Publications

The Conditional Gradient (or Frank-Wolfe) method is one of the most well-known methods for solving constrained optimization problems appeari… (voir plus)ng in various machine learning tasks. The simplicity of iteration and applicability to many practical problems helped the method to gain popularity in the community. In recent years, the Frank-Wolfe algorithm received many different extensions, including stochastic modifications with variance reduction and coordinate sampling for training of huge models or distributed variants for big data problems. In this paper, we present a unified convergence analysis of the Stochastic Frank-Wolfe method that covers a large number of particular practical cases that may have completely different nature of stochasticity, intuitions and application areas. Our analysis is based on a key parametric assumption on the variance of the stochastic gradients. But unlike most works on unified analysis of other methods, such as SGD, we do not assume an unbiasedness of the real gradient estimation. We conduct analysis for convex and non-convex problems due to the popularity of both cases in machine learning. With this general theoretical framework, we not only cover rates of many known methods, but also develop numerous new methods. This shows the flexibility of our approach in developing new algorithms based on the Conditional Gradient approach. We also demonstrate the properties of the new methods through numerical experiments.

2024-01-01

International Conference on Artificial Intelligence and Statistics (publié)

proceedings.mlr.press

arxiv.org

Stochastic Simulated Quantum Annealing for Fast Solution of Combinatorial Optimization Problems

Naoya Onizawa

Ryoma Sasaki

Duckgyu Shin

Warren Gross

Takahiro Hanyu

In this paper, we introduce stochastic simulated quantum annealing (SSQA) for large-scale combinatorial optimization problems. SSQA is desig… (voir plus)ned based on stochastic computing and quantum Monte Carlo, which can simulate quantum annealing (QA) by using multiple replicas of spins (probabilistic bits) in classical computing. The use of stochastic computing leads to an efficient parallel spin-state update algorithm, enabling quick search for a solution around the global minimum energy. Therefore, SSQA realizes quantum-like annealing for large-scale problems and can handle fully connected models in combinatorial optimization, unlike QA. The proposed method is evaluated in MATLAB on graph isomorphism problems, which are typical combinatorial optimization problems. The proposed method achieves a convergence speed an order of magnitude faster than a conventional stochastic simulaated annealing method. Additionally, it can handle a 100-times larger problem size compared to QA and a 25-times larger problem size compared to a traditional SA method, respectively, for similar convergence probabilities.

2024-01-01

IEEE Access (publié)

doi.org

arxiv.org

Strong Consistency and Rate of Convergence of Switched Least Squares System Identification for Autonomous Markov Jump Linear Systems

Borna Sayedana

Mohammad Afshari

Peter E. Caines

Aditya Mahajan

In this paper, we investigate the problem of system identification for autonomous Markov jump linear systems (MJS) with complete state obser… (voir plus)vations. We propose switched least squares method for identification of MJS, show that this method is strongly consistent, and derive data-dependent and data-independent rates of convergence. In particular, our data-independent rate of convergence shows that, almost surely, the system identification error is

2024-01-01

IEEE Transactions on Automatic Control (publié)

doi.org

arxiv.org

No such thing as one-size-fits-all in AI ethics frameworks: a comparative case study

Vivian Qiang

Jimin Rhim

AJung Moon

2024-01-01

AI Soc. (publié)

doi.org

Sufficient conditions for offline reactivation in recurrent neural networks

Nanda H Krishna

Colin Bredenberg

Daniel Levenstein

Blake Richards

Guillaume Lajoie

During periods of quiescence, such as sleep, neural activity in many brain circuits resembles that observed during periods of task engagemen… (voir plus)t. However, the precise conditions under which task-optimized networks can autonomously reactivate the same network states responsible for online behavior are poorly understood. In this study, we develop a mathematical framework that outlines sufficient conditions for the emergence of neural reactivation in circuits that encode features of smoothly varying stimuli. We demonstrate mathematically that noisy recurrent networks optimized to track environmental state variables using change-based sensory information naturally develop denoising dynamics, which, in the absence of input, cause the network to revisit state configurations observed during periods of online activity. We validate our findings using numerical experiments on two canonical neuroscience tasks: spatial position estimation based on self-motion cues, and head direction estimation based on angular velocity cues. Overall, our work provides theoretical support for modeling offline reactivation as an emergent consequence of task optimization in noisy neural circuits.

2024-01-01

International Conference on Learning Representations (publié)

openreview.net

Survey on Explainable AI: Techniques, challenges and open issues

Adel Abusitta

Miles Q. Li

Benjamin Fung

2024-01-01

Expert Syst. Appl. (publié)

doi.org

À la hauteur du moment

Rapport et guide politique GPAI: Vers une réelle égalité en IA

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Publications

À la hauteur du moment

Rapport et guide politique GPAI: Vers une réelle égalité en IA

Demandes de supervision

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Publications