Author name cluster

Seungyeon Kim

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6 papers

2 author rows

ICRA Conference 2025 Conference Paper

OPPA: Online Planner's Parameter Adaptation for Enhanced Mobile Robot Navigation

Minsu Chang
Junwon Jang
Daewoong Han
Wonje Choi 0005
Seungyeon Kim
Hyunkyu Park 0007
Hyundo Choi

Autonomous navigation in mobile robots has made significant advancements; however, traditional methods often struggle to adapt in real-time to dynamic or unstructured environments. This paper presents the Online Planner's Parameter Adaptation (OPPA) framework, which enhances both adaptability and safety in mobile robot navigation by dynamically adjusting planner parameters. OPPA integrates a rule-based system for estimating tunnel width using 2D LiDAR and path data with a learning-based approach utilizing a shallow transformer model. By incorporating a human-in-the-loop process to refine training data, OPPA improves accuracy and reliability in complex environments. Designed for real-time efficiency on resource-constrained platforms, OPPA has been validated through simulation and real-world experiments, demonstrating its ability to enhance both safety and performance. These results highlight OPPA as a viable solution for dynamic and complex robotic applications.

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NeurIPS Conference 2020 Conference Paper

Why are Adaptive Methods Good for Attention Models?

Jingzhao Zhang
Sai Praneeth Karimireddy
Andreas Veit
Seungyeon Kim
Sashank Reddi
Sanjiv Kumar
Suvrit Sra

While stochastic gradient descent (SGD) is still the de facto algorithm in deep learning, adaptive methods like Clipped SGD/Adam have been observed to outperform SGD across important tasks, such as attention models. The settings under which SGD performs poorly in comparison to adaptive methods are not well understood yet. In this paper, we provide empirical and theoretical evidence that a heavy-tailed distribution of the noise in stochastic gradients is one cause of SGD's poor performance. We provide the first tight upper and lower convergence bounds for adaptive gradient methods under heavy-tailed noise. Further, we demonstrate how gradient clipping plays a key role in addressing heavy-tailed gradient noise. Subsequently, we show how clipping can be applied in practice by developing an adaptive coordinate-wise clipping algorithm (ACClip) and demonstrate its superior performance on BERT pretraining and finetuning tasks.

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JMLR Journal 2016 Journal Article

LLORMA: Local Low-Rank Matrix Approximation

Joonseok Lee
Seungyeon Kim
Guy Lebanon
Yoram Singer
Samy Bengio

Matrix approximation is a common tool in recommendation systems, text mining, and computer vision. A prevalent assumption in constructing matrix approximations is that the partially observed matrix is low-rank. In this paper, we propose, analyze, and experiment with two procedures, one parallel and the other global, for constructing local matrix approximations. The two approaches approximate the observed matrix as a weighted sum of low-rank matrices. These matrices are limited to a local region of the observed matrix. We analyze the accuracy of the proposed local low-rank modeling. Our experiments show improvements in prediction accuracy over classical approaches for recommendation tasks. [abs] [ pdf ][ bib ] &copy JMLR 2016. ( edit, beta )

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AAAI Conference 2015 Conference Paper

Estimating Temporal Dynamics of Human Emotions

Seungyeon Kim
Joonseok Lee
Guy Lebanon
Haesun Park

Sentiment analysis predicts a one-dimensional quantity describing the positive or negative emotion of an author. Mood analysis extends the one-dimensional sentiment response to a multi-dimensional quantity, describing a diverse set of human emotions. In this paper, we extend sentiment and mood analysis temporally and model emotions as a function of time based on temporal streams of blog posts authored by a specific author. The model is useful for constructing predictive models and discovering scientific models of human emotions.

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AAAI Conference 2015 Conference Paper

Local Context Sparse Coding

Seungyeon Kim
Joonseok Lee
Guy Lebanon
Haesun Park

The n-gram model has been widely used to capture the local ordering of words, yet its exploding feature space often causes an estimation issue. This paper presents local context sparse coding (LCSC), a non-probabilistic topic model that effectively handles large feature spaces using sparse coding. In addition, it introduces a new concept of locality, local contexts, which provides a representation that can generate locally coherent topics and document representations. Our model efficiently finds topics and representations by applying greedy coordinate descent updates. The model is useful for discovering local topics and the semantic flow of a document, as well as constructing predictive models.

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NeurIPS Conference 2012 Conference Paper

Automatic Feature Induction for Stagewise Collaborative Filtering

Joonseok Lee
Mingxuan Sun
Seungyeon Kim
Guy Lebanon

Recent approaches to collaborative filtering have concentrated on estimating an algebraic or statistical model, and using the model for predicting missing ratings. In this paper we observe that different models have relative advantages in different regions of the input space. This motivates our approach of using stagewise linear combinations of collaborative filtering algorithms, with non-constant combination coefficients based on kernel smoothing. The resulting stagewise model is computationally scalable and outperforms a wide selection of state-of-the-art collaborative filtering algorithms.

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