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08/15/2023
Near-Optimal Last-iterate Convergence of Policy Optimization in Zero-sum Polymatrix Markov games
Computing approximate Nash equilibria in multi-player general-sum Markov...
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07/10/2023
Enhancing Adversarial Robustness via Score-Based Optimization
Adversarial attacks have the potential to mislead deep neural network cl...
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07/04/2023
Training Energy-Based Models with Diffusion Contrastive Divergences
Energy-Based Models (EBMs) have been widely used for generative modeling...
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06/08/2023
Entropy-based Training Methods for Scalable Neural Implicit Sampler
Efficiently sampling from un-normalized target distributions is a fundam...
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05/29/2023
Diff-Instruct: A Universal Approach for Transferring Knowledge From Pre-trained Diffusion Models
Due to the ease of training, ability to scale, and high sample quality, ...
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04/29/2023
Semi-Infinitely Constrained Markov Decision Processes and Efficient Reinforcement Learning
We propose a novel generalization of constrained Markov decision process...
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04/15/2023
Stochastic Distributed Optimization under Average Second-order Similarity: Algorithms and Analysis
We study finite-sum distributed optimization problems with n-clients und...
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02/02/2023
Avoiding Model Estimation in Robust Markov Decision Processes with a Generative Model
Robust Markov Decision Processes (MDPs) are getting more attention for l...
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09/12/2022
Statistical Estimation of Confounded Linear MDPs: An Instrumental Variable Approach
In an Markov decision process (MDP), unobservable confounders may exist ...
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05/19/2022
Sparse Adversarial Attack in Multi-agent Reinforcement Learning
Cooperative multi-agent reinforcement learning (cMARL) has many real app...
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05/17/2022
On the Convergence of Policy in Unregularized Policy Mirror Descent
In this short note, we give the convergence analysis of the policy in th...
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04/06/2022
Federated Reinforcement Learning with Environment Heterogeneity
We study a Federated Reinforcement Learning (FedRL) problem in which n a...
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01/08/2022
Global Convergence Analysis of Deep Linear Networks with A One-neuron Layer
In this paper, we follow Eftekhari's work to give a non-local convergenc...
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10/16/2021
Greedy and Random Broyden's Methods with Explicit Superlinear Convergence Rates in Nonlinear Equations
In this paper, we propose the greedy and random Broyden's method for sol...
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05/31/2021
Memory-Efficient Differentiable Transformer Architecture Search
Differentiable architecture search (DARTS) is successfully applied in ma...
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05/09/2021
Directional Convergence Analysis under Spherically Symmetric Distribution
We consider the fundamental problem of learning linear predictors (i.e.,...
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05/09/2021
Non-asymptotic Performances of Robust Markov Decision Processes
In this paper, we study the non-asymptotic performance of optimal policy...
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04/12/2021
Meta-Regularization: An Approach to Adaptive Choice of the Learning Rate in Gradient Descent
We propose Meta-Regularization, a novel approach for the adaptive choice...
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03/15/2021
Lower Complexity Bounds of Finite-Sum Optimization Problems: The Results and Construction
The contribution of this paper includes two aspects. First, we study the...
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03/15/2021
DIPPA: An improved Method for Bilinear Saddle Point Problems
This paper studies bilinear saddle point problems min_xmax_y g(x) + x^⊤A...
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09/16/2020
Landscape of Sparse Linear Network: A Brief Investigation
Network pruning, or sparse network has a long history and practical sign...
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08/30/2020
Optimal Quantization for Batch Normalization in Neural Network Deployments and Beyond
Quantized Neural Networks (QNNs) use low bit-width fixed-point numbers f...
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08/09/2020
Intervention Generative Adversarial Networks
In this paper we propose a novel approach for stabilizing the training p...
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07/11/2020
An Asymptotically Optimal Multi-Armed Bandit Algorithm and Hyperparameter Optimization
The evaluation of hyperparameters, neural architectures, or data augment...
