Search Results for author: Jaeyoung Park

Found 8 papers, 3 papers with code

Silent Abandonment in Text-Based Contact Centers: Identifying, Quantifying, and Mitigating its Operational Impacts

no code implementations15 Jan 2025 Antonio Castellanos, Galit B. Yom-Tov, Yair Goldberg, Jaeyoung Park

We find that companies should use classification models to estimate abandonment scope and our EM algorithm to assess patience.

Fair Class-Incremental Learning using Sample Weighting

no code implementations2 Oct 2024 Jaeyoung Park, Minsu Kim, Steven Euijong Whang

We then propose a fair class-incremental learning framework that adjusts the training weights of current task samples to change the direction of the average gradient vector and thus reduce the forgetting of underperforming groups and achieve fairness.

class-incremental learning Class Incremental Learning +2

Smart-Infinity: Fast Large Language Model Training using Near-Storage Processing on a Real System

1 code implementation11 Mar 2024 Hongsun Jang, Jaeyong Song, Jaewon Jung, Jaeyoung Park, Youngsok Kim, Jinho Lee

Our work, Smart-Infinity, addresses the storage bandwidth bottleneck of storage-offloaded LLM training using near-storage processing devices on a real system.

Language Modeling Language Modelling +1

Falcon