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Found 44 papers, 12 papers with code
DRO: A Python Library for Distributionally Robust Optimization in Machine Learning
We introduce dro, an open-source Python library for distributionally robust optimization (DRO) for regression and classification problems.
Extreme Conformal Prediction: Reliable Intervals for High-Impact Events
The advantages of this extreme conformal prediction method are illustrated in a simulation study and in an application to flood risk forecasting.
Stochastic Optimization with Optimal Importance Sampling
Importance Sampling (IS) is a widely used variance reduction technique for enhancing the efficiency of Monte Carlo methods, particularly in rare-event simulation and related applications.
Dissecting the Impact of Model Misspecification in Data-Driven Optimization
Data-driven optimization aims to translate a machine learning model into decision-making by optimizing decisions on estimated costs.
Prediction-Enhanced Monte Carlo: A Machine Learning View on Control Variate
For many complex simulation tasks spanning areas such as healthcare, engineering, and finance, Monte Carlo (MC) methods are invaluable due to their unbiased estimates and precise error quantification.
LLM Embeddings Improve Test-time Adaptation to Tabular $Y|X$-Shifts
Since it is impossible to generalize to a completely new and unknown domain, we study models that are easy to adapt to the target domain even with few labeled examples.
Bayesian Bandit Algorithms with Approximate Inference in Stochastic Linear Bandits
To fill this gap, we propose a theoretical framework to analyze the impact of approximate inference in stochastic linear bandits and conduct regret analysis on two Bayesian bandit algorithms, Linear Thompson sampling (LinTS) and the extension of Bayesian Upper Confidence Bound, namely Linear Bayesian Upper Confidence Bound (LinBUCB).
MallowsPO: Fine-Tune Your LLM with Preference Dispersions
Direct Preference Optimization (DPO) has recently emerged as a popular approach to improve reinforcement learning with human feedback (RLHF), leading to better techniques to fine-tune large language models (LLM).
Subsampled Ensemble Can Improve Generalization Tail Exponentially
Ensemble learning is a popular technique to improve the accuracy of machine learning models.
Learning from Sparse Offline Datasets via Conservative Density Estimation
Offline reinforcement learning (RL) offers a promising direction for learning policies from pre-collected datasets without requiring further interactions with the environment.
Resampling Stochastic Gradient Descent Cheaply for Efficient Uncertainty Quantification
Stochastic gradient descent (SGD) or stochastic approximation has been widely used in model training and stochastic optimization.
Pseudo-Bayesian Optimization
Its key idea is to use a surrogate model to approximate the objective and, importantly, quantify the associated uncertainty that allows a sequential search of query points that balance exploitation-exploration.
Smoothed $f$-Divergence Distributionally Robust Optimization
In data-driven optimization, sample average approximation (SAA) is known to suffer from the so-called optimizer's curse that causes an over-optimistic evaluation of the solution performance.
Optimizer's Information Criterion: Dissecting and Correcting Bias in Data-Driven Optimization
We develop a general bias correction approach, building on what we call Optimizer's Information Criterion (OIC), that directly approximates the first-order bias and does not require solving any additional optimization problems.
Efficient Uncertainty Quantification and Reduction for Over-Parameterized Neural Networks
Uncertainty quantification (UQ) is important for reliability assessment and enhancement of machine learning models.
Short-term Temporal Dependency Detection under Heterogeneous Event Dynamic with Hawkes Processes
Then we proposed a robust and computationally-efficient method modified from MLE that does not rely on the prior estimation of the heterogeneous intensity and is thus applicable in a data-limited regime (e. g., few-shot, no repeated observations).
Estimate-Then-Optimize versus Integrated-Estimation-Optimization versus Sample Average Approximation: A Stochastic Dominance Perspective
In this paper, we show that a reverse behavior appears when the model class is well-specified and there is sufficient data.
Hedging Complexity in Generalization via a Parametric Distributionally Robust Optimization Framework
We propose a simple approach in which the distribution of random perturbations is approximated using a parametric family of distributions.
Adaptive Data Fusion for Multi-task Non-smooth Optimization
We study the problem of multi-task non-smooth optimization that arises ubiquitously in statistical learning, decision-making and risk management.
Group Distributionally Robust Reinforcement Learning with Hierarchical Latent Variables
One key challenge for multi-task Reinforcement learning (RL) in practice is the absence of task indicators.
Evaluating Aleatoric Uncertainty via Conditional Generative Models
To overcome these restrictions, we study conditional generative models for aleatoric uncertainty estimation.
Test Against High-Dimensional Uncertainties: Accelerated Evaluation of Autonomous Vehicles with Deep Importance Sampling
In this work, we present Deep Importance Sampling (Deep IS) framework that utilizes a deep neural network to obtain an efficient IS that is on par with the state-of-the-art, capable of reducing the required sample size 43 times smaller than the naive sampling method to achieve 10% relative error and while producing an estimate that is much less conservative.
Optimal Regret Is Achievable with Bounded Approximate Inference Error: An Enhanced Bayesian Upper Confidence Bound Framework
Previous research only indicates a negative theoretical result: Thompson sampling could have a worst-case linear regret $\Omega(T)$ with a constant threshold on the inference error measured by one $\alpha$-divergence.
Efficient Calibration of Multi-Agent Simulation Models from Output Series with Bayesian Optimization
Multi-agent simulation is commonly used across multiple disciplines, specifically in artificial intelligence in recent years, which creates an environment for downstream machine learning or reinforcement learning tasks.
Certifiable Deep Importance Sampling for Rare-Event Simulation of Black-Box Systems
Rare-event simulation techniques, such as importance sampling (IS), constitute powerful tools to speed up challenging estimation of rare catastrophic events.
Quantifying Epistemic Uncertainty in Deep Learning
Uncertainty quantification is at the core of the reliability and robustness of machine learning.
Decision Tree Algorithms for MDP
In this paper, we show that even for problems with large state space, when the solution policy of the MDP can be represented by a tree-like structure, our proposed algorithm retrieves a tree of the solution policy of the MDP in computationally tractable time.
Generalization Bounds with Minimal Dependency on Hypothesis Class via Distributionally Robust Optimization
In contrast to the hypothesis class complexity in ERM, our DRO bounds depend on the ambiguity set geometry and its compatibility with the true loss function.
Scalable Safety-Critical Policy Evaluation with Accelerated Rare Event Sampling
Evaluating rare but high-stakes events is one of the main challenges in obtaining reliable reinforcement learning policies, especially in large or infinite state/action spaces where limited scalability dictates a prohibitively large number of testing iterations.
Calibrating Over-Parametrized Simulation Models: A Framework via Eligibility Set
Stochastic simulation aims to compute output performance for complex models that lack analytical tractability.
Learning Prediction Intervals for Regression: Generalization and Calibration
We study the generation of prediction intervals in regression for uncertainty quantification.
Conditional Coverage Estimation for High-quality Prediction Intervals
Deep learning has achieved state-of-the-art performance to generate high-quality prediction intervals (PIs) for uncertainty quantification in regression tasks.
Context-dependent Ranking and Selection under a Bayesian Framework
We consider a context-dependent ranking and selection problem.
Methodology
Efficient Learning for Clustering and Optimizing Context-Dependent Designs
We consider a simulation optimization problem for a context-dependent decision-making.
Decision Making
Methodology
Rare-Event Simulation for Neural Network and Random Forest Predictors
We study rare-event simulation for a class of problems where the target hitting sets of interest are defined via modern machine learning tools such as neural networks and random forests.
Convolution Bounds on Quantile Aggregation
We discuss relevant applications in risk management and economics.
Deep Probabilistic Accelerated Evaluation: A Robust Certifiable Rare-Event Simulation Methodology for Black-Box Safety-Critical Systems
Evaluating the reliability of intelligent physical systems against rare safety-critical events poses a huge testing burden for real-world applications.
Robust Importance Weighting for Covariate Shift
In many learning problems, the training and testing data follow different distributions and a particularly common situation is the \textit{covariate shift}.
Uncertainty Quantification and Exploration for Reinforcement Learning
We investigate statistical uncertainty quantification for reinforcement learning (RL) and its implications in exploration policy.
Evaluation Uncertainty in Data-Driven Self-Driving Testing
These Monte Carlo samples are generated from stochastic input models constructed based on real-world data.
A Versatile Approach to Evaluating and Testing Automated Vehicles based on Kernel Methods
The distribution used in sampling is pivotal to the performance of the method, but building a suitable distribution requires case-by-case analysis.
Robust and Parallel Bayesian Model Selection
Effective and accurate model selection is an important problem in modern data analysis.
A Bayesian Approach for Online Classifier Ensemble
We propose a Bayesian approach for recursively estimating the classifier weights in online learning of a classifier ensemble.
From Black-Scholes to Online Learning: Dynamic Hedging under Adversarial Environments
We consider a non-stochastic online learning approach to price financial options by modeling the market dynamic as a repeated game between the nature (adversary) and the investor.
