70 citations · 293 across the 70 of their papers we have counts for
20 papers · 1 filter
ArcMark: Distortion-Free Multi-Byte LLM Watermark via Optimal Transport
Atefeh Gilani, Sajani Vithana, Carol Xuan Long +3
Watermarking is an important tool for promoting the responsible use of large language models (LLMs). Existing watermarks insert a signal into generated tokens that either flags LLM…
Regretful Decisions under Label Noise
Sujay Nagaraj, Yang Liu, Flavio P. Calmon +1
Machine learning models are routinely used to support decisions that affect individuals -- be it to screen a patient for a serious illness or to gauge their response to treatment.…
Interpreting CLIP with Sparse Linear Concept Embeddings (SpLiCE)
Usha Bhalla, Alex Oesterling, Suraj Srinivas +2
CLIP embeddings have demonstrated remarkable performance across a wide range of multimodal applications. However, these high-dimensional, dense vector representations are not easil…
Multi-Group Fairness Evaluation via Conditional Value-at-Risk Testing
Lucas Monteiro Paes, Ananda Theertha Suresh, Alex Beutel +2
Machine learning (ML) models used in prediction and classification tasks may display performance disparities across population groups determined by sensitive attributes (e.g., race…
Fair Machine Unlearning: Data Removal while Mitigating Disparities
Alex Oesterling, Jiaqi Ma, Flavio P. Calmon +1
The Right to be Forgotten is a core principle outlined by regulatory frameworks such as the EU's General Data Protection Regulation (GDPR). This principle allows individuals to req…
Arbitrariness Lies Beyond the Fairness-Accuracy Frontier
Carol Xuan Long, Hsiang Hsu, Wael Alghamdi +1
Machine learning tasks may admit multiple competing models that achieve similar performance yet produce conflicting outputs for individual samples -- a phenomenon known as predicti…
Adapting Fairness Interventions to Missing Values
Raymond Feng, Flavio P. Calmon, Hao Wang
Missing values in real-world data pose a significant and unique challenge to algorithmic fairness. Different demographic groups may be unequally affected by missing data, and the s…
Gaussian Max-Value Entropy Search for Multi-Agent Bayesian Optimization
Haitong Ma, Tianpeng Zhang, Yixuan Wu +2
We study the multi-agent Bayesian optimization (BO) problem, where multiple agents maximize a black-box function via iterative queries. We focus on Entropy Search (ES), a sample-ef…
Arbitrary Decisions are a Hidden Cost of Differentially Private Training
Bogdan Kulynych, Hsiang Hsu, Carmela Troncoso +1
Mechanisms used in privacy-preserving machine learning often aim to guarantee differential privacy (DP) during model training. Practical DP-ensuring training methods use randomizat…
Aleatoric and Epistemic Discrimination: Fundamental Limits of Fairness Interventions
Hao Wang, Luxi He, Rui Gao +1
Machine learning (ML) models can underperform on certain population groups due to choices made during model development and bias inherent in the data. We categorize sources of disc…
Bottlenecks CLUB: Unifying Information-Theoretic Trade-offs Among Complexity, Leakage, and Utility
Behrooz Razeghi, Flavio P. Calmon, Deniz Gunduz +1
Bottleneck problems are an important class of optimization problems that have recently gained increasing attention in the domain of machine learning and information theory. They ar…
Beyond Adult and COMPAS: Fairness in Multi-Class Prediction
Wael Alghamdi, Hsiang Hsu, Haewon Jeong +4
We consider the problem of producing fair probabilistic classifiers for multi-class classification tasks. We formulate this problem in terms of "projecting" a pre-trained (and pote…
Rashomon Capacity: A Metric for Predictive Multiplicity in Classification
Hsiang Hsu, Flavio du Pin Calmon
Predictive multiplicity occurs when classification models with statistically indistinguishable performances assign conflicting predictions to individual samples. When used for deci…
Fairness without Imputation: A Decision Tree Approach for Fair Prediction with Missing Values
Haewon Jeong, Hao Wang, Flavio P. Calmon
We investigate the fairness concerns of training a machine learning model using data with missing values. Even though there are a number of fairness intervention methods in the lit…
CPR: Classifier-Projection Regularization for Continual Learning
Sungmin Cha, Hsiang Hsu, Taebaek Hwang +2
We propose a general, yet simple patch that can be applied to existing regularization-based continual learning methods called classifier-projection regularization (CPR). Inspired b…
To Split or Not to Split: The Impact of Disparate Treatment in Classification
Hao Wang, Hsiang Hsu, Mario Diaz +1
Disparate treatment occurs when a machine learning model yields different decisions for individuals based on a sensitive attribute (e.g., age, sex). In domains where prediction acc…
Predictive Multiplicity in Classification
Charles T. Marx, Flavio du Pin Calmon, Berk Ustun
Prediction problems often admit competing models that perform almost equally well. This effect challenges key assumptions in machine learning when competing models assign conflicti…
Optimized Score Transformation for Consistent Fair Classification
Dennis Wei, Karthikeyan Natesan Ramamurthy, Flavio du Pin Calmon
This paper considers fair probabilistic binary classification where the outputs of primary interest are predicted probabilities, commonly referred to as scores. We formulate the pr…
Repairing without Retraining: Avoiding Disparate Impact with Counterfactual Distributions
Hao Wang, Berk Ustun, Flavio P. Calmon
When the performance of a machine learning model varies over groups defined by sensitive attributes (e.g., gender or ethnicity), the performance disparity can be expressed in terms…
Generalizing Correspondence Analysis for Applications in Machine Learning
Hsiang Hsu, Salman Salamatian, Flavio P. Calmon
Correspondence analysis (CA) is a multivariate statistical tool used to visualize and interpret data dependencies by finding maximally correlated embeddings of pairs of random vari…