64 citations · 251 across the 24 of their papers we have counts for
25 papers
Beyond Second-Order Information: Paid Logistic Regression with Noisy Features
Sanghwa Kim, Kyoungseok Jang, Se-Young Yun
We consider online binary prediction with paid stochastic features, where the learner can spend resources to obtain better observations of the underlying features. This setting is…
Mold into a Graph: Efficient Bayesian Optimization over Mixed-Spaces
Jaeyeon Ahn, Taehyeon Kim, Seyoung Yun
Real-world optimization problems are generally not just black-box problems, but also involve mixed types of inputs in which discrete and continuous variables coexist. Such mixed-sp…
Meta-learning Amidst Heterogeneity and Ambiguity
Kyeongryeol Go, Seyoung Yun
Meta-learning aims to learn a model that can handle multiple tasks generated from an unknown but shared distribution. However, typical meta-learning algorithms have assumed the tas…
Adaptive Local Bayesian Optimization Over Multiple Discrete Variables
Taehyeon Kim, Jaeyeon Ahn, Nakyil Kim +1
In the machine learning algorithms, the choice of the hyperparameter is often an art more than a science, requiring labor-intensive search with expert experience. Therefore, automa…
Test Score Algorithms for Budgeted Stochastic Utility Maximization
Dabeen Lee, Milan Vojnovic, Se-Young Yun
Motivated by recent developments in designing algorithms based on individual item scores for solving utility maximization problems, we study the framework of using test scores, def…
MixCo: Mix-up Contrastive Learning for Visual Representation
Sungnyun Kim, Gihun Lee, Sangmin Bae +1
Contrastive learning has shown remarkable results in recent self-supervised approaches for visual representation. By learning to contrast positive pairs' representation from the co…
Regret in Online Recommendation Systems
Kaito Ariu, Narae Ryu, Se-Young Yun +1
This paper proposes a theoretical analysis of recommendation systems in an online setting, where items are sequentially recommended to users over time. In each round, a user, rando…
BOIL: Towards Representation Change for Few-shot Learning
Jaehoon Oh, Hyungjun Yoo, ChangHwan Kim +1
Model Agnostic Meta-Learning (MAML) is one of the most representative of gradient-based meta-learning algorithms. MAML learns new tasks with a few data samples using inner updates…
SIPA: A Simple Framework for Efficient Networks
Gihun Lee, Sangmin Bae, Jaehoon Oh +1
With the success of deep learning in various fields and the advent of numerous Internet of Things (IoT) devices, it is essential to lighten models suitable for low-power devices. I…
Reinforcement with Fading Memories
Kuang Xu, Se-Young Yun
We study the effect of imperfect memory on decision making in the context of a stochastic sequential action-reward problem. An agent chooses a sequence of actions which generate di…
Spectral Approximate Inference
Sejun Park, Eunho Yang, Se-Young Yun +1
Given a graphical model (GM), computing its partition function is the most essential inference task, but it is computationally intractable in general. To address the issue, iterati…
Accelerated MM Algorithms for Ranking Scores Inference from Comparison Data
Milan Vojnovic, Seyoung Yun, Kaifang Zhou
In this paper, we study a popular method for inference of the Bradley-Terry model parameters, namely the MM algorithm, for maximum likelihood estimation and maximum a posteriori pr…
Spectrogram-channels u-net: a source separation model viewing each channel as the spectrogram of each source
Jaehoon Oh, Duyeon Kim, Se-Young Yun
Sound source separation has attracted attention from Music Information Retrieval(MIR) researchers, since it is related to many MIR tasks such as automatic lyric transcription, sing…
Clustering in Block Markov Chains
Jaron Sanders, Alexandre Proutière, Se-Young Yun
This paper considers cluster detection in Block Markov Chains (BMCs). These Markov chains are characterized by a block structure in their transition matrix. More precisely, the …
Parameter Estimation for Thurstone Choice Models
Milan Vojnovic, Se-Young Yun
We consider the estimation accuracy of individual strength parameters of a Thurstone choice model when each input observation consists of a choice of one item from a set of two or…
Submodular Maximization using Test Scores
Shreyas Sekar, Milan Vojnovic, Se-Young Yun
We study the canonical problem of maximizing a stochastic submodular function subject to a cardinality constraint, where the goal is to select a subset from a ground set of items w…
Optimal Cluster Recovery in the Labeled Stochastic Block Model
Se-Young Yun, Alexandre Proutiere
We consider the problem of community detection or clustering in the labeled Stochastic Block Model (LSBM) with a finite number of clusters of sizes linearly growing with the gl…
Streaming, Memory Limited Matrix Completion with Noise
Se-Young Yun, Marc Lelarge, Alexandre Proutiere
In this paper, we consider the streaming memory-limited matrix completion problem when the observed entries are noisy versions of a small random fraction of the original entries. W…
Accurate Community Detection in the Stochastic Block Model via Spectral Algorithms
Se-Young Yun, Alexandre Proutiere
We consider the problem of community detection in the Stochastic Block Model with a finite number of communities of sizes linearly growing with the network size . This model…
Streaming, Memory Limited Algorithms for Community Detection
Se-Young Yun, Marc Lelarge, Alexandre Proutiere
In this paper, we consider sparse networks consisting of a finite number of non-overlapping communities, i.e. disjoint clusters, so that there is higher density within clusters tha…
Distributed Load Balancing in Heterogeneous Systems
Se-Young Yun, Alexandre Proutiere
We consider the problem of distributed load balancing in heterogenous parallel server systems, where the service rate achieved by a user at a server depends on both the user and th…
Community Detection via Random and Adaptive Sampling
Se-Young Yun, Alexandre Proutiere
In this paper, we consider networks consisting of a finite number of non-overlapping communities. To extract these communities, the interaction between pairs of nodes may be sample…
On the Delay Scaling Laws of Cache Networks
Boram Jin, Se-Young Yun, Deawoo Kim +5
The Internet is becoming more and more content-oriented, where one of main components in content-oriented Internet architectures is network caching. Despite a surge of extensive us…
CSMA over Time-varying Channels: Optimality, Uniqueness and Limited Backoff Rate
Se-Young Yun, Jinwoo Shin, Yung Yi
Recent studies on MAC scheduling have shown that carrier sense multiple access (CSMA) algo- rithms can be throughput optimal for arbitrary wireless network topology. However, these…