PhD Seminars VI
Nicholas Halliwell (Wimmics)
Avoiding Far-Fetched Counterfactuals to Explain Automated Decisions
A popular way to explain a decision made by a black-box model is via a counterfactual explanation: for a given input, find the closest counterfactual input that leads to the desired outcome. Here we raise the issue that state-of-the-art counterfactual explanation techniques can easily propose very implausible counterfactuals. A generated counterfactual may have feature values that cannot exist together, e.g., a 20 year old employee with 30 years of work experience. Indeed, these techniques ignore the distribution of the inputs. To overcome this issue, we define a data-aware distance when searching for counterfactuals. This distance is defined using binary trees learned from the data to form counterfactual explanations with realistic feature values. Using both real and simulated datasets, we show that this approach produces counterfactual explanations closer on average to training data than current state-of-the-art methods.
Minh Hoang LE (Eurocom)
An Additional Correction to Robistify DoA-based 2D Positioning Algorithm
Direction of arrival (DoA) estimation is crucial to improve the performance of communications systems, which has been greatly improved thanks to multi-antenna techniques leading to much more accurate results in localization. Unlike the range-based ones, the direction-based positioning algorithms estimate the unknown position by the measured angle whose value must be predefined in an interval of 2π-length. Noisy measurements with values near the edge of this interval can lead to drastic estimation errors, making the convergence of iterative procedures much more challenging. In this paper, we propose an iterative Maximum Likelihood (ML) estimator for positioning estimation. Our procedure is based on the atan2 function, which has the 2π-long codomain to map the DoA. Moreover, a novel mechanism to make the estimation near the edges much more robust, an additional correction scheme is proposed to robustify the final estimates. Simulation results show significant performance improvement compared to the methods with no correction.
- mars 15, 2021, 10:00 am