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Found 49 papers, 9 papers with code
Semiparametric Differential Graph Models
In many cases of network analysis, it is more attractive to study how a network varies under different conditions than an individual static network.
Communication-efficient Distributed Estimation and Inference for Transelliptical Graphical Models
In detail, the proposed method distributes the $d$-dimensional data of size $N$ generated from a transelliptical graphical model into $m$ worker machines, and estimates the latent precision matrix on each worker machine based on the data of size $n=N/m$.
Speeding Up Latent Variable Gaussian Graphical Model Estimation via Nonconvex Optimizations
In order to speed up the estimation of the sparse plus low-rank components, we propose a sparsity constrained maximum likelihood estimator based on matrix factorization, and an efficient alternating gradient descent algorithm with hard thresholding to solve it.
Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization
Furthermore, for the first time we prove the global convergence guarantee for variance reduced stochastic gradient Langevin dynamics (SVRG-LD) to the almost minimizer within $\tilde O\big(\sqrt{n}d^5/(\lambda^4\epsilon^{5/2})\big)$ stochastic gradient evaluations, which outperforms the gradient complexities of GLD and SGLD in a wide regime.
Uncertainty Assessment and False Discovery Rate Control in High-Dimensional Granger Causal Inference
Causal inference among high-dimensional time series data proves an important research problem in many fields.
Allocation Problems in Ride-Sharing Platforms: Online Matching with Offline Reusable Resources
Prior work addresses online bipartite matching markets, where agents arrive over time and are dynamically matched to a known set of disposable resources.
Speeding Up Latent Variable Gaussian Graphical Model Estimation via Nonconvex Optimization
In order to speed up the estimation of the sparse plus low-rank components, we propose a sparsity constrained maximum likelihood estimator based on matrix factorization and an efficient alternating gradient descent algorithm with hard thresholding to solve it.
Third-order Smoothness Helps: Even Faster Stochastic Optimization Algorithms for Finding Local Minima
We propose stochastic optimization algorithms that can find local minima faster than existing algorithms for nonconvex optimization problems, by exploiting the third-order smoothness to escape non-degenerate saddle points more efficiently.
Stochastic Variance-Reduced Hamilton Monte Carlo Methods
We propose a fast stochastic Hamilton Monte Carlo (HMC) method, for sampling from a smooth and strongly log-concave distribution.
Stochastic Variance-Reduced Cubic Regularized Newton Method
At the core of our algorithm is a novel semi-stochastic gradient along with a semi-stochastic Hessian, which are specifically designed for cubic regularization method.
Attenuate Locally, Win Globally: An Attenuation-based Framework for Online Stochastic Matching with Timeouts
On the upper bound side, we show that this framework, combined with a black-box adapted from Bansal et al., (Algorithmica, 2012), yields an online algorithm which nearly doubles the ratio to 0. 46.
Stochastic Nested Variance Reduction for Nonconvex Optimization
We study finite-sum nonconvex optimization problems, where the objective function is an average of $n$ nonconvex functions.
Finding Local Minima via Stochastic Nested Variance Reduction
For general stochastic optimization problems, the proposed $\text{SNVRG}^{+}+\text{Neon2}^{\text{online}}$ achieves $\tilde{O}(\epsilon^{-3}+\epsilon_H^{-5}+\epsilon^{-2}\epsilon_H^{-3})$ gradient complexity, which is better than both $\text{SVRG}+\text{Neon2}^{\text{online}}$ (Allen-Zhu and Li, 2017) and Natasha2 (Allen-Zhu, 2017) in certain regimes.
Covariate Adjusted Precision Matrix Estimation via Nonconvex Optimization
We propose a nonconvex estimator for the covariate adjusted precision matrix estimation problem in the high dimensional regime, under sparsity constraints.
Continuous and Discrete-time Accelerated Stochastic Mirror Descent for Strongly Convex Functions
We provide a second-order stochastic differential equation (SDE), which characterizes the continuous-time dynamics of accelerated stochastic mirror descent (ASMD) for strongly convex functions.
Sample Efficient Stochastic Variance-Reduced Cubic Regularization Method
The proposed algorithm achieves a lower sample complexity of Hessian matrix computation than existing cubic regularization based methods.
Third-order Smoothness Helps: Faster Stochastic Optimization Algorithms for Finding Local Minima
We propose stochastic optimization algorithms that can find local minima faster than existing algorithms for nonconvex optimization problems, by exploiting the third-order smoothness to escape non-degenerate saddle points more efficiently.
Stochastic Nested Variance Reduced Gradient Descent for Nonconvex Optimization
We study finite-sum nonconvex optimization problems, where the objective function is an average of $n$ nonconvex functions.
An Improved Convergence Analysis of Stochastic Variance-Reduced Policy Gradient
We revisit the stochastic variance-reduced policy gradient (SVRPG) method proposed by Papini et al. (2018) for reinforcement learning.
Sample Efficient Policy Gradient Methods with Recursive Variance Reduction
Improving the sample efficiency in reinforcement learning has been a long-standing research problem.
Mix and Match: Markov Chains & Mixing Times for Matching in Rideshare
Rideshare platforms such as Uber and Lyft dynamically dispatch drivers to match riders' requests.
Stochastic Gradient Hamiltonian Monte Carlo Methods with Recursive Variance Reduction
Stochastic Gradient Hamiltonian Monte Carlo (SGHMC) algorithms have received increasing attention in both theory and practice.
