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09/21/2023
BitCoin: Bidirectional Tagging and Supervised Contrastive Learning based Joint Relational Triple Extraction Framework
Relation triple extraction (RTE) is an essential task in information ext...
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07/25/2023
Settling the Sample Complexity of Online Reinforcement Learning
A central issue lying at the heart of online reinforcement learning (RL)...
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07/25/2023
Learning Regions of Interest for Bayesian Optimization with Adaptive Level-Set Estimation
We study Bayesian optimization (BO) in high-dimensional and non-stationa...
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06/17/2023
Active Policy Improvement from Multiple Black-box Oracles
Reinforcement learning (RL) has made significant strides in various comp...
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06/15/2023
Towards Faster Non-Asymptotic Convergence for Diffusion-Based Generative Models
Diffusion models, which convert noise into new data instances by learnin...
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05/26/2023
The Curious Price of Distributional Robustness in Reinforcement Learning with a Generative Model
This paper investigates model robustness in reinforcement learning (RL) ...
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05/17/2023
Reward-agnostic Fine-tuning: Provable Statistical Benefits of Hybrid Reinforcement Learning
This paper studies tabular reinforcement learning (RL) in the hybrid set...
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05/03/2023
Efficient Online Decision Tree Learning with Active Feature Acquisition
Constructing decision trees online is a classical machine learning probl...
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04/14/2023
Minimax-Optimal Reward-Agnostic Exploration in Reinforcement Learning
This paper studies reward-agnostic exploration in reinforcement learning...
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04/05/2023
A force-sensing surgical drill for real-time force feedback in robotic mastoidectomy
Purpose: Robotic assistance in otologic surgery can reduce the task load...
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03/10/2023
Deflated HeteroPCA: Overcoming the curse of ill-conditioning in heteroskedastic PCA
This paper is concerned with estimating the column subspace of a low-ran...
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01/30/2023
Fast Computation of Optimal Transport via Entropy-Regularized Extragradient Methods
Efficient computation of the optimal transport distance between two dist...
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08/22/2022
Minimax-Optimal Multi-Agent RL in Markov Games With a Generative Model
This paper studies multi-agent reinforcement learning in Markov games, w...
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07/13/2022
Cost-Effective Online Contextual Model Selection
How can we collect the most useful labels to learn a model selection pol...
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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...
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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...
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04/11/2022
Settling the Sample Complexity of Model-Based Offline Reinforcement Learning
This paper is concerned with offline reinforcement learning (RL), which ...
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03/20/2022
Open-Vocabulary One-Stage Detection with Hierarchical Visual-Language Knowledge Distillation
Open-vocabulary object detection aims to detect novel object categories ...
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03/16/2022
Learning Representation for Bayesian Optimization with Collision-free Regularization
Bayesian optimization has been challenged by datasets with large-scale, ...
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03/14/2022
The Efficacy of Pessimism in Asynchronous Q-Learning
This paper is concerned with the asynchronous form of Q-learning, which ...
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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...
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02/03/2022
Rethinking Explainability as a Dialogue: A Practitioner's Perspective
As practitioners increasingly deploy machine learning models in critical...
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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...
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12/27/2021
BALanCe: Deep Bayesian Active Learning via Equivalence Class Annealing
Active learning has demonstrated data efficiency in many fields. Existin...
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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...
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11/03/2021
Online Learning of Energy Consumption for Navigation of Electric Vehicles
Energy-efficient navigation constitutes an important challenge in electr...
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10/28/2021
Class-wise Thresholding for Detecting Out-of-Distribution Data
We consider the problem of detecting OoD(Out-of-Distribution) input data...
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10/28/2021
Teaching an Active Learner with Contrastive Examples
We study the problem of active learning with the added twist that the le...
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10/09/2021
Breaking the Sample Complexity Barrier to Regret-Optimal Model-Free Reinforcement Learning
Achieving sample efficiency in online episodic reinforcement learning (R...
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08/12/2021
A Contract Theory based Incentive Mechanism for Federated Learning
Federated learning (FL) serves as a data privacy-preserved machine learn...
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07/26/2021
Inference for Heteroskedastic PCA with Missing Data
This paper studies how to construct confidence regions for principal com...
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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...
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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...
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05/16/2021
Understanding the Effect of Bias in Deep Anomaly Detection
Anomaly detection presents a unique challenge in machine learning, due t...
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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...
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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...
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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...
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02/22/2021
Softmax Policy Gradient Methods Can Take Exponential Time to Converge
The softmax policy gradient (PG) method, which performs gradient ascent ...
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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...
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12/15/2020
Spectral Methods for Data Science: A Statistical Perspective
Spectral methods have emerged as a simple yet surprisingly effective app...
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10/17/2020
Preference-Based Batch and Sequential Teaching
Algorithmic machine teaching studies the interaction between a teacher a...
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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...
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09/23/2020
Learning Mixtures of Low-Rank Models
We study the problem of learning mixtures of low-rank models, i.e. recon...
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08/20/2020
Using Ensemble Classifiers to Detect Incipient Anomalies
Incipient anomalies present milder symptoms compared to severe ones, and...
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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...
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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...
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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 ...
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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...
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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...
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06/15/2020