no code implementations • 17 Apr 2024 • Jiayi Huang, Sangwoo Park, Osvaldo Simeone
This paper proposes an extension of variational inference (VI)-based Bayesian learning that integrates calibration regularization for improved ID performance, confidence minimization for OOD detection, and selective calibration to ensure a synergistic use of calibration regularization and confidence minimization.
no code implementations • 11 Apr 2024 • Sangwoo Park, Amritanshu Pandey
Given time-series measurement values coming from a fixed set of sensors on the grid, can we identify anomalies in the network topology or measurement data?
no code implementations • 2 Apr 2024 • Jiechen Chen, Sangwoo Park, Petar Popovski, H. Vincent Poor, Osvaldo Simeone
This work proposes a novel architecture that integrates a wake-up radio mechanism within a split computing system consisting of remote, wirelessly connected, NPUs.
no code implementations • 14 Mar 2024 • Yunchuan Zhang, Sangwoo Park, Osvaldo Simeone
In many applications, ranging from logistics to engineering, a designer is faced with a sequence of optimization tasks for which the objectives are in the form of black-box functions that are costly to evaluate.
no code implementations • 22 Jan 2024 • Matteo Zecchin, Sangwoo Park, Osvaldo Simeone, Fredrik Hellström
A popular technique to achieve this goal is conformal prediction (CP), which transforms an arbitrary base predictor into a set predictor with coverage guarantees.
no code implementations • 22 Jan 2024 • Kfir M. Cohen, Sangwoo Park, Osvaldo Simeone, Shlomo Shamai
CV-CRC is proved to offer theoretical guarantees on the average risk of the set predictor.
no code implementations • 25 Oct 2023 • Jiechen Chen, Sangwoo Park, Osvaldo Simeone
Spiking neural networks (SNNs) are recurrent models that can leverage sparsity in input time series to efficiently carry out tasks such as classification.
no code implementations • 16 Oct 2023 • Matteo Zecchin, Sangwoo Park, Osvaldo Simeone
This property is leveraged to devise a novel model predictive control (MPC) framework that addresses open-loop and closed-loop control problems under general average constraints on the quality or safety of the control policy.
no code implementations • 8 Aug 2023 • Meiyi Zhu, Matteo Zecchin, Sangwoo Park, Caili Guo, Chunyan Feng, Osvaldo Simeone
Recent work has introduced federated conformal prediction (CP), which leverages devices-to-server communication to improve the reliability of the server's decision.
no code implementations • 30 Jun 2023 • Yunchuan Zhang, Sangwoo Park, Osvaldo Simeone
Focusing on methods based on Bayesian optimization (BO), prior art has introduced an optimization scheme -- referred to as SAFEOPT -- that is guaranteed not to select any unsafe solution with a controllable probability over feedback noise as long as strict assumptions on the safety constraint function are met.
no code implementations • 18 May 2023 • Jiechen Chen, Sangwoo Park, Osvaldo Simeone
Spiking neural networks (SNNs) process time-series data via internal event-driven neural dynamics whose energy consumption depends on the number of spikes exchanged between neurons over the course of the input presentation.
no code implementations • 12 May 2023 • Jiayi Huang, Sangwoo Park, Osvaldo Simeone
Deep learning models, including modern systems like large language models, are well known to offer unreliable estimates of the uncertainty of their decisions.
1 code implementation • 6 Apr 2023 • Sangwoo Park, Osvaldo Simeone
In this work, we aim at augmenting the decisions output by quantum models with "error bars" that provide finite-sample coverage guarantees.
1 code implementation • 15 Feb 2023 • Kfir M. Cohen, Sangwoo Park, Osvaldo Simeone, Petar Popovski, Shlomo Shamai
The dynamic scheduling of ultra-reliable and low-latency traffic (URLLC) in the uplink can significantly enhance the efficiency of coexisting services, such as enhanced mobile broadband (eMBB) devices, by only allocating resources when necessary.
no code implementations • 15 Dec 2022 • Kfir M. Cohen, Sangwoo Park, Osvaldo Simeone, Shlomo Shamai
This paper investigates the application of conformal prediction as a general framework to obtain AI models that produce decisions with formal calibration guarantees.
1 code implementation • 29 Oct 2022 • YoungJoon Lee, Sangwoo Park, Joonhyuk Kang
While being an effective framework of learning a shared model across multiple edge devices, federated learning (FL) is generally vulnerable to Byzantine attacks from adversarial edge devices.
