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Found 31 papers, 7 papers with code
An Economic Solution to Copyright Challenges of Generative AI
Generative artificial intelligence (AI) systems are trained on large data corpora to generate new pieces of text, images, videos, and other media.
Provable Multi-Party Reinforcement Learning with Diverse Human Feedback
Our work \textit{initiates} the theoretical study of multi-party RLHF that explicitly models the diverse preferences of multiple individuals.
Can AI Be as Creative as Humans?
With the rise of advanced generative AI models capable of tasks once reserved for human creativity, the study of AI's creative potential becomes imperative for its responsible development and application.
Learning and Forgetting Unsafe Examples in Large Language Models
As the number of large language models (LLMs) released to the public grows, there is a pressing need to understand the safety implications associated with these models learning from third-party custom finetuning data.
Prompt Risk Control: A Rigorous Framework for Responsible Deployment of Large Language Models
The recent explosion in the capabilities of large language models has led to a wave of interest in how best to prompt a model to perform a given task.
Analyzing and Mitigating Object Hallucination in Large Vision-Language Models
Large vision-language models (LVLMs) have shown remarkable abilities in understanding visual information with human languages.
Distribution-Free Statistical Dispersion Control for Societal Applications
Explicit finite-sample statistical guarantees on model performance are an important ingredient in responsible machine learning.
How Does Information Bottleneck Help Deep Learning?
In this paper, we provide the first rigorous learning theory for justifying the benefit of information bottleneck in deep learning by mathematically relating information bottleneck to generalization errors.
Last-Layer Fairness Fine-tuning is Simple and Effective for Neural Networks
As machine learning has been deployed ubiquitously across applications in modern data science, algorithmic fairness has become a great concern.
HappyMap: A Generalized Multi-calibration Method
Fairness is captured by incorporating demographic subgroups into the class of functions~$\mathcal{C}$.
Understanding Multimodal Contrastive Learning and Incorporating Unpaired Data
We show that the algorithm can detect the ground-truth pairs and improve performance by fully exploiting unpaired datasets.
Quantile Risk Control: A Flexible Framework for Bounding the Probability of High-Loss Predictions
In this work, we propose a flexible framework to produce a family of bounds on quantiles of the loss distribution incurred by a predictor.
Reinforcement Learning with Stepwise Fairness Constraints
AI methods are used in societally important settings, ranging from credit to employment to housing, and it is crucial to provide fairness in regard to algorithmic decision making.
Investigating Fairness Disparities in Peer Review: A Language Model Enhanced Approach
We distill several insights from our analysis on study the peer review process with the help of large LMs.
Robustness Implies Generalization via Data-Dependent Generalization Bounds
This paper proves that robustness implies generalization via data-dependent generalization bounds.
FIFA: Making Fairness More Generalizable in Classifiers Trained on Imbalanced Data
Specifically, FIFA encourages both classification and fairness generalization and can be flexibly combined with many existing fair learning methods with logits-based losses.
The Power of Contrast for Feature Learning: A Theoretical Analysis
Contrastive learning has achieved state-of-the-art performance in various self-supervised learning tasks and even outperforms its supervised counterpart.
An Unconstrained Layer-Peeled Perspective on Neural Collapse
We prove that gradient flow on this model converges to critical points of a minimum-norm separation problem exhibiting neural collapse in its global minimizer.
Understanding Dynamics of Nonlinear Representation Learning and Its Application
Representation learning allows us to automatically discover suitable representations from raw sensory data.
Adversarial Training Helps Transfer Learning via Better Representations
Recent works empirically demonstrate that adversarial training in the source data can improve the ability of models to transfer to new domains.
How Gradient Descent Separates Data with Neural Collapse: A Layer-Peeled Perspective
In this paper, we derive a landscape analysis to the surrogate model to study the inductive bias of the neural features and parameters from neural networks with cross-entropy.
When and How Mixup Improves Calibration
In addition, we study how Mixup improves calibration in semi-supervised learning.
Toward Better Generalization Bounds with Locally Elastic Stability
Given that, we propose \emph{locally elastic stability} as a weaker and distribution-dependent stability notion, which still yields exponential generalization bounds.
Towards Understanding the Dynamics of the First-Order Adversaries
An acknowledged weakness of neural networks is their vulnerability to adversarial perturbations to the inputs.
How Does Mixup Help With Robustness and Generalization?
For robustness, we show that minimizing the Mixup loss corresponds to approximately minimizing an upper bound of the adversarial loss.
Interpreting Robust Optimization via Adversarial Influence Functions
Robust optimization has been widely used in nowadays data science, especially in adversarial training.
Decision-Aware Conditional GANs for Time Series Data
We introduce the decision-aware time-series conditional generative adversarial network (DAT-CGAN) as a method for time-series generation.
Representation via Representations: Domain Generalization via Adversarially Learned Invariant Representations
We study an adversarial loss function for $k$ domains and precisely characterize its limiting behavior as $k$ grows, formalizing and proving the intuition, backed by experiments, that observing data from a larger number of domains helps.
Improving Adversarial Robustness via Unlabeled Out-of-Domain Data
In this work, we investigate how adversarial robustness can be enhanced by leveraging out-of-domain unlabeled data.
Architecture Selection via the Trade-off Between Accuracy and Robustness
We provide a general framework for characterizing the trade-off between accuracy and robustness in supervised learning.
