World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
30
Citations
6181
World Ranking
13895
National Ranking
222

Overview

Nicol N. Schraudolph is affiliated with the Dalle Molle Institute for Artificial Intelligence Research in Switzerland. Their research is primarily situated within the field of artificial intelligence, with a focus on advancing the theoretical and practical aspects of this domain.

Although specific details on recent papers, co-authors, publication venues, or books are not listed, their association with the Dalle Molle Institute highlights engagement with one of Europe's prominent centers dedicated to AI research.

Their work contributes to evolving topics and subfields within artificial intelligence, addressing challenges that intersect with machine learning, neural networks, and algorithmic optimization. These endeavors align with the broader goals of increasing efficiency and capability in computational models.

There is no available data on awards or honors, which might provide further insight into recognition within the research community. The absence of detailed publication records limits a more comprehensive evaluation of the scientist's output and impact.

Their research profile suggests a specialization in AI research that is likely interdisciplinary, given the nature of the Dalle Molle Institute's collaborative environment.

Best Publications

  • Learning precise timing with lstm recurrent networks

    Felix A. Gers;Nicol N. Schraudolph;Jürgen Schmidhuber

  • Graph Kernels

    S. V. N. Vishwanathan;Nicol N. Schraudolph;Risi Kondor;Karsten M. Borgwardt

  • A stochastic quasi-Newton method for online convex optimization

    Nicol N. Schraudolph;Jin Yu;Simon Günter

  • Dynamic Parameter Encoding for Genetic Algorithms

    Nicol N. Schraudolph;Richard K. Belew

  • Accelerating evolutionary algorithms with Gaussian process fitness function models

    D. Buche;N.N. Schraudolph;P. Koumoutsakos

  • Accelerated training of conditional random fields with stochastic gradient methods

    S. V. N. Vishwanathan;Nicol N. Schraudolph;Mark W. Schmidt;Kevin P. Murphy

  • Fast curvature matrix-vector products for second-order gradient descent

    Nicol N. Schraudolph

  • Local Gain Adaptation in Stochastic Gradient Descent

    Nicol N. Schraudolph

  • A Fast, Compact Approximation of the Exponential Function

    Nicol N. Schraudolph

  • Fast Computation of Graph Kernels

    Karsten M. Borgwardt;Nicol N. Schraudolph;S.v.n. Vishwanathan

  • Temporal Difference Learning of Position Evaluation in the Game of Go

    Nicol N. Schraudolph;Peter Dayan;Terrence J. Sejnowski

  • Fast Iterative Kernel Principal Component Analysis

    Simon Günter;Nicol N. Schraudolph;S. V. N. Vishwanathan

  • A Quasi-Newton Approach to Nonsmooth Convex Optimization Problems in Machine Learning

    Jin Yu;S.V.N. Vishwanathan;Simon Günter;Nicol N. Schraudolph

  • Graph Kernels

    S. V. N. Vishwanathan;Karsten M. Borgwardt;Imre Risi Kondor;Nicol N. Schraudolph

  • Empirical Entropy Manipulation for Real-World Problems

    Paul A. Viola;Nicol N. Schraudolph;Terrence J. Sejnowski

  • 3D hand tracking by rapid stochastic gradient descent using a skinning model

    M. Bray;E. Koller-Meier;P. Muller;L. Van Gool

  • A User's Guide to GAucsd 1.4

    N. N. Schraudolph

  • GRAPH KERNELS FOR DISEASE OUTCOME PREDICTION FROM PROTEIN-PROTEIN INTERACTION NETWORKS

    Karsten M Borgwardt;Hans-Peter Kriegel;S V N Vishwanathan;Nicol N Schraudolph

  • Reinforcement learning with self-modifying policies

    Juergen Schmidhuber;Jieyu Zhao;Nicol N. Schraudolph

  • Efficient Exact Inference in Planar Ising Models

    Nicol N. Schraudolph;Dmitry Kamenetsky

  • Dynamic Parameter Encoding for genetic algorithms

    Unknown

Frequent Co-Authors

S. V. N. Vishwanathan
S. V. N. Vishwanathan Purdue University West Lafayette
Terrence J. Sejnowski
Terrence J. Sejnowski Salk Institute for Biological Studies
Jürgen Schmidhuber
Jürgen Schmidhuber King Abdullah University of Science and Technology
Thore Graepel
Thore Graepel University College London
Karsten M. Borgwardt
Karsten M. Borgwardt Max Planck Institute of Biochemistry
Alexander J. Smola
Alexander J. Smola Amazon (United States)
Petros Koumoutsakos
Petros Koumoutsakos Harvard University
Yves Barral
Yves Barral ETH Zurich
Peter Dayan
Peter Dayan Max Planck Institute for Biological Cybernetics

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

A background in Computer Science opens doors to many related online degrees and exciting career pathways. Students frequently branch into specialized fields that offer flexible online study options, including engineering, physics, and data science.

For those interested in advanced technical expertise, exploring the cheapest online master's mechanical engineering programs is an excellent option. Mechanical engineering blends well with computer science, especially in areas like robotics and automation.

Additionally, a bachelor of science in physics online can be a strong alternative or complement for students fascinated by problem-solving and scientific research. Physics graduates are in high demand across tech, engineering, and research sectors.

Data science is another booming field. If you're interested in analytics or artificial intelligence, check out the cheapest data science degree programs available online.

Finally, for students drawn to electronics and digital systems, learn more about electrical engineering degree online admissions. This path offers excellent career prospects in diverse industries.

Best Scientists Citing Nicol N. Schraudolph