1 code implementation • 29 Oct 2022 • YoungJoon Lee, Sangwoo Park, Joonhyuk Kang
Federated learning (FL) aims at optimizing a shared global model over multiple edge devices without transmitting (private) data to the central server.
no code implementations • 10 Oct 2022 • Kfir M. Cohen, Sangwoo Park, Osvaldo Simeone, Shlomo Shamai
We propose to leverage the conformal prediction framework to obtain data-driven set predictions whose calibration properties hold irrespective of the data distribution.
1 code implementation • 6 Oct 2022 • Sangwoo Park, Kfir M. Cohen, Osvaldo Simeone
Conventional frequentist learning is known to yield poorly calibrated models that fail to reliably quantify the uncertainty of their decisions.
no code implementations • 3 Oct 2022 • Lisha Chen, Sharu Theresa Jose, Ivana Nikoloska, Sangwoo Park, Tianyi Chen, Osvaldo Simeone
This review monograph provides an introduction to meta-learning by covering principles, algorithms, theory, and engineering applications.
no code implementations • 13 Jul 2022 • Riccardo Marini, Sangwoo Park, Osvaldo Simeone, Chiara Buratti
Unmanned aerial base stations (UABSs) can be deployed in vehicular wireless networks to support applications such as extended sensing via vehicle-to-everything (V2X) services.
no code implementations • 1 Jul 2022 • Matteo Zecchin, Sangwoo Park, Osvaldo Simeone, Marios Kountouris, David Gesbert
In this context, we explore the application of the framework of robust Bayesian learning.
1 code implementation • 23 Mar 2022 • Sangwoo Park, Osvaldo Simeone
An efficient data-driven prediction strategy for multi-antenna frequency-selective channels must operate based on a small number of pilot symbols.
no code implementations • 3 Mar 2022 • Matteo Zecchin, Sangwoo Park, Osvaldo Simeone, Marios Kountouris, David Gesbert
Standard Bayesian learning is known to have suboptimal generalization capabilities under misspecification and in the presence of outliers.
1 code implementation • 1 Oct 2021 • Sangwoo Park, Osvaldo Simeone
This paper proposes to leverage meta-learning in order to mitigate the requirements in terms of training data for channel fading prediction.
1 code implementation • 2 Aug 2021 • Kfir M. Cohen, Sangwoo Park, Osvaldo Simeone, Shlomo Shamai
Bayesian active meta-learning is seen in experiments to significantly reduce the number of frames required to obtain efficient adaptation procedure for new frames.
no code implementations • 1 Jun 2021 • Sharu Theresa Jose, Sangwoo Park, Osvaldo Simeone
Under a Bayesian formulation, assuming a well-specified model, the two contributions can be exactly expressed (for the log-loss) or bounded (for more general losses) in terms of information-theoretic quantities (Xu and Raginsky, 2020).
no code implementations • 26 Dec 2020 • Hopyong Gil, Sangwoo Park, Yusang Park, Wongoo Han, Juyean Hong, Juneyoung Jung
This work aims to address imbalance problem in the situation of using a general unbalanced data of non-extreme distribution not including few shot and the focal loss for anchor free object detector.
1 code implementation • 3 Mar 2020 • Sangwoo Park, Osvaldo Simeone, Joonhyuk Kang
The proposed approach is based on a meta-training phase in which the online gradient-based meta-learning of the decoder is coupled with the joint training of the encoder via the transmission of pilots and the use of a feedback link.
1 code implementation • 5 Jan 2020 • Osvaldo Simeone, Sangwoo Park, Joonhyuk Kang
Machine learning methods adapt the parameters of a model, constrained to lie in a given model class, by using a fixed learning procedure based on data or active observations.
no code implementations • 22 Oct 2019 • Jongmin Yu, Sangwoo Park, Sangwook Lee, Moongu Jeon
The proposed framework consists of four models: spatio-temporal representation learning, scene condition understanding, feature fusion, and drowsiness detection.
1 code implementation • 22 Oct 2019 • Sangwoo Park, Osvaldo Simeone, Joonhyuk Kang
When a channel model is available, learning how to communicate on fading noisy channels can be formulated as the (unsupervised) training of an autoencoder consisting of the cascade of encoder, channel, and decoder.
1 code implementation • 23 Aug 2019 • Sangwoo Park, Hyeryung Jang, Osvaldo Simeone, Joonhyuk Kang
This paper considers an Internet-of-Things (IoT) scenario in which devices sporadically transmit short packets with few pilot symbols over a fading channel.
1 code implementation • 6 Mar 2019 • Sangwoo Park, Hyeryung Jang, Osvaldo Simeone, Joonhyuk Kang
Consider an Internet-of-Things (IoT) scenario in which devices transmit sporadically using short packets with few pilot symbols.