Portrait de Antonios Valkanas

Antonios Valkanas

Doctorat - McGill
Superviseur⋅e principal⋅e
Mark Coates
Sujets de recherche
Apprentissage automatique appliqué
Apprentissage en ligne
Apprentissage profond
Réseaux de neurones en graphes
Systèmes de recommandation
Site web
Google Scholar

Publications

SKOLR: Structured Koopman Operator Linear RNN for Time-Series Forecasting
Yitian Zhang
Liheng Ma
Antonios Valkanas
Boris Oreshkin
Mark Coates
Koopman operator theory provides a framework for nonlinear dynamical system analysis and time-series forecasting by mapping dynamics to a sp… (voir plus)ace of real-valued measurement functions, enabling a linear operator representation. Despite the advantage of linearity, the operator is generally infinite-dimensional. Therefore, the objective is to learn measurement functions that yield a tractable finite-dimensional Koopman operator approximation. In this work, we establish a connection between Koopman operator approximation and linear Recurrent Neural Networks (RNNs), which have recently demonstrated remarkable success in sequence modeling. We show that by considering an extended state consisting of lagged observations, we can establish an equivalence between a structured Koopman operator and linear RNN updates. Building on this connection, we present SKOLR, which integrates a learnable spectral decomposition of the input signal with a multilayer perceptron (MLP) as the measurement functions and implements a structured Koopman operator via a highly parallel linear RNN stack. Numerical experiments on various forecasting benchmarks and dynamical systems show that this streamlined, Koopman-theory-based design delivers exceptional performance. Our code is available at: https://github.com/networkslab/SKOLR.
2025-05-01
ICML.cc/2025/Conference (poster)
doi.org
openreview.net
MODL: Multilearner Online Deep Learning
Antonios Valkanas
Boris Oreshkin
Mark Coates
Online deep learning solves the problem of learning from streams of data, reconciling two opposing objectives: learn fast and learn deep. Ex… (voir plus)isting work focuses almost exclusively on exploring pure deep learning solutions, which are much better suited to handle the"deep"than the"fast"part of the online learning equation. In our work, we propose a different paradigm, based on a hybrid multilearner approach. First, we develop a fast online logistic regression learner. This learner does not rely on backpropagation. Instead, it uses closed form recursive updates of model parameters, handling the fast learning part of the online learning problem. We then analyze the existing online deep learning theory and show that the widespread ODL approach, currently operating at complexity
2025-01-22
aistats.org/AISTATS/2025/Conference (poster)
doi.org
openreview.net
Personalized Negative Reservoir for Incremental Learning in Recommender Systems
Antonios Valkanas
Yuening Wang
Yingxue Zhang
Mark Coates
2025-01-01
Trans. Mach. Learn. Res. (publié)
doi.org
openreview.net
Population Monte Carlo With Normalizing Flow
Soumyasundar Pal
Antonios Valkanas
Mark Coates
Adaptive importance sampling (AIS) methods provide a useful alternative to Markov Chain Monte Carlo (MCMC) algorithms for performing inferen… (voir plus)ce of intractable distributions. Population Monte Carlo (PMC) algorithms constitute a family of AIS approaches which adapt the proposal distributions iteratively to improve the approximation of the target distribution. Recent work in this area primarily focuses on ameliorating the proposal adaptation procedure for high-dimensional applications. However, most of the AIS algorithms use simple proposal distributions for sampling, which might be inadequate in exploring target distributions with intricate geometries. In this work, we construct expressive proposal distributions in the AIS framework using normalizing flow, an appealing approach for modeling complex distributions. We use an iterative parameter update rule to enhance the approximation of the target distribution. Numerical experiments show that in high-dimensional settings, the proposed algorithm offers significantly improved performance compared to the existing techniques.
2024-01-01
IEEE Signal Processing Letters (publié)
doi.org
arxiv.org
Motion In-Betweening via Deep <inline-formula><tex-math notation="LaTeX">$\Delta$</tex-math><alternatives><mml:math><mml:mi>Δ</mml:mi></mml:math><inline-graphic xlink:href="oreshkin-ieq1-3309107.gif"/></alternatives></inline-formula>-Interpolator
Boris Oreshkin
Antonios Valkanas
Félix Harvey
Louis-Simon Ménard
Florent Bocquelet
Mark Coates
We show that the task of synthesizing human motion conditioned on a set of key frames can be solved more accurately and effectively if a dee… (voir plus)p learning based interpolator operates in the delta mode using the spherical linear interpolator as a baseline. We empirically demonstrate the strength of our approach on publicly available datasets achieving state-of-the-art performance. We further generalize these results by showing that the
2023-08-28
IEEE Transactions on Visualization and Computer Graphics (publié)
doi.org