Search Results for author: Yehezkel S. Resheff

Found 8 papers, 1 papers with code

Explainable AI and Adoption of Financial Algorithmic Advisors: an Experimental Study

no code implementations5 Jan 2021 Daniel Ben David, Yehezkel S. Resheff, Talia Tron

We study whether receiving advice from either a human or algorithmic advisor, accompanied by five types of Local and Global explanation labelings, has an effect on the readiness to adopt, willingness to pay, and trust in a financial AI consultant.

Decision Making

Paired-Consistency: An Example-Based Model-Agnostic Approach to Fairness Regularization in Machine Learning

no code implementations7 Aug 2019 Yair Horesh, Noa Haas, Elhanan Mishraky, Yehezkel S. Resheff, Shir Meir Lador

As AI systems develop in complexity it is becoming increasingly hard to ensure non-discrimination on the basis of protected attributes such as gender, age, and race.

BIG-bench Machine Learning Fairness +1

Fusing Multifaceted Transaction Data for User Modeling and Demographic Prediction

no code implementations19 Dec 2017 Yehezkel S. Resheff, Moni Shahar

Inferring user characteristics such as demographic attributes is of the utmost importance in many user-centric applications.

Every Untrue Label is Untrue in its Own Way: Controlling Error Type with the Log Bilinear Loss

1 code implementation20 Apr 2017 Yehezkel S. Resheff, Amit Mandelbaum, Daphna Weinshall

Deep learning has become the method of choice in many application domains of machine learning in recent years, especially for multi-class classification tasks.

Multi-class Classification

Online Trajectory Segmentation and Summary With Applications to Visualization and Retrieval

no code implementations24 Jul 2016 Yehezkel S. Resheff

Trajectory segmentation is the process of subdividing a trajectory into parts either by grouping points similar with respect to some measure of interest, or by minimizing a global objective function.

Optimized Linear Imputation

no code implementations17 Nov 2015 Yehezkel S. Resheff, Daphna Weinshall

Since most data analysis and statistical methods do not handle gracefully missing values, the first step in the analysis requires the imputation of missing values.

Imputation

Topic Modeling of Behavioral Modes Using Sensor Data

no code implementations16 Nov 2015 Yehezkel S. Resheff, Shay Rotics, Ran Nathan, Daphna Weinshall

A common use of accelerometer data is for supervised learning of behavioral modes.

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