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Found 18 papers, 9 papers with code
PPA-Game: Characterizing and Learning Competitive Dynamics Among Online Content Creators
We introduce the Proportional Payoff Allocation Game (PPA-Game) to model how agents, akin to content creators on platforms like YouTube and TikTok, compete for divisible resources and consumers' attention.
On the Out-Of-Distribution Generalization of Multimodal Large Language Models
We investigate the generalization boundaries of current Multimodal Large Language Models (MLLMs) via comprehensive evaluation under out-of-distribution scenarios and domain-specific tasks.
Flatness-Aware Minimization for Domain Generalization
However, we reveal that Adam is not necessarily the optimal choice for the majority of current DG methods and datasets.
Competing for Shareable Arms in Multi-Player Multi-Armed Bandits
In reality, agents often have to learn and maximize the rewards of the resources at the same time.
Rethinking the Evaluation Protocol of Domain Generalization
This paper examines the risks of test data information leakage from two aspects of the current evaluation protocol: supervised pretraining on ImageNet and oracle model selection.
Gradient Norm Aware Minimization Seeks First-Order Flatness and Improves Generalization
Yet the current definition of flatness discussed in SAM and its follow-ups are limited to the zeroth-order flatness (i. e., the worst-case loss within a perturbation radius).
Model Agnostic Sample Reweighting for Out-of-Distribution Learning
The overfitting issue is addressed by considering a bilevel formulation to search for the sample reweighting, in which the generalization complexity depends on the search space of sample weights instead of the model size.
Stable Learning via Sparse Variable Independence
The problem of covariate-shift generalization has attracted intensive research attention.
Product Ranking for Revenue Maximization with Multiple Purchases
In this paper, we assume that each consumer can purchase multiple products at will.
NICO++: Towards Better Benchmarking for Domain Generalization
Most current evaluation methods for domain generalization (DG) adopt the leave-one-out strategy as a compromise on the limited number of domains.
Towards Domain Generalization in Object Detection
Despite the striking performance achieved by modern detectors when training and test data are sampled from the same or similar distribution, the generalization ability of detectors under unknown distribution shifts remains hardly studied.
Regulatory Instruments for Fair Personalized Pricing
Personalized pricing is a business strategy to charge different prices to individual consumers based on their characteristics and behaviors.
A Theoretical Analysis on Independence-driven Importance Weighting for Covariate-shift Generalization
Afterward, we prove that under ideal conditions, independence-driven importance weighting algorithms could identify the variables in this set.
Towards Out-Of-Distribution Generalization: A Survey
This paper represents the first comprehensive, systematic review of OOD generalization, encompassing a spectrum of aspects from problem definition, methodological development, and evaluation procedures, to the implications and future directions of the field.
Towards Unsupervised Domain Generalization
Domain generalization (DG) aims to help models trained on a set of source domains generalize better on unseen target domains.
Deep Stable Learning for Out-Of-Distribution Generalization
Approaches based on deep neural networks have achieved striking performance when testing data and training data share similar distribution, but can significantly fail otherwise.
Ranked #28 on
Domain Generalization
on VLCS
Sample Balancing for Improving Generalization under Distribution Shifts
We propose to address this problem by removing the dependencies between features via reweighting training samples, which results in a more balanced distribution and helps deep models get rid of spurious correlations and, in turn, concentrate more on the true connection between features and labels.
Algorithmic Decision Making with Conditional Fairness
In practice, there frequently exist a certain set of variables we term as fair variables, which are pre-decision covariates such as users' choices.
