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Neural Information Processing Systems http://nips.cc/

On the positive side, we give anα = 1.01-approximate proper learner that uses O(1/( 2γ2)) samples (which is optimal) and runs in timepoly(d/) 2 O(1/γ

Insuch situations, weallowthealgorithm toreturn asmall listofcandidates such that one of the candidates is close to the true parameter.

Robust learning is a critical field that seeks to develop efficient algorithms that can recover an underlying model despite possibly malicious corruptions in the data. In recent decades, being able to deal with corrupted measurements has become of crucial importance.

Researchers have studied Stewart-Gough platforms, also known as Gough-Stewart platforms or hexapod platforms extensively for their inherent fine control characteristics. Their studies led to the potential deployment opportunities of Stewart-Gough Platforms in many critical applications such as the medical field, engineering machines, space research, electronic chip manufacturing, automobile manufacturing, etc. Some of these applications need micro and nano-level movement control in 3D space for the motions to be precise, complicated, and repeatable; a Stewart-Gough platform fulfills these challenges smartly. For this, the platform must be more accurate than the specified application accuracy level and thus proper calibration for a parallel robot is crucial. Forward kinematics-based calibration for these hexapod machines becomes unnecessarily complex and inverse kinematics complete this task with much ease. To experiment with different calibration techniques, various calibration approaches were implemented by using external instruments, constraining one or more motions of the system, and using extra sensors for auto or self-calibration. This survey paid attention to those key methodologies, their outcome, and important details related to inverse kinematic-based parallel robot calibrations. It was observed during this study that the researchers focused on improving the accuracy of the platform position and orientation considering the errors contributed by one source or multiple sources. The error sources considered are mainly kinematic and structural, in some cases, environmental factors also are reviewed, however, those calibrations are done under no-load conditions. This study aims to review the present state of the art in this field and highlight the processes and errors considered for the calibration of Stewart-Gough platforms.

We study high-dimensional sparse estimation tasks in a robust setting where a constant fraction of the dataset is adversarially corrupted. Specifically, we focus on the fundamental problems of robust sparse mean estimation and robust sparse PCA. We give the first practically viable robust estimators for these problems. In more detail, our algorithms are sample and computationally efficient and achieve near-optimal robustness guarantees. In contrast to prior provable algorithms which relied on the ellipsoid method, our algorithms use spectral techniques to iteratively remove outliers from the dataset. Our experimental evaluation on synthetic data shows that our algorithms are scalable and significantly outperform a range of previous approaches, nearly matching the best error rate without corruptions.

Rod Stewart had a few surprise guests at a recent concert in Charlotte, North Carolina. His old friend Ozzy Osbourne, the lead singer of Black Sabbath who died last month, was apparently beamed in from some kind of rock heaven, where he was reunited with other departed stars including Michael Jackson, Tina Turner and Bob Marley. The AI-generated images divided Stewart's fans. Some denounced them as disrespectful and distasteful; others found the tribute beautiful. At about the same time, another AI controversy erupted when Jim Acosta, a former CNN White House correspondent, interviewed a digital recreation of Joaquin Oliver, who was killed at the age of 17 in a 2018 high school shooting in Florida.