Accuracy
Achievable Fairness on Y our Data With Utility Guarantees
Secondly, these works do not account for finite-sampling errors in the obtained curve.
No Free Lunch in LLM Watermarking: Trade-offs in Watermarking Design Choices
Advances in generative models have made it possible for AI-generated text, code, and images to mirror human-generated content in many applications. W atermark-ing, a technique that aims to embed information in the output of a model to verify its source, is useful for mitigating the misuse of such AI-generated content. However, we show that common design choices in LLM watermarking schemes make the resulting systems surprisingly susceptible to attack--leading to fundamental trade-offs in robustness, utility, and usability. To navigate these trade-offs, we rigorously study a set of simple yet effective attacks on common watermarking systems, and propose guidelines and defenses for LLM watermarking in practice.
Human Expertise in Algorithmic Prediction
Algorithmic indistinguishability yields a natural test for assessing whether experts incorporate this kind of "side
Graph Classification via Reference Distribution Learning: Theory and Practice
This work introduces Graph Reference Distribution Learning (GRDL), an efficient and accurate graph classification method. GRDL treats each graph's latent node embeddings given by GNN layers as a
A Disparity Metric Definitions 566 A.1 Observational Metrics
U 2 U that influences all of the variables U influences. Figure 5: Example of step one in the marginalisation, taken from Evans [22]. In this section we analyse the datasets presented in Le Quy et al. For each bias we provide a justification of our decision. Therefore we drop them from the analysis. Diabetes For this dataset, the goal is to predict if a patient will be readmitted in the next 30 days.
The Fragility of Fairness: Causal Sensitivity Analysis for Fair Machine Learning
Fairness metrics are a core tool in the fair machine learning literature (FairML), used to determine that ML models are, in some sense, "fair."
AED: Adaptable Error Detection for Few-shot Imitation Policy Jia-Fong Y eh 1 Kuo-Han Hung 1, Pang-Chi Lo1, Chi-Ming Chung 1
We introduce a new task called Adaptable Error Detection (AED), which aims to identify behavior errors in few-shot imitation (FSI) policies based on visual observations in novel environments. The potential to cause serious damage to surrounding areas limits the application of FSI policies in real-world scenarios.
Proximal Causal Inference with Text Data
Data-driven decision making relies on estimating the effect of interventions, i.e. causal effect estimation . For example, a doctor must decide which medicine she will give her patient, ideally the one with the greatest effect on positive outcomes.
Weak Supervision Performance Evaluation via Partial Identification Felipe Maia Polo Department of Statistics University of Michigan Subha Maity
In previous work, for example, Fréchet bounds were studied in the financial context ( e.g., see