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Found 8 papers, 5 papers with code
COLEP: Certifiably Robust Learning-Reasoning Conformal Prediction via Probabilistic Circuits
In this work, we propose a certifiably robust learning-reasoning conformal prediction framework (COLEP) via probabilistic circuits, which comprise a data-driven learning component that trains statistical models to learn different semantic concepts, and a reasoning component that encodes knowledge and characterizes the relationships among the trained models for logic reasoning.
C-RAG: Certified Generation Risks for Retrieval-Augmented Language Models
Specifically, we provide conformal risk analysis for RAG models and certify an upper confidence bound of generation risks, which we refer to as conformal generation risk.
DiffAttack: Evasion Attacks Against Diffusion-Based Adversarial Purification
In particular, we propose a deviated-reconstruction loss at intermediate diffusion steps to induce inaccurate density gradient estimation to tackle the problem of vanishing/exploding gradients.
DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models
Yet, while the literature on the trustworthiness of GPT models remains limited, practitioners have proposed employing capable GPT models for sensitive applications such as healthcare and finance -- where mistakes can be costly.
Fairness in Federated Learning via Core-Stability
Nonetheless, the heterogeneity nature of distributed data makes it challenging to define and ensure fairness among local agents.
Certifying Some Distributional Fairness with Subpopulation Decomposition
In this paper, we first formulate the certified fairness of an ML model trained on a given data distribution as an optimization problem based on the model performance loss bound on a fairness constrained distribution, which is within bounded distributional distance with the training distribution.
Label-Assemble: Leveraging Multiple Datasets with Partial Labels
We discovered that learning from negative examples facilitates both computer-aided disease diagnosis and detection.
MgSvF: Multi-Grained Slow vs. Fast Framework for Few-Shot Class-Incremental Learning
As a challenging problem, few-shot class-incremental learning (FSCIL) continually learns a sequence of tasks, confronting the dilemma between slow forgetting of old knowledge and fast adaptation to new knowledge.