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10/21/2019
Communication Efficient Decentralized Training with Multiple Local Updates
Communication efficiency plays a significant role in decentralized optim...
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10/02/2019
Distillation ≈ Early Stopping? Harvesting Dark Knowledge Utilizing Anisotropic Information Retrieval For Overparameterized Neural Network
Distillation is a method to transfer knowledge from one model to another...
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09/13/2019
A Stochastic Proximal Point Algorithm for Saddle-Point Problems
We consider saddle point problems which objective functions are the aver...
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08/22/2019
A General Analysis Framework of Lower Complexity Bounds for Finite-Sum Optimization
This paper studies the lower bound complexity for the optimization probl...
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08/18/2019
Towards Better Generalization: BP-SVRG in Training Deep Neural Networks
Stochastic variance-reduced gradient (SVRG) is a classical optimization ...
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07/04/2019
On the Convergence of FedAvg on Non-IID Data
Federated learning enables a large amount of edge computing devices to l...
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05/28/2019
A Gram-Gauss-Newton Method Learning Overparameterized Deep Neural Networks for Regression Problems
First-order methods such as stochastic gradient descent (SGD) are curren...
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03/02/2019
A Unified Framework for Regularized Reinforcement Learning
We propose and study a general framework for regularized Markov decision...
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02/15/2019
Lipschitz Generative Adversarial Nets
In this paper we study the convergence of generative adversarial network...
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02/13/2019
Do Subsampled Newton Methods Work for High-Dimensional Data?
Subsampled Newton methods approximate Hessian matrices through subsampli...
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08/10/2018
Hierarchical Attention: What Really Counts in Various NLP Tasks
Attention mechanisms in sequence to sequence models have shown great abi...
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05/17/2018
Interpolatron: Interpolation or Extrapolation Schemes to Accelerate Optimization for Deep Neural Networks
In this paper we explore acceleration techniques for large scale nonconv...
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02/27/2017
A Unifying Framework for Convergence Analysis of Approximate Newton Methods
Many machine learning models are reformulated as optimization problems. ...
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08/16/2016
An Efficient Character-Level Neural Machine Translation
Neural machine translation aims at building a single large neural networ...
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01/31/2016
A Proximal Stochastic Quasi-Newton Algorithm
In this paper, we discuss the problem of minimizing the sum of two conve...
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11/18/2015
Wishart Mechanism for Differentially Private Principal Components Analysis
We propose a new input perturbation mechanism for publishing a covarianc...
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10/29/2015
Nonconvex Penalization in Sparse Estimation: An Approach Based on the Bernstein Function
In this paper we study nonconvex penalization using Bernstein functions ...
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10/26/2015
A Parallel algorithm for X-Armed bandits
The target of X-armed bandit problem is to find the global maximum of an...
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09/08/2015
A Scalable and Extensible Framework for Superposition-Structured Models
In many learning tasks, structural models usually lead to better interpr...
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12/26/2014
Adjusting Leverage Scores by Row Weighting: A Practical Approach to Coherent Matrix Completion
Low-rank matrix completion is an important problem with extensive real-w...
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10/03/2014
Group Orbit Optimization: A Unified Approach to Data Normalization
In this paper we propose and study an optimization problem over a matrix...
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12/17/2013
The Bernstein Function: A Unifying Framework of Nonconvex Penalization in Sparse Estimation
In this paper we study nonconvex penalization using Bernstein functions....
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12/17/2013
The Matrix Ridge Approximation: Algorithms and Applications
We are concerned with an approximation problem for a symmetric positive ...
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08/28/2013
Compound Poisson Processes, Latent Shrinkage Priors and Bayesian Nonconvex Penalization
In this paper we discuss Bayesian nonconvex penalization for sparse lear...
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07/22/2013
Kinetic Energy Plus Penalty Functions for Sparse Estimation
In this paper we propose and study a family of sparsity-inducing penalty...
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10/04/2012
