:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Xue, Songkai, Sun, Yuekai
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2412.04346
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Learning the Distribution Map in Reverse Causal Performative Prediction
by: Bracale, Daniele, et al.
Published: (2024)

Algorithmic Fairness in Performative Policy Learning: Escaping the Impossibility of Group Fairness
by: Somerstep, Seamus, et al.
Published: (2024)

Minimax Regret Learning for Data with Heterogeneous Subgroups
by: Mo, Weibin, et al.
Published: (2024)

Likelihood-Free Estimation for Spatiotemporal Hawkes processes with missing data and application to predictive policing
by: Das, Pramit, et al.
Published: (2025)

Microfoundation Inference for Strategic Prediction
by: Bracale, Daniele, et al.
Published: (2024)

Weak Supervision Performance Evaluation via Partial Identification
by: Polo, Felipe Maia, et al.
Published: (2023)

Learning to Choose or Choosing to Learn: Best-of-N vs. Supervised Fine-Tuning for Bit String Generation
by: Somerstep, Seamus, et al.
Published: (2025)

Maximin Relative Improvement: Fair Learning as a Bargaining Problem
by: Han, Jiwoo, et al.
Published: (2026)

LUCF-Net: Lightweight U-shaped Cascade Fusion Network for Medical Image Segmentation
by: Sun, Songkai, et al.
Published: (2024)

Prompt Exploration with Prompt Regression
by: Feffer, Michael, et al.
Published: (2024)

Learning In Reverse Causal Strategic Environments With Ramifications on Two Sided Markets
by: Somerstep, Seamus, et al.
Published: (2024)

Revenue Maximization Under Sequential Price Competition Via The Estimation Of s-Concave Demand Functions
by: Bracale, Daniele, et al.
Published: (2025)

Dynamic Pricing in the Linear Valuation Model using Shape Constraints
by: Bracale, Daniele, et al.
Published: (2025)

Limitations of refinement methods for weak to strong generalization
by: Somerstep, Seamus, et al.
Published: (2025)

Robust Length Prediction: A Perspective from Heavy-Tailed Prompt-Conditioned Distributions
by: Wang, Jing, et al.
Published: (2026)

Beyond In-Distribution Performance: A Cross-Dataset Study of Trajectory Prediction Robustness
by: Yao, Yue, et al.
Published: (2025)

Sloth: scaling laws for LLM skills to predict multi-benchmark performance across families
by: Polo, Felipe Maia, et al.
Published: (2024)

Fusing Models with Complementary Expertise
by: Wang, Hongyi, et al.
Published: (2023)

Multi-Distribution Robust Conformal Prediction
by: Yang, Yuqi, et al.
Published: (2026)

A transfer learning framework for weak-to-strong generalization
by: Somerstep, Seamus, et al.
Published: (2024)

Robust Conformal Prediction under Distribution Shift via Physics-Informed Structural Causal Model
by: Xu, Rui, et al.
Published: (2024)

Understanding the Robustness of Multi-modal Contrastive Learning to Distribution Shift
by: Xue, Yihao, et al.
Published: (2023)

MoE-Compression: How the Compression Error of Experts Affects the Inference Accuracy of MoE Model?
by: Ma, Songkai, et al.
Published: (2025)

ODP-Bench: Benchmarking Out-of-Distribution Performance Prediction
by: Yu, Han, et al.
Published: (2025)

Aligners: Decoupling LLMs and Alignment
by: Ngweta, Lilian, et al.
Published: (2024)

Distribution-Free Robust Predict-Then-Optimize in Function Spaces
by: Patel, Yash, et al.
Published: (2026)

tinyBenchmarks: evaluating LLMs with fewer examples
by: Polo, Felipe Maia, et al.
Published: (2024)

Bridging Human and LLM Judgments: Understanding and Narrowing the Gap
by: Polo, Felipe Maia, et al.
Published: (2025)

Robust Validation: Confident Predictions Even When Distributions Shift
by: Cauchois, Maxime, et al.
Published: (2020)

Distributionally Robust Set Representation Learning Under Inference-Time Element Corruption
by: Chen, Yankai, et al.
Published: (2026)

ProFL: Performative Robust Optimal Federated Learning
by: Zheng, Xue, et al.
Published: (2024)

Robust Load Prediction of Power Network Clusters Based on Cloud-Model-Improved Transformer
by: Jiang, Cheng, et al.
Published: (2024)

Exploring the Noise Robustness of Online Conformal Prediction
by: Xi, Huajun, et al.
Published: (2025)

Distributional Offline Policy Evaluation with Predictive Error Guarantees
by: Wu, Runzhe, et al.
Published: (2023)

Robust Calibration For Improved Weather Prediction Under Distributional Shift
by: Gilda, Sankalp, et al.
Published: (2024)

Robust Predictive Modeling Under Unseen Data Distribution Shifts: A Methodological Commentary
by: Duan, Hanyu, et al.
Published: (2025)

DistMatch: Adaptive Binning via Distribution Matching for Robust Sequential Conformal Prediction
by: Menadjiev, Enver, et al.
Published: (2026)

Collaborative Performance Prediction for Large Language Models
by: Zhang, Qiyuan, et al.
Published: (2024)

Mixed-feature Logistic Regression Robust to Distribution Shifts
by: Sun, Qingshi, et al.
Published: (2025)

Wasserstein-regularized Conformal Prediction under General Distribution Shift
by: Xu, Rui, et al.
Published: (2025)