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Ali Abbas

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

5 papers
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5

AAAI Conference 2026 Conference Paper

Geometrically Inspired Kernel Machines for Collaborative Learning Beyond Gradient Descent (Abstract Reprint)

  • Mohit Kumar
  • Alexander Valentinitsch
  • Magdalena Fuchs
  • Mathias Brucker
  • Juliana Bowles
  • Adnan Husakovic
  • Ali Abbas
  • Bernhard A. Moser

This paper develops a novel mathematical framework for collaborative learning by means of geometrically inspired kernel machines which includes statements on the bounds of generalisation and approximation errors, and sample complexity. For classification problems, this approach allows us to learn bounded geometric structures around given data points and hence solve the global model learning problem in an efficient way by exploiting convexity properties of the related optimisation problem in a Reproducing Kernel Hilbert Space (RKHS). In this way, we can reduce classification problems to determining the closest bounded geometric structure from a given data point. Further advantages that come with our solution is that our approach does not require clients to perform multiple epochs of local optimisation using stochastic gradient descent, nor require rounds of communication between client/server for optimising the global model. We highlight that numerous experiments have shown that the proposed method is a competitive alternative to the state-of-the-art.

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JAIR Journal 2025 Journal Article

Geometrically Inspired Kernel Machines for Collaborative Learning Beyond Gradient Descent

  • Mohit Kumar
  • Alexander Valentinitsch
  • Magdalena Fuchs
  • Mathias Brucker
  • Juliana Bowles
  • Adnan Husakovic
  • Ali Abbas
  • Bernhard A. Moser

This paper develops a novel mathematical framework for collaborative learning by means of geometrically inspired kernel machines which includes statements on the bounds of generalisation and approximation errors, and sample complexity. For classification problems, this approach allows us to learn bounded geometric structures around given data points and hence solve the global model learning problem in an efficient way by exploiting convexity properties of the related optimisation problem in a Reproducing Kernel Hilbert Space (RKHS). In this way, we can reduce classification problems to determining the closest bounded geometric structure from a given data point. Further advantages that come with our solution is that our approach does not require clients to perform multiple epochs of local optimisation using stochastic gradient descent, nor require rounds of communication between client/server for optimising the global model. We highlight that numerous experiments have shown that the proposed method is a competitive alternative to the state-of-the-art.

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IJCAI Conference 2019 Conference Paper

SAGE: A Hybrid Geopolitical Event Forecasting System

  • Fred Morstatter
  • Aram Galstyan
  • Gleb Satyukov
  • Daniel Benjamin
  • Andres Abeliuk
  • Mehrnoosh Mirtaheri
  • KSM Tozammel Hossain
  • Pedro Szekely

Forecasting of geopolitical events is a notoriously difficult task, with experts failing to significantly outperform a random baseline across many types of forecasting events. One successful way to increase the performance of forecasting tasks is to turn to crowdsourcing: leveraging many forecasts from non-expert users. Simultaneously, advances in machine learning have led to models that can produce reasonable, although not perfect, forecasts for many tasks. Recent efforts have shown that forecasts can be further improved by ``hybridizing'' human forecasters: pairing them with the machine models in an effort to combine the unique advantages of both. In this demonstration, we present Synergistic Anticipation of Geopolitical Events (SAGE), a platform for human/computer interaction that facilitates human reasoning with machine models.

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ICRA Conference 2017 Conference Paper

A physics based model for twisted and coiled actuator

  • Ali Abbas
  • Jianguo Zhao

This paper presents the static and dynamic modeling for a recently discovered artificial muscle-twisted and coiled actuator (TCA). This actuator can generate large force and displacement; moreover, it is low-cost, easy to fabricate, and customizable. Since the discovery of TCA, it has been widely adopted for various robotic applications. Nevertheless, theoretical models to describe the static performance and dynamic response are underexplored. In this paper, we aim to model the statics and dynamics for TCA from physics perspective. Specifically, the developed model utilizes parameters related to the working principle and material properties of the actuator. Experiments are conducted to verify the proposed model, and the results demonstrate that the proposed model can predict the static performance and dynamic response for the actuator. Given the wide applications of TCA in robotics, the developed model will enable closed-loop control of robotic systems with TCAs to achieve precise motion.

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IROS Conference 2017 Conference Paper

Twisted and coiled sensor for shape estimation of soft robots

  • Ali Abbas
  • Jianguo Zhao

Soft robots with inherent compliance have been recently investigated intensively for locomotion or manipulations. A critical problem for soft robots is the capability to estimate their shapes to enable closed-loop control for precise motion. In this paper, we propose a new low-cost sensor that can be leveraged for shape estimation of soft robots. This sensor, recently discovered as an artificial muscle, can be conveniently fabricated from low-cost conductive sewing threads. We recently found that the resistance will increase if the fabricated sensor is elongated due to an external force [1]. Since the sensor is inherently soft, it can be embedded into soft robots to estimate the shape. We establish a physics-based model to predict the external force and the displacement if the resistance is given and experimentally validate its correctness. Moreover, to demonstrate the sensing capability, we embed the proposed sensor into soft materials and successfully measure two curvatures of a two-segment soft robot. Therefore, the proposed sensor has the potential to estimate complicated shapes of soft robots to enable closed-loop control.

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