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03/22/2023
Noisy recovery from random linear observations: Sharp minimax rates under elliptical constraints
Estimation problems with constrained parameter spaces arise in various s...
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01/17/2023
Sparse and Integrative Principal Component Analysis for Multiview Data
We consider dimension reduction of multiview data, which are emerging in...
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11/19/2022
Pairwise Instance Relation Augmentation for Long-tailed Multi-label Text Classification
Multi-label text classification (MLTC) is one of the key tasks in natura...
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10/04/2022
Linear Convergence of Natural Policy Gradient Methods with Log-Linear Policies
We consider infinite-horizon discounted Markov decision processes and st...
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10/03/2022
Faster Last-iterate Convergence of Policy Optimization in Zero-Sum Markov Games
Multi-Agent Reinforcement Learning (MARL) – where multiple agents learn ...
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05/25/2022
BiT: Robustly Binarized Multi-distilled Transformer
Modern pre-trained transformers have rapidly advanced the state-of-the-a...
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04/27/2022
FedShuffle: Recipes for Better Use of Local Work in Federated Learning
The practice of applying several local updates before aggregation across...
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04/08/2022
Federated Learning with Partial Model Personalization
We consider two federated learning algorithms for training partially per...
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01/19/2022
On the Convergence Rates of Policy Gradient Methods
We consider infinite-horizon discounted Markov decision problems with fi...
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10/19/2021
Importance Estimation from Multiple Perspectives for Keyphrase Extraction
Keyphrase extraction is a fundamental task in Natural Language Processin...
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01/24/2021
Does Head Label Help for Long-Tailed Multi-Label Text Classification
Multi-label text classification (MLTC) aims to annotate documents with t...
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10/12/2020
Improving Self-supervised Pre-training via a Fully-Explored Masked Language Model
Masked Language Model (MLM) framework has been widely adopted for self-s...
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05/30/2020
Jointly Modeling Intra- and Inter-transaction Dependencies with Hierarchical Attentive Transaction Embeddings for Next-item Recommendation
A transaction-based recommender system (TBRS) aims to predict the next i...
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02/25/2020
Statistically Preconditioned Accelerated Gradient Method for Distributed Optimization
We consider the setting of distributed empirical risk minimization where...
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02/25/2020
Statistical Adaptive Stochastic Gradient Methods
We propose a statistical adaptive procedure called SALSA for automatical...
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10/30/2019
Understanding the Role of Momentum in Stochastic Gradient Methods
The use of momentum in stochastic gradient methods has become a widespre...
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10/18/2019
Joint Computation and Communication Design for UAV-Assisted Mobile Edge Computing in IoT
Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) syste...
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09/21/2019
Using Statistics to Automate Stochastic Optimization
Despite the development of numerous adaptive optimizers, tuning the lear...
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08/29/2019
Multi-Level Composite Stochastic Optimization via Nested Variance Reduction
We consider multi-level composite optimization problems where each mappi...
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06/24/2019
A Stochastic Composite Gradient Method with Incremental Variance Reduction
We consider the problem of minimizing the composition of a smooth (nonco...
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05/26/2019
Hyperbolic Interaction Model For Hierarchical Multi-Label Classification
Different from the traditional classification tasks which assume mutual ...
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05/24/2019
Label-aware Document Representation via Hybrid Attention for Extreme Multi-Label Text Classification
Extreme multi-label text classification (XMTC) aims at tagging a documen...
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07/12/2018
Secrecy Energy Efficiency Maximization for UAV-Enabled Mobile Relaying
This paper investigates the secrecy energy efficiency (SEE) maximization...
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03/06/2018
Learning SMaLL Predictors
We present a new machine learning technique for training small resource-...
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12/29/2017
Smoothed Dual Embedding Control
We revisit the Bellman optimality equation with Nesterov's smoothing tec...
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10/13/2017
DSCOVR: Randomized Primal-Dual Block Coordinate Algorithms for Asynchronous Distributed Optimization
Machine learning with big data often involves large optimization models....
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03/07/2017
Exploiting Strong Convexity from Data with Primal-Dual First-Order Algorithms
We consider empirical risk minimization of linear predictors with convex...
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02/25/2017
Stochastic Variance Reduction Methods for Policy Evaluation
Policy evaluation is a crucial step in many reinforcement-learning proce...
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01/01/2015
Communication-Efficient Distributed Optimization of Self-Concordant Empirical Loss
We consider distributed convex optimization problems originated from sam...
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03/19/2014
A Proximal Stochastic Gradient Method with Progressive Variance Reduction
We consider the problem of minimizing the sum of two convex functions: o...
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06/25/2013
A Randomized Nonmonotone Block Proximal Gradient Method for a Class of Structured Nonlinear Programming
We propose a randomized nonmonotone block proximal gradient (RNBPG) meth...
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05/21/2013
On the Complexity Analysis of Randomized Block-Coordinate Descent Methods
In this paper we analyze the randomized block-coordinate descent (RBCD) ...
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03/14/2012