:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Mittal, Daksh, Li, Ang, Yen, Tzu-Ching, Guetta, Daniel, Namkoong, Hongseok
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2503.01215
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Exchangeable Sequence Models Quantify Uncertainty Over Latent Concepts
by: Ye, Naimeng, 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)

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

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

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

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

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

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

Benchmarking In-context Experiential Learning Through Repeated Product Recommendations
by: Yang, Gilbert, 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)

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

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

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

Optimization-Driven Adaptive Experimentation
by: Che, Ethan, et al.
Published: (2024)

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

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

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

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

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

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

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

Geometric Inductive Biases of Deep Networks: The Role of Data and Architecture
by: Movahedi, Sajad, et al.
Published: (2024)

Temporal Task Diversity: Inductive Biases Under Non-Stationarity in Synthetic Sequence Modelling
by: Aswadi, Afiq Abdillah Effiezal, et al.
Published: (2026)

Language Models Need Inductive Biases to Count Inductively
by: Chang, Yingshan, et al.
Published: (2024)

Incorporating Inductive Biases to Energy-based Generative Models
by: Li, Yukun, et al.
Published: (2025)

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

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases
by: Romero, David W.
Published: (2024)

Priors in Time: Missing Inductive Biases for Language Model Interpretability
by: Lubana, Ekdeep Singh, et al.
Published: (2025)

Transformers Are Born Biased: Structural Inductive Biases at Random Initialization and Their Practical Consequences
by: Li, Siquan, et al.
Published: (2026)

Instilling Inductive Biases with Subnetworks
by: Zhang, Enyan, et al.
Published: (2023)

How Well Can a Long Sequence Model Model Long Sequences? Comparing Architechtural Inductive Biases on Long-Context Abilities
by: Huang, Jerry
Published: (2024)

Characterising the Inductive Biases of Neural Networks on Boolean Data
by: Mingard, Chris, et al.
Published: (2025)

Theoretical Analysis of Inductive Biases in Deep Convolutional Networks
by: Wang, Zihao, et al.
Published: (2023)

Clustering Inductive Biases with Unrolled Networks
by: Huml, Jonathan, et al.
Published: (2023)

LLM Generated Persona is a Promise with a Catch
by: Li, Ang, et al.
Published: (2025)

When Diffusion Models Memorize: Inductive Biases in Probability Flow of Minimum-Norm Shallow Neural Nets
by: Zeno, Chen, et al.
Published: (2025)

Enhancing the Inductive Biases of Graph Neural ODE for Modeling Dynamical Systems
by: Bishnoi, Suresh, et al.
Published: (2022)