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Related papers: Notes on the runtime of A* sampling

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This work considers the sample complexity of obtaining an $\varepsilon$-optimal policy in an average reward Markov Decision Process (AMDP), given access to a generative model (simulator). When the ground-truth MDP is weakly communicating,…

Machine Learning · Computer Science 2022-12-02 Jinghan Wang , Mengdi Wang , Lin F. Yang

In offline reinforcement learning (RL), the absence of active exploration calls for attention on the model robustness to tackle the sim-to-real gap, where the discrepancy between the simulated and deployed environments can significantly…

Machine Learning · Computer Science 2024-06-28 He Wang , Laixi Shi , Yuejie Chi

Let $P_n$ and $Q_n$ be two probability measures representing two different probabilistic models of some system (e.g., an $n$-particle equilibrium system, a set of random graphs with $n$ vertices, or a stochastic process evolving over a time…

Statistical Mechanics · Physics 2023-03-30 Hugo Touchette

We present a novel $Q$-learning algorithm tailored to solve distributionally robust Markov decision problems where the corresponding ambiguity set of transition probabilities for the underlying Markov decision process is a Wasserstein ball…

Machine Learning · Computer Science 2024-06-21 Ariel Neufeld , Julian Sester

We consider a simulation-based Ranking and Selection (R&S) problem with input uncertainty, where unknown input distributions can be estimated using input data arriving in batches of varying sizes over time. Each time a batch arrives,…

Optimization and Control · Mathematics 2022-09-05 Di Wu , Yuhao Wang , Enlu Zhou

We study fundamental theoretical aspects of probabilistic roadmaps (PRM) in the finite time (non-asymptotic) regime. In particular, we investigate how completeness and optimality guarantees of the approach are influenced by the underlying…

Data Structures and Algorithms · Computer Science 2019-09-24 Matthew Tsao , Kiril Solovey , Marco Pavone

We establish large deviation formulas for linear statistics on the $N$ transmission eigenvalues $\{T_i\}$ of a chaotic cavity, in the framework of Random Matrix Theory. Given any linear statistics of interest $A=\sum_{i=1}^N a(T_i)$, the…

Mesoscale and Nanoscale Physics · Physics 2015-05-14 Pierpaolo Vivo , Satya N. Majumdar , Oriol Bohigas

In this paper, we propose a new framework for designing fast parallel algorithms for fundamental statistical subset selection tasks that include feature selection and experimental design. Such tasks are known to be weakly submodular and are…

Machine Learning · Computer Science 2021-04-02 Sharon Qian , Yaron Singer

This article presents a novel, general, and effective simulation-inspired approach, called {\it repro samples method}, to conduct statistical inference. The approach studies the performance of artificial samples, referred to as {\it repro…

Methodology · Statistics 2022-06-15 Min-ge Xie , Peng Wang

We study the problem of estimating the optimal Q-function of $\gamma$-discounted Markov decision processes (MDPs) under the synchronous setting, where independent samples for all state-action pairs are drawn from a generative model at each…

Machine Learning · Statistics 2025-05-27 Mohammad Boveiri , Peyman Mohajerin Esfahani

Sampling algorithms play a pivotal role in probabilistic AI. However, verifying if a sampler program indeed samples from the claimed distribution is a notoriously hard problem. Provably correct testers like Barbarik, Teq, Flash, CubeProbe…

Data Structures and Algorithms · Computer Science 2025-12-09 Rishiraj Bhattacharyya , Sourav Chakraborty , Yash Pote , Uddalok Sarkar , Sayantan Sen

Stochastic Optimization (SO) is a classical approach for optimization under uncertainty that typically requires knowledge about the probability distribution of uncertain parameters. As the latter is often unknown, Distributionally Robust…

In many data analysis pipelines, a basic and time-consuming process is to produce join results and feed them into downstream tasks. Numerous enumeration algorithms have been developed for this purpose. To be a statistically meaningful…

Databases · Computer Science 2025-07-02 Pengyu Chen , Zizheng Guo , Jianwei Yang , Dongjing Miao

This work unifies pseudo-time and inexact regularization techniques for nonmonotone classes of partial differential equations, into a regularized pseudo-time framework. Convergence of the residual at the predicted rate is investigated…

Numerical Analysis · Mathematics 2016-11-29 Sara Pollock

We consider optimal route planning when the objective function is a general nonlinear and non-monotonic function. Such an objective models user behavior more accurately, for example, when a user is risk-averse, or the utility function needs…

Data Structures and Algorithms · Computer Science 2015-11-24 Ger Yang , Evdokia Nikolova

There are many high dimensional function classes that have fast agnostic learning algorithms when assumptions on the distribution of examples can be made, such as Gaussianity or uniformity over the domain. But how can one be confident that…

Machine Learning · Computer Science 2022-11-22 Ronitt Rubinfeld , Arsen Vasilyan

Subsampling is an efficient method to deal with massive data. In this paper, we investigate the optimal subsampling for linear quantile regression when the covariates are functions. The asymptotic distribution of the subsampling estimator…

Numerical Analysis · Mathematics 2022-05-06 Qian Yan , Hanyu Li , Chengmei Niu

We consider the sample complexity of learning with adversarial robustness. Most prior theoretical results for this problem have considered a setting where different classes in the data are close together or overlapping. Motivated by some…

Machine Learning · Computer Science 2023-01-19 Robi Bhattacharjee , Somesh Jha , Kamalika Chaudhuri

We consider the algorithm by Ferson et al. (Reliable computing 11(3), p. 207-233, 2005) designed for solving the NP-hard problem of computing the maximal sample variance over interval data, motivated by robust statistics (in fact, the…

Optimization and Control · Mathematics 2022-07-28 Miroslav Rada , Michal Černý , Ondřej Sokol

Denoising Diffusion Probabilistic Models (DDPMs) have emerged as powerful tools for generative modeling. However, their sequential computation requirements lead to significant inference-time bottlenecks. In this work, we utilize the…

Machine Learning · Computer Science 2025-08-08 Hengyuan Hu , Aniket Das , Dorsa Sadigh , Nima Anari
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