Papers
Found 25 papers
Sorted by: Newest FirstU-Statistic Reduction: Higher-Order Accurate Risk Control and Statistical-Computational Trade-Off
Meijia Shao, Dong Xia, Yuan Zhang
Simulation-Based, Finite-Sample Inference for Privatized Data
Jordan Awan, Zhanyu Wang
Geometric Ergodicity of Trans-Dimensional Markov Chain Monte Carlo Algorithms
Qian Qin
Statistical and Computational Efficiency for Smooth Tensor Estimation with Unknown Permutations
Chanwoo Lee, Miaoyan Wang
Algorithmic Stability Implies Training-Conditional Coverage for Distribution-Free Prediction Methods
Ruiting Liang, Rina Foygel Barber
Prominent Roles of Conditionally Invariant Components in Domain Adaptation: Theory and Algorithms
Keru Wu, Yuansi Chen, Wooseok Ha et al.
Domain adaptation (DA) is a statistical learning problem that arises when the distribution of the source data used to train a model differs from that ...
Near-Optimal Nonconvex-Strongly-Convex Bilevel Optimization with Fully First-Order Oracles
Lesi Chen, Yaohua Ma, Jingzhao Zhang
In this work, we consider bilevel optimization when the lower-level problem is strongly convex. Recent works show that with a Hessian-vector product (...
On Global and Local Convergence of Iterative Linear Quadratic Optimization Algorithms for Discrete Time Nonlinear Control
Vincent Roulet, Siddhartha Srinivasa, Maryam Fazel et al.
A classical approach for solving discrete time nonlinear control on a finite horizon consists in repeatedly minimizing linear quadratic approximations...
A Decentralized Proximal Gradient Tracking Algorithm for Composite Optimization on Riemannian Manifolds
Lei Wang, Le Bao, Xin Liu
This paper focuses on minimizing a smooth function combined with a nonsmooth regularization term on a compact Riemannian submanifold embedded in the E...
Distributed Stochastic Bilevel Optimization: Improved Complexity and Heterogeneity Analysis
Youcheng Niu, Jinming Xu, Ying Sun et al.
This paper considers solving a class of nonconvex-strongly-convex distributed stochastic bilevel optimization (DSBO) problems with personalized inner-...
Optimization Over a Probability Simplex
James Chok, Geoffrey M. Vasil
We propose a new iteration scheme, the Cauchy-Simplex, to optimize convex problems over the probability simplex $\{w\in\mathbb{R}^n\ |\ \sum_i w_i=1\ ...
Instability, Computational Efficiency and Statistical Accuracy
Nhat Ho, Koulik Khamaru, Raaz Dwivedi et al.
Many statistical estimators are defined as the fixed point of a data-dependent operator, with estimators based on minimizing a cost function being an ...
On Adaptive Stochastic Optimization for Streaming Data: A Newton's Method with O(dN) Operations
Antoine Godichon-Baggioni, Nicklas Werge
Stochastic optimization methods face new challenges in the realm of streaming data, characterized by a continuous flow of large, high-dimensional data...
Adjusted Expected Improvement for Cumulative Regret Minimization in Noisy Bayesian Optimization
Shouri Hu, Haowei Wang, Zhongxiang Dai et al.
The expected improvement (EI) is one of the most popular acquisition functions for Bayesian optimization (BO) and has demonstrated good empirical perf...
Extremal graphical modeling with latent variables via convex optimization
Sebastian Engelke, Armeen Taeb
Extremal graphical models encode the conditional independence structure of multivariate extremes and provide a powerful tool for quantifying the risk ...
Statistical Inference of Constrained Stochastic Optimization via Sketched Sequential Quadratic Programming
Sen Na, Michael Mahoney
We consider online statistical inference of constrained stochastic nonlinear optimization problems. We apply the Stochastic Sequential Quadratic Progr...
Accelerating optimization over the space of probability measures
Shi Chen, Qin Li, Oliver Tse et al.
The acceleration of gradient-based optimization methods is a subject of significant practical and theoretical importance, particularly within machine ...
Riemannian Bilevel Optimization
Jiaxiang Li, Shiqian Ma
In this work, we consider the bilevel optimization problem on Riemannian manifolds. We inspect the calculation of the hypergradient of such problems o...
Two-Timescale Gradient Descent Ascent Algorithms for Nonconvex Minimax Optimization
Tianyi Lin, Chi Jin, Michael I. Jordan
We provide a unified analysis of two-timescale gradient descent ascent (TTGDA) for solving structured nonconvex minimax optimization problems in the f...
Efficiently Escaping Saddle Points in Bilevel Optimization
Minhui Huang, Xuxing Chen, Kaiyi Ji et al.
Bilevel optimization is one of the fundamental problems in machine learning and optimization. Recent theoretical developments in bilevel optimization ...
Optimal clustering by Lloyd’s algorithm for low-rank mixture model
Zhongyuan Lyu, Dong Xia
Unbiased and consistent nested sampling via sequential Monte Carlo
Robert Salomone, others
Towards a turnkey approach for unbiased Monte Carlo estimation of smooth functions of expectations
Nicolas Chopin, others
Abstract
Sequential Monte Carlo testing by betting
Lasse Fischer, Aaditya Ramdas
Bayesian penalized empirical likelihood and Markov Chain Monte Carlo sampling
Jinyuan Chang, others