Cover Image

Optimization-Driven Adaptive Experimentation

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Che, Ethan, Jiang, Daniel R., Namkoong, Hongseok, Wang, Jimmy
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

AExGym: Benchmarks and Environments for Adaptive Experimentation
by: Wang, Jimmy, et al.
Published: (2024)

Differentiable Discrete Event Simulation for Queuing Network Control
by: Che, Ethan, et al.
Published: (2024)

Empirical Likelihood for Nonsmooth Functionals
by: Namkoong, Hongseok
Published: (2026)

Adaptive Elicitation of Latent Information Using Natural Language
by: Wang, Jimmy, et al.
Published: (2025)

Exchangeable Sequence Models Quantify Uncertainty Over Latent Concepts
by: Ye, Naimeng, et al.
Published: (2024)

A Planning Framework for Adaptive Labeling
by: Mittal, Daksh, et al.
Published: (2025)

QGym: Scalable Simulation and Benchmarking of Queuing Network Controllers
by: Chen, Haozhe, et al.
Published: (2024)

Data-Driven Stochastic Modeling Using Autoregressive Sequence Models: Translating Event Tables to Queueing Dynamics
by: Mittal, Daksh, et al.
Published: (2025)

A Broader View of Thompson Sampling
by: Qu, Yanlin, et al.
Published: (2025)

A Sensitivity Approach to Causal Inference Under Limited Overlap
by: Ma, Yuanzhe, et al.
Published: (2025)

Design and Scheduling of an AI-based Queueing System
by: Lee, Jiung, et al.
Published: (2024)

Distilled Thompson Sampling: Practical and Efficient Thompson Sampling via Imitation Learning
by: Namkoong, Hongseok, et al.
Published: (2020)

Minimax Optimal Estimation of Stability Under Distribution Shift
by: Namkoong, Hongseok, et al.
Published: (2022)

Benchmarking In-context Experiential Learning Through Repeated Product Recommendations
by: Yang, Gilbert, et al.
Published: (2025)

Rethinking Distribution Shifts: Empirical Analysis and Inductive Modeling for Tabular Data
by: Wang, Tianyu, et al.
Published: (2023)

DRO: A Python Library for Distributionally Robust Optimization in Machine Learning
by: Liu, Jiashuo, et al.
Published: (2025)

Active Exploration via Autoregressive Generation of Missing Data
by: Cai, Tiffany Tianhui, et al.
Published: (2024)

Data Mixture Optimization: A Multi-fidelity Multi-scale Bayesian Framework
by: Yen, Thomson, et al.
Published: (2025)

C-Learner: Constrained Learning for Causal Inference
by: Cai, Tiffany Tianhui, et al.
Published: (2024)

Evaluating Model Performance Under Worst-case Subpopulations
by: Li, Mike, et al.
Published: (2024)

Architectural and Inferential Inductive Biases For Exchangeable Sequence Modeling
by: Mittal, Daksh, et al.
Published: (2025)

Contextual Thompson Sampling via Generation of Missing Data
by: Zhang, Kelly W., et al.
Published: (2025)

LLM Embeddings Improve Test-time Adaptation to Tabular $Y|X$-Shifts
by: Zeng, Yibo, et al.
Published: (2024)

Stochastic Gradient Descent with Adaptive Data
by: Che, Ethan, et al.
Published: (2024)

Self-supervised Equality Embedded Deep Lagrange Dual for Approximate Constrained Optimization
by: Kim, Minsoo, et al.
Published: (2023)

SynthTools: A Framework for Scaling Synthetic Tools for Agent Development
by: Castellani, Tommaso, et al.
Published: (2025)

LLM Swiss Round: Aggregating Multi-Benchmark Performance via Competitive Swiss-System Dynamics
by: Liu, Jiashuo, et al.
Published: (2025)

Optimization of Epsilon-Greedy Exploration
by: Che, Ethan, et al.
Published: (2025)

PersonalLLM: Tailoring LLMs to Individual Preferences
by: Zollo, Thomas P., et al.
Published: (2024)

An Infinite-Width Analysis on the Jacobian-Regularised Training of a Neural Network
by: Kim, Taeyoung, et al.
Published: (2023)

Adaptive Simulation Experiment for LLM Policy Optimization
by: Hu, Mingjie, et al.
Published: (2026)

Does Alignment Tuning Really Break LLMs' Internal Confidence?
by: Oh, Hongseok, et al.
Published: (2024)

Optimizing Adaptive Experiments: A Unified Approach to Regret Minimization and Best-Arm Identification
by: Qin, Chao, et al.
Published: (2024)

Adaptive Exponential Integration for Stable Gaussian Mixture Black-Box Variational Inference
by: Che, Baojun, et al.
Published: (2026)

Adaptive Experimentation for Censored Survival Outcomes
by: Wang, Yuxin, et al.
Published: (2026)

Deep Neural Network-Driven Adaptive Filtering
by: Wang, Qizhen, et al.
Published: (2025)

Adaptive Threshold-Driven Continuous Greedy Method for Scalable Submodular Optimization
by: Rostami, Mohammadreza, et al.
Published: (2026)

Adaptive Experimentation When You Can't Experiment
by: Zhao, Yao, et al.
Published: (2024)

Adaptive Variance Reduction for Stochastic Optimization under Weaker Assumptions
by: Jiang, Wei, et al.
Published: (2024)

Dynamic Time-aware Continual User Representation Learning
by: Choi, Seungyoon, et al.
Published: (2025)