6 papers
Zero-Shot Active Feature Acquisition via LLM-Elicitation
Binyamin Perets, Natalie Mendelson, Shiran Vainberg +3
Active feature acquisition (AFA) sequentially selects which features to observe to reach a classification or ranking decision. Its central limitation is reliance on large amount of…
Finite Resources False Discovery Rate Control in Structured Hypothesis Spaces
Binyamin Perets, Shie Mannor
Scientific discovery relies on large-scale hypothesis testing. However, the capacity to identify true discoveries while controlling false discovery faces major challenges: obtainin…
Controlling False Discovery in Arbitrarily Structured Hypothesis Spaces via Reproducing Kernels
Binyamin Perets, Shie Mannor
Large-scale hypothesis testing is central to modern science, where controlling the False Discovery Rate (FDR) has become the standard approach to managing false positives across ma…
Efficient Fairness-Performance Pareto Front Computation
Mark Kozdoba, Binyamin Perets, Shie Mannor
There is a well known intrinsic trade-off between the fairness of a representation and the performance of classifiers derived from the representation. Due to the complexity of opti…
Sobolev Space Regularised Pre Density Models
Mark Kozdoba, Binyamin Perets, Shie Mannor
We propose a new approach to non-parametric density estimation that is based on regularizing a Sobolev norm of the density. This method is statistically consistent, and makes the i…
Learning Hidden Markov Models When the Locations of Missing Observations are Unknown
Binyamin Perets, Mark Kozdoba, Shie Mannor
The Hidden Markov Model (HMM) is one of the most widely used statistical models for sequential data analysis. One of the key reasons for this versatility is the ability of HMM to d…