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Found 20 papers, 10 papers with code
On the Benefits of Over-parameterization for Out-of-Distribution Generalization
We demonstrate that in this scenario, further increasing the model's parameterization can significantly reduce the OOD loss.
Do CLIPs Always Generalize Better than ImageNet Models?
The performance drops from the common to counter groups quantify the reliance of models on spurious features (i. e., backgrounds) to predict the animals.
Arithmetic Control of LLMs for Diverse User Preferences: Directional Preference Alignment with Multi-Objective Rewards
Additionally, DPA models user preferences as directions (i. e., unit vectors) in the reward space to achieve user-dependent preference control.
The Instinctive Bias: Spurious Images lead to Hallucination in MLLMs
In this paper, we identify a typical class of inputs that baffles MLLMs, which consist of images that are highly relevant but inconsistent with answers, causing MLLMs to suffer from hallucination.
R-Tuning: Teaching Large Language Models to Refuse Unknown Questions
This approach is formalized by first identifying the knowledge gap between parametric knowledge and the instruction tuning data.
Continuous Invariance Learning
To address this challenge, we then propose Continuous Invariance Learning (CIL), which extracts invariant features across continuously indexed domains.
Spurious Feature Diversification Improves Out-of-distribution Generalization
Contrary to the conventional wisdom that focuses on learning invariant features for better OOD performance, our findings suggest that incorporating a large number of diverse spurious features weakens their individual contributions, leading to improved overall OOD generalization performance.
Mitigating the Alignment Tax of RLHF
Building on the analysis and the observation that averaging different layers of the transformer leads to significantly different reward-tax trade-offs, we propose Adaptive Model Averaging (AMA) to adaptively find various combination ratios of model layers.
Optimal Sample Selection Through Uncertainty Estimation and Its Application in Deep Learning
Our proposed method, COPS (unCertainty based OPtimal Sub-sampling), is designed to minimize the expected loss of a model trained on subsampled data.
What is Essential for Unseen Goal Generalization of Offline Goal-conditioned RL?
In this paper, we study out-of-distribution (OOD) generalization of offline GCRL both theoretically and empirically to identify factors that are important.
Active Prompting with Chain-of-Thought for Large Language Models
For this purpose, we propose a solution to the key problem of determining which questions are the most important and helpful ones to annotate from a pool of task-specific queries.
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.
Probabilistic Bilevel Coreset Selection
The goal of coreset selection in supervised learning is to produce a weighted subset of data, so that training only on the subset achieves similar performance as training on the entire dataset.
Stable Learning via Sparse Variable Independence
The problem of covariate-shift generalization has attracted intensive research attention.
Particle-based Variational Inference with Preconditioned Functional Gradient Flow
With the popularity of Stein variational gradient descent (SVGD), the focus of particle-based VI algorithms has been on the properties of functions in Reproducing Kernel Hilbert Space (RKHS) to approximate the gradient flow.
Self-Guided Noise-Free Data Generation for Efficient Zero-Shot Learning
In this paradigm, the synthesized data from the PLM acts as the carrier of knowledge, which is used to train a task-specific model with orders of magnitude fewer parameters than the PLM, achieving both higher performance and efficiency than prompt-based zero-shot learning methods on PLMs.
ZIN: When and How to Learn Invariance Without Environment Partition?
When data are divided into distinct environments according to the heterogeneity, recent invariant learning methods have proposed to learn robust and invariant models based on this environment partition.
Black-box Prompt Learning for Pre-trained Language Models
Particularly, instead of fine-tuning the model in the cloud, we adapt PLMs by prompt learning, which efficiently optimizes only a few parameters of the discrete prompts.
Bayesian Invariant Risk Minimization
Generalization under distributional shift is an open challenge for machine learning.
Homologies of path complexes and digraphs
In this paper we introduce a path complex that can be regarded as a generalization of the notion of a simplicial complex.
Combinatorics Algebraic Topology