Rank Aggregation via Heterogeneous Thurstone Preference Models
By allowing different noise distributions, the proposed HTM model maintains the generality of Thurstone's original framework, and as such, also extends the Bradley-Terry-Luce (BTL) model for pairwise comparisons to heterogeneous populations of users.
A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation
Q-learning with neural network function approximation (neural Q-learning for short) is among the most prevalent deep reinforcement learning algorithms.
Balancing the Tradeoff between Profit and Fairness in Rideshare Platforms During High-Demand Hours
Moreover, if in such a scenario, the assignment of requests to drivers (by the platform) is made only to maximize profit and/or minimize wait time for riders, requests of a certain type (e. g. from a non-popular pickup location, or to a non-popular drop-off location) might never be assigned to a driver.
Double Explore-then-Commit: Asymptotic Optimality and Beyond
In this paper, we show that a variant of ETC algorithm can actually achieve the asymptotic optimality for multi-armed bandit problems as UCB-type algorithms do and extend it to the batched bandit setting.
MOTS: Minimax Optimal Thompson Sampling
Thompson sampling is one of the most widely used algorithms for many online decision problems, due to its simplicity in implementation and superior empirical performance over other state-of-the-art methods.
A Finite Time Analysis of Two Time-Scale Actor Critic Methods
In this work, we provide a non-asymptotic analysis for two time-scale actor-critic methods under non-i. i. d.
Faster Convergence of Stochastic Gradient Langevin Dynamics for Non-Log-Concave Sampling
We provide a new convergence analysis of stochastic gradient Langevin dynamics (SGLD) for sampling from a class of distributions that can be non-log-concave.
A Finite-Time Analysis of Two Time-Scale Actor-Critic Methods
In this work, we provide a non-asymptotic analysis for two time-scale actor-critic methods under non-i. i. d.
Neural Contextual Bandits with Deep Representation and Shallow Exploration
We study a general class of contextual bandits, where each context-action pair is associated with a raw feature vector, but the reward generating function is unknown.
A Unified Model for the Two-stage Offline-then-Online Resource Allocation
In this paper, we propose a unified model which incorporates both offline and online resource allocation into a single framework.
Trading the System Efficiency for the Income Equality of Drivers in Rideshare
Rigorous online competitive ratio analysis is offered to demonstrate the flexibility and efficiency of our online algorithms in balancing the two conflicting goals, promotions of fairness and profit.
Fairness Maximization among Offline Agents in Online-Matching Markets
Examples of online and offline agents include keywords (online) and sponsors (offline) in Google Advertising; workers (online) and tasks (offline) in Amazon Mechanical Turk (AMT); riders (online) and drivers (offline when restricted to a short time window) in ridesharing.
Adaptive Sampling for Heterogeneous Rank Aggregation from Noisy Pairwise Comparisons
In heterogeneous rank aggregation problems, users often exhibit various accuracy levels when comparing pairs of items.
Equity Promotion in Online Resource Allocation
We consider online resource allocation under a typical non-profit setting, where limited or even scarce resources are administered by a not-for-profit organization like a government.
Finite-Time Regret of Thompson Sampling Algorithms for Exponential Family Multi-Armed Bandits
We study the regret of Thompson sampling (TS) algorithms for exponential family bandits, where the reward distribution is from a one-dimensional exponential family, which covers many common reward distributions including Bernoulli, Gaussian, Gamma, Exponential, etc.
Langevin Monte Carlo for Contextual Bandits
Existing Thompson sampling-based algorithms need to construct a Laplace approximation (i. e., a Gaussian distribution) of the posterior distribution, which is inefficient to sample in high dimensional applications for general covariance matrices.
Equity Promotion in Public Transportation
Specifically, we aim to design a strategy of allocating a given limited budget to different candidate programs such that the overall social equity is maximized, which is defined as the minimum covering ratio among all pre-specified protected groups of households (based on race, income, etc.).
Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning
In particular, we show that, despite restricting each agent's attention to only its $\kappa$-hop neighborhood, the agents are able to learn a policy with an optimality gap that decays polynomially in $\kappa$.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Queer In AI: A Case Study in Community-Led Participatory AI
We present Queer in AI as a case study for community-led participatory design in AI.
Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo
One of the key shortcomings of existing Thompson sampling algorithms is the need to perform a Gaussian approximation of the posterior distribution, which is not a good surrogate in most practical settings.
Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits
To overcome these limitations, we propose a novel DRO approach that employs the Wasserstein distance instead.
Optimal Batched Best Arm Identification
Based on Tri-BBAI, we further propose the almost optimal batched best arm identification (Opt-BBAI) algorithm, which is the first algorithm that achieves the near-optimal sample and batch complexity in the non-asymptotic setting (i. e., $\delta>0$ is arbitrarily fixed), while enjoying the same batch and sample complexity as Tri-BBAI when $\delta$ tends to zero.
Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization
Yet, existing analyses of signSGD rely on assuming that data are sampled with replacement in each iteration, contradicting the practical implementation where data are randomly reshuffled and sequentially fed into the algorithm.
Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs
Specifically, we assume there is a local reward for each hyperedge, and the reward of the joint arm is the sum of these local rewards.
Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation
We provide the first study on online DRMDPs with function approximation for off-dynamics RL.
Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning
Distributionally robust offline reinforcement learning (RL), which seeks robust policy training against environment perturbation by modeling dynamics uncertainty, calls for function approximations when facing large state-action spaces.
Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning
This is the first theoretical result for randomized exploration in cooperative MARL.
