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Fred Roosta
- Associate Professor
- School of Mathematics and Physics, University of Queensland, Australia
- Email:
fred.roostauq.edu.au
- Office:
Priestley Building (67) - Room 754
- Phone: +61 7 336 53259
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News
2024
- February, 2024: Our paper on an adaptive gradient descent appeared in the Transactions on Machine Learning Research (TMLR).
- January, 2024: Our new paper investigates conjugate direction methods, such as conjugate gradient (CG) and conjugate residual (CR), across a wide range of settings, including inconsistent ones. In particular, we demonstrate that the widely celebrated but more complex MINRES method is essentially equivalent to the much simpler CR method!
- January, 2024: In our new paper, we developed a new efficient algorithm for approximating leverage scores of a tall matrix, with applications to big time series data.
2023
- December, 2023: I will be serving as an area chair for the 41st International Conference on Machine Learning (ICML, 2024).
- November, 2023: A new paper on a variant of gradient descent in which the step-size is automatically tuned using a novel local first-order smoothness oracle, which generalizes the Lipschitz continuous gradients smoothness condition.
- November, 2023: A new paper on PAC-Bayesian bound for a general class of models, characterizing factors which influence generalization performance in the interpolating regime.
- October, 2023: I will be serving on the program committee of the 2025 SIAM Annual Meeting, to be held in Montreal, Canada, July 26 - August 2, 2025.
- October, 2023: In this paper, we show that for inconsistent linear systems, it is possible to obtain the pseudo-inverse solution in a variety of settings using a straightforward one-dimensional projection method.
- September, 2023: I will be serving as an area chair for the 12th International Conference on Learning Representations (ICLR 2024).
- August, 2023: Our recent paper on the iteration and operation complexity analysis of non-convex Newton-MR algorithm with inexact Hessian information is now available.
- July, 2023: In our new paper, we develop a (Bayesian) information criterion for interpolating overparameterized models that, among other things, captures the sharpness (flat minima) heuristic commonly used in deep learning.
- May, 2023: I will be serving on the program committee of the 2024 SIAM Conference on the Mathematics of Data Science (SIAM MDS), to be held October 21-25, 2024, in Atlanta, Georgia, USA.
- May, 2023: Our paper on uncertainty quantification of deep learning models applied to oncology has appeared in Nature's Scientific Reports.
- March, 2023: Our paper on double descent phenomenon in Gaussian processes has been accepted in ICML 2023.
- March, 2023: I will be serving as an area chair for the 37th Conference on Neural Information Processing Systems (NeurIPS) 2023.
2022
- December, 2022: I will be serving as an area chair for the 40th International Conference on Machine Learning (ICML) 2023.
- October, 2022: New paper on double descent phenomenon in Gaussian processes.
- September, 2022: Congratulations to my outgoing PhD student, Yang Liu, for landing a postdoc position at the Oxford Mathematical Institute as part of the Centre for Intelligent Multidimensional Data Analysis (CIMDA).
- August, 2022: Our paper on the complexity analysis of non-convex variants of Newton-MR is now available.
- July, 2022: Our paper on a variant of the Newton-CG method with inexact gradient and Hessian information has been accepted to IMA Journal of Numerical Analysis (IMAJNA).
- July, 2022: Our paper on a variant of the Newton's method with MINRES as sub-problem solver for invex optimization has been accepted to EURO Journal on Computational Optimization (EJCO).
- June, 2022: Our paper on negative curvature detection and monotonicity properties of MINRES has been accepted to SIAM Journal on Optimization (SIOPT).
- April, 2022: I will be serving on the organizing committee of the SIAM Conference on Optimization (OP23), to be held May 30-June 3, 2023, in Seattle, Washington, USA.
- March, 2022: I will be serving as an area chair for the 36th Conference on Neural Information Processing Systems (NeurIPS) 2022.
2021
- December, 2021: I will be serving as an area chair for the 39th International Conference on Machine Learning (ICML) 2022.
- November, 2021: Our
new
paper on a novel invexifying regualrization framework for non-linear least squares problems is available.
- September, 2021: Our
new
paper on complexity analysis of variants of Newton-CG algorithm with inexact gradient and Hessian for non-convex optimization is available.
- January, 2021: With Albert S. Berahas (University of Michigan), we will have a two part mini-symposium on "Beyond First Order Methods in Machine Learning" during the EUROPT 2021, the 18th international workshop on continuous optimization.
2020
- July, 2020: Our paper on
Newton-ADMM, a distributed GPU-accelerated second-order
optimization method, has been accepted in the Proceedings of
the ACM/IEEE Supercomputing Conference (SC20) - 18%
acceptance rate.
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June, 2020:
Our
paper on central limit theorems and concentration
inequalities for out-of-sample extensions of the adjacency and
Laplacian spectral embeddings has been accepted to the Journal
of Machine Learning Research (JMLR), with minor revision.
- June, 2020: Our
paper on distributed non-convex Newton-type optimization
methods has been accepted in
ICML, 2020! Congratulations to my PhD student
Rixon
Crane for his second paper in an A* conference!
- February, 2020: Our
recent
paper on stochastic normalizing flows, an extension of
continuous normalizing flows for maximum likelihood estimation
and variational inference, using stochastic differential
equations is available.
- February, 2020: Our
new
paper on studying Gaussian processes arising from
infinitely wide neural networks with ELU and GELU activations
as well as analysing the fixed-point dynamics of iterated
kernels is available.
- January, 2020: Our
paper
on the theory and application of the reproducing Stein kernel
for a posteriori correction of Monte-Carlo sampling algorithms
is now available on arXiv.
- January, 2020: Our
paper
on the theoretical and practical properties of Monte-Carlo
sampling algorithms by implicit discretizations of Langevin
SDE has been accepted to the Journal of Machine Learning
Research (JMLR), with minor revision.
2019
- December, 2019: Our
paper
on an empirical study of second-order optimization methods in
deep learning has been accepted to the SIAM International
Conference on Data Mining (SDM20) -
19%
acceptance rate.
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December, 2019:
Our new
paper,
which extends the existing results on Gaussian process
iterpretation of multi-layer perceptrons to richer families of
priors, is available.
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November, 2019:
Our new
paper on the application of RandNLA and leverage score
sampling within the context of big-data time series is now
available.
- November, 2019:
I will be serving as a senior program committee member for the
29th International Joint Conference on Artificial Intelligence
(IJCAI 2020).
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October, 2019:
Our paper on central limit theorems and concentration
inequalities for out-of-sample extensions of the adjacency and
Laplacian spectral embeddings is now
available.
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September, 2019:
Our paper on stability analysis of Newton-MR with respect to
Hessian approximations is now
available.
- September, 2019:
DINGO Does Vancouver! Our paper on
distributed Newton-type methods for optimization of
invex objectives has been accepted to NeurIPS 2019!
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June, 2019: Congratulations to my PhD
student, Rixon Crane, for being awarded the Best Poster Award
at
AMSI Optimise this year.
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June, 2019: I was selected as a top 5%
reviewer for ICML 2019.
- May, 2019: After 17 months and 21 days, our
paper on non-convex Newton-type methods with inexact
Hessian information has been accepted to Mathematical
Programming.
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May, 2019: Our
paper
on the invariance kernels of multilayer perceptrons during
training has been accepted to the International Joint
Conference on Artificial Intelligence (IJCAI
2019) -
18%
acceptance rate.
-
April, 2019: Our
paper
on the theoretical and practical properties of Monte-Carlo
sampling algorithms by implicit discretizations of Langevin
SDE is now available on arXiv.
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