun Zhang, Lisa Lee +3
Representation learning often plays a critical role in reinforcement learning by managing the curse of dimensionality. A representative class of algorithms exploits a spectral deco…
Making Linear MDPs Practical via Contrastive Representation Learning
Tianjun Zhang, Tongzheng Ren, Mengjiao Yang +3
It is common to address the curse of dimensionality in Markov decision processes (MDPs) by exploiting low-rank representations. This motivates much of the recent theoretical study…
Statistical and Computational Complexities of BFGS Quasi-Newton Method for Generalized Linear Models
Qiujiang Jin, Tongzheng Ren, Nhat Ho +1
The gradient descent (GD) method has been used widely to solve parameter estimation in generalized linear models (GLMs), a generalization of linear models when the link function ca…
Efficient Forecasting of Large Scale Hierarchical Time Series via Multilevel Clustering
Xing Han, Tongzheng Ren, Jing Hu +2
We propose a novel approach to the problem of clustering hierarchically aggregated time-series data, which has remained an understudied problem though it has several commercial app…
Beyond EM Algorithm on Over-specified Two-Component Location-Scale Gaussian Mixtures
Tongzheng Ren, Fuheng Cui, Sujay Sanghavi +1
The Expectation-Maximization (EM) algorithm has been predominantly used to approximate the maximum likelihood estimation of the location-scale Gaussian mixtures. However, when the…
An Exponentially Increasing Step-size for Parameter Estimation in Statistical Models
Nhat Ho, Tongzheng Ren, Sujay Sanghavi +2
Using gradient descent (GD) with fixed or decaying step-size is a standard practice in unconstrained optimization problems. However, when the loss function is only locally convex,…
Policy Learning for Robust Markov Decision Process with a Mismatched Generative Model
Jialian Li, Tongzheng Ren, Dong Yan +2
In high-stake scenarios like medical treatment and auto-piloting, it's risky or even infeasible to collect online experimental data to train the agent. Simulation-based training ca…
Improving Computational Complexity in Statistical Models with Second-Order Information
Tongzheng Ren, Jiacheng Zhuo, Sujay Sanghavi +1
It is known that when the statistical models are singular, i.e., the Fisher information matrix at the true parameter is degenerate, the fixed step-size gradient descent algorithm t…
A Free Lunch from the Noise: Provable and Practical Exploration for Representation Learning
Tongzheng Ren, Tianjun Zhang, Csaba Szepesvári +1
Representation learning lies at the heart of the empirical success of deep learning for dealing with the curse of dimensionality. However, the power of representation learning has…
Towards Statistical and Computational Complexities of Polyak Step Size Gradient Descent
Tongzheng Ren, Fuheng Cui, Alexia Atsidakou +2
We study the statistical and computational complexities of the Polyak step size gradient descent algorithm under generalized smoothness and Lojasiewicz conditions of the population…
Quasi-Bayesian Dual Instrumental Variable Regression
Ziyu Wang, Yuhao Zhou, Tongzheng Ren +1
Recent years have witnessed an upsurge of interest in employing flexible machine learning models for instrumental variable (IV) regression, but the development of uncertainty quant…
Unsupervised Out-of-Domain Detection via Pre-trained Transformers
Keyang Xu, Tongzheng Ren, Shikun Zhang +2
Deployed real-world machine learning applications are often subject to uncontrolled and even potentially malicious inputs. Those out-of-domain inputs can lead to unpredictable outp…
Combinatorial Bandits without Total Order for Arms
Shuo Yang, Tongzheng Ren, Inderjit S. Dhillon +1
We consider the combinatorial bandits problem, where at each time step, the online learner selects a size- subset from the arms set , where $\left|\mathcal{A}\r…
Nearly Horizon-Free Offline Reinforcement Learning
Tongzheng Ren, Jialian Li, Bo Dai +2
We revisit offline reinforcement learning on episodic time-homogeneous Markov Decision Processes (MDP). For tabular MDP with states and actions, or linear MDP with anchor p…
Linear Bandit Algorithms with Sublinear Time Complexity
Shuo Yang, Tongzheng Ren, Sanjay Shakkottai +3
We propose two linear bandits algorithms with per-step complexity sublinear in the number of arms . The algorithms are designed for applications where the arm set is extremely l…
Accountable Off-Policy Evaluation With Kernel Bellman Statistics
Yihao Feng, Tongzheng Ren, Ziyang Tang +1
We consider off-policy evaluation (OPE), which evaluates the performance of a new policy from observed data collected from previous experiments, without requiring the execution of…
MaxUp: A Simple Way to Improve Generalization of Neural Network Training
Chengyue Gong, Tongzheng Ren, Mao Ye +1
We propose \emph{MaxUp}, an embarrassingly simple, highly effective technique for improving the generalization performance of machine learning models, especially deep neural networ…
Stein Self-Repulsive Dynamics: Benefits From Past Samples
Mao Ye, Tongzheng Ren, Qiang Liu
We propose a new Stein self-repulsive dynamics for obtaining diversified samples from intractable un-normalized distributions. Our idea is to introduce Stein variational gradient a…
Implicit Regularization and Convergence for Weight Normalization
Xiaoxia Wu, Edgar Dobriban, Tongzheng Ren +5
Normalization methods such as batch [Ioffe and Szegedy, 2015], weight [Salimansand Kingma, 2016], instance [Ulyanov et al., 2016], and layer normalization [Baet al., 2016] have bee…
Function Space Particle Optimization for Bayesian Neural Networks
Ziyu Wang, Tongzheng Ren, Jun Zhu +1
While Bayesian neural networks (BNNs) have drawn increasing attention, their posterior inference remains challenging, due to the high-dimensional and over-parameterized nature. To…