b'Yuxin Chen'

research

∙ 06/08/2022

Model-Based Reinforcement Learning Is Minimax-Optimal for Offline Zero-Sum Markov Games

This paper makes progress towards learning Nash equilibria in two-player...

Yuling Yan, et al. ∙

research

∙ 05/25/2022

These Maps Are Made For Walking: Real-Time Terrain Property Estimation for Mobile Robots

The equations of motion governing mobile robots are dependent on terrain...

Parker Ewen, et al. ∙

research

∙ 04/11/2022

Settling the Sample Complexity of Model-Based Offline Reinforcement Learning

This paper is concerned with offline reinforcement learning (RL), which ...

Gen Li, et al. ∙

research

∙ 03/20/2022

Open-Vocabulary One-Stage Detection with Hierarchical Visual-Language Knowledge Distillation

Open-vocabulary object detection aims to detect novel object categories ...

Zongyang Ma, et al. ∙

research

∙ 03/16/2022

Learning Representation for Bayesian Optimization with Collision-free Regularization

Bayesian optimization has been challenged by datasets with large-scale, ...

Fengxue Zhang, et al. ∙

research

∙ 03/14/2022

The Efficacy of Pessimism in Asynchronous Q-Learning

This paper is concerned with the asynchronous form of Q-learning, which ...

Yuling Yan, et al. ∙

research

∙ 02/28/2022

Pessimistic Q-Learning for Offline Reinforcement Learning: Towards Optimal Sample Complexity

Offline or batch reinforcement learning seeks to learn a near-optimal po...

Laixi Shi, et al. ∙

research

∙ 02/03/2022

Rethinking Explainability as a Dialogue: A Practitioner's Perspective

As practitioners increasingly deploy machine learning models in critical...

Himabindu Lakkaraju, et al. ∙

research

∙ 01/21/2022

LRSVRG-IMC: An SVRG-Based Algorithm for LowRank Inductive Matrix Completion

Low-rank inductive matrix completion (IMC) is currently widely used in I...

Shangrong Yu, et al. ∙

research

∙ 12/27/2021

BALanCe: Deep Bayesian Active Learning via Equivalence Class Annealing

Active learning has demonstrated data efficiency in many fields. Existin...

Renyu Zhang, et al. ∙

research

∙ 11/30/2021

Atos: A Task-Parallel GPU Dynamic Scheduling Framework for Dynamic Irregular Computations

We present Atos, a task-parallel GPU dynamic scheduling framework that i...

Yuxin Chen, et al. ∙

research

∙ 11/03/2021

Online Learning of Energy Consumption for Navigation of Electric Vehicles

Energy-efficient navigation constitutes an important challenge in electr...

Niklas Åkerblom, et al. ∙

research

∙ 10/28/2021

Class-wise Thresholding for Detecting Out-of-Distribution Data

We consider the problem of detecting OoD(Out-of-Distribution) input data...

Matteo Guarrera, et al. ∙

research

∙ 10/28/2021

Teaching an Active Learner with Contrastive Examples

We study the problem of active learning with the added twist that the le...

Chaoqi Wang, et al. ∙

research

∙ 10/09/2021

Breaking the Sample Complexity Barrier to Regret-Optimal Model-Free Reinforcement Learning

Achieving sample efficiency in online episodic reinforcement learning (R...

Gen Li, et al. ∙

research

∙ 08/12/2021

A Contract Theory based Incentive Mechanism for Federated Learning

Federated learning (FL) serves as a data privacy-preserved machine learn...

Mengmeng Tian, et al. ∙

research

∙ 07/26/2021

Inference for Heteroskedastic PCA with Missing Data

This paper studies how to construct confidence regions for principal com...

Yuling Yan, et al. ∙

research

∙ 05/24/2021

Policy Mirror Descent for Regularized Reinforcement Learning: A Generalized Framework with Linear Convergence

Policy optimization, which learns the policy of interest by maximizing t...

Wenhao Zhan, et al. ∙

research

∙ 05/17/2021

Sample-Efficient Reinforcement Learning Is Feasible for Linearly Realizable MDPs with Limited Revisiting

Low-complexity models such as linear function representation play a pivo...

Gen Li, et al. ∙

research

∙ 05/16/2021

Understanding the Effect of Bias in Deep Anomaly Detection

Anomaly detection presents a unique challenge in machine learning, due t...

Ziyu Ye, et al. ∙

research

∙ 04/23/2021

Generating Continuous Motion and Force Plans in Real-Time for Legged Mobile Manipulation

Manipulators can be added to legged robots, allowing them to interact wi...

Parker Ewen, et al. ∙

research

∙ 04/14/2021

Towards an Interpretable Data-driven Trigger System for High-throughput Physics Facilities

Data-intensive science is increasingly reliant on real-time processing c...

Chinmaya Mahesh, et al. ∙

research

∙ 04/07/2021

Minimax Estimation of Linear Functions of Eigenvectors in the Face of Small Eigen-Gaps

Eigenvector perturbation analysis plays a vital role in various statisti...

Gen Li, et al. ∙

research

∙ 02/22/2021

Softmax Policy Gradient Methods Can Take Exponential Time to Converge

The softmax policy gradient (PG) method, which performs gradient ascent ...

Gen Li, et al. ∙

research

∙ 02/12/2021

Is Q-Learning Minimax Optimal? A Tight Sample Complexity Analysis

Q-learning, which seeks to learn the optimal Q-function of a Markov deci...

Gen Li, et al. ∙

research

∙ 12/15/2020

Spectral Methods for Data Science: A Statistical Perspective

Spectral methods have emerged as a simple yet surprisingly effective app...

Yuxin Chen, et al. ∙

research

∙ 10/17/2020

Preference-Based Batch and Sequential Teaching

Algorithmic machine teaching studies the interaction between a teacher a...

Farnam Mansouri, et al. ∙

research

∙ 10/04/2020

Learning Time Varying Risk Preferences from Investment Portfolios using Inverse Optimization with Applications on Mutual Funds

The fundamental principle in Modern Portfolio Theory (MPT) is based on t...

Shi Yu, et al. ∙

research

∙ 09/23/2020

Learning Mixtures of Low-Rank Models

We study the problem of learning mixtures of low-rank models, i.e. recon...

Yanxi Chen, et al. ∙

research

∙ 08/20/2020

Using Ensemble Classifiers to Detect Incipient Anomalies

Incipient anomalies present milder symptoms compared to severe ones, and...

Baihong Jin, et al. ∙

research

∙ 08/04/2020

Convex and Nonconvex Optimization Are Both Minimax-Optimal for Noisy Blind Deconvolution

We investigate the effectiveness of convex relaxation and nonconvex opti...

Yuxin Chen, et al. ∙

research

∙ 07/13/2020

Fast Global Convergence of Natural Policy Gradient Methods with Entropy Regularization

Natural policy gradient (NPG) methods are among the most widely used pol...

Shicong Cen, et al. ∙

research

∙ 07/12/2020

Exploiting Uncertainties from Ensemble Learners to Improve Decision-Making in Healthcare AI

Ensemble learning is widely applied in Machine Learning (ML) to improve ...

Yingshui Tan, et al. ∙

research

∙ 07/07/2020

Are Ensemble Classifiers Powerful Enough for the Detection and Diagnosis of Intermediate-Severity Faults?

IS faults present milder symptoms compared to severe faults, and are mor...

Baihong Jin, et al. ∙

research

∙ 06/25/2020

Average-case Complexity of Teaching Convex Polytopes via Halfspace Queries

We examine the task of locating a target region among those induced by i...

Akash Kumar, et al. ∙

research

∙ 06/15/2020

Uncertainty quantification for nonconvex tensor completion: Confidence intervals, heteroscedasticity and optimality

We study the distribution and uncertainty of nonconvex optimization for ...

Changxiao Cai, et al. ∙

research

∙ 06/04/2020

Sample Complexity of Asynchronous Q-Learning: Sharper Analysis and Variance Reduction

Asynchronous Q-learning aims to learn the optimal action-value function ...

Gen Li, et al. ∙

research

∙ 05/26/2020

Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model

We investigate the sample efficiency of reinforcement learning in a γ-di...

Gen Li, et al. ∙

research

∙ 03/21/2020

Understanding the Power and Limitations of Teaching with Imperfect Knowledge

Machine teaching studies the interaction between a teacher and a student...

Rati Devidze, et al. ∙

research

∙ 03/03/2020

An Online Learning Framework for Energy-Efficient Navigation of Electric Vehicles

Energy-efficient navigation constitutes an important challenge in electr...

Niklas Åkerblom, et al. ∙

research

∙ 02/17/2020

A Financial Service Chatbot based on Deep Bidirectional Transformers

We develop a chatbot using Deep Bidirectional Transformer models (BERT) ...

Shi Yu, et al. ∙

research

∙ 01/27/2020

Adaptive Teaching of Temporal Logic Formulas to Learners with Preferences

Machine teaching is an algorithmic framework for teaching a target hypot...

Zhe Xu, et al. ∙

research

∙ 01/15/2020

Bridging Convex and Nonconvex Optimization in Robust PCA: Noise, Outliers, and Missing Data

This paper delivers improved theoretical guarantees for the convex progr...

Yuxin Chen, et al. ∙

research

∙ 01/14/2020

Inference for linear forms of eigenvectors under minimal eigenvalue separation: Asymmetry and heteroscedasticity

A fundamental task that spans numerous applications is inference and unc...

Chen Cheng, et al. ∙

research

∙ 11/11/2019

Nonconvex Low-Rank Symmetric Tensor Completion from Noisy Data

We study a noisy symmetric tensor completion problem of broad practical ...

Changxiao Cai, et al. ∙

research

∙ 10/27/2019

Landmark Ordinal Embedding

In this paper, we aim to learn a low-dimensional Euclidean representatio...

Nikhil Ghosh, et al. ∙

research

∙ 10/24/2019

Preference-Based Batch and Sequential Teaching: Towards a Unified View of Models

Algorithmic machine teaching studies the interaction between a teacher a...

Farnam Mansouri, et al. ∙

research

∙ 10/09/2019

Subspace Estimation from Unbalanced and Incomplete Data Matrices: ℓ_2,∞ Statistical Guarantees

This paper is concerned with estimating the column space of an unknown l...

Changxiao Cai, et al. ∙

research

∙ 10/04/2019

RDMA vs. RPC for Implementing Distributed Data Structures

Distributed data structures are key to implementing scalable application...

Benjamin Brock, et al. ∙

research

∙ 09/18/2019

Nailed It: Autonomous Roofing with a Nailgun-Equipped Octocopter

This paper presents the first demonstration of autonomous roofing with a...

Matthew Romano, et al. ∙

Yuxin Chen

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