Posters

Fairness-aware estimation of graphical models (GMs) tackles bias in GM estimations by integrating graph disparity error and a tailored loss function into multi-objective optimization, effectively miti…

FairnessWithoutHarm achieves fairer ML models without sacrificing accuracy by using an influence-guided active sampling method that doesn’t require sensitive training data.

Fairness in social influence maximization is achieved via optimal transport, optimizing both outreach and a new ‘mutual fairness’ metric that considers variability in outreach scenarios.

First non-wasteful algorithm achieving 1/2-approximation for class envy-freeness, class proportionality, and utilitarian social welfare in online class matching.

Fair Wasserstein Coresets (FWC) efficiently generates fair, representative subsets of large datasets for downstream machine learning tasks, improving fairness and utility.

Fair algorithms can leverage biased predictions to improve performance while guaranteeing fairness for all candidates.

This paper proposes a novel online bilateral trading algorithm maximizing the fair gain from trade and provides tight regret bounds under various settings.

Fair Kernel K-Means (FKKM) framework ensures fair data partitioning by integrating a novel fairness regularization term into the kernel k-means algorithm, extending this to multiple kernel settings fo…

Fair GLASSO ensures fair Gaussian graphical models by introducing novel bias metrics and a penalized maximum likelihood estimator to mitigate group biases in data.

FairBiNN, a novel bilevel neural network, achieves Pareto optimal solutions by simultaneously optimizing for accuracy and fairness, outperforming existing methods.

This paper presents novel, scalable algorithms for fair and efficient constrained resource allocation under uncertainty using robust and CVaR optimization.

This paper presents optimal fair mechanisms for dynamic auction design, maximizing seller revenue while guaranteeing minimum allocations to multiple buyer groups.

FACTORSim generates full, coded simulations from natural language descriptions, outperforming existing methods in accuracy and zero-shot transfer learning by using a factored POMDP representation.

FactorizePhys leverages Non-negative Matrix Factorization for a novel multidimensional attention mechanism (FSAM) to improve remote PPG signal extraction from videos.

This paper presents a novel neural network architecture that simultaneously learns to generate and segment images in an unsupervised manner, achieving accurate results across multiple datasets without…

FACT, a novel federated learning mechanism, eliminates free-riding and incentivizes truthful agent behavior by introducing a penalty system and a competitive environment, boosting model performance si…

Researchers dynamically integrate contrastive and supervised learning to overcome the modality imbalance problem in multimodal classification, significantly improving model performance.

Face2QR: A unified framework generates aesthetically pleasing, scannable QR codes that faithfully preserve facial features, solving the conflict between aesthetics, identity, and scannability.

F-OAL: Forward-only Online Analytic Learning achieves high accuracy and low memory usage in online class incremental learning by using a frozen encoder and recursive least squares to update a linear …

EZ-HOI: Efficient Zero-Shot HOI detection adapts Vision-Language Models (VLMs) for Human-Object Interaction (HOI) tasks using a novel prompt learning framework, achieving state-of-the-art performance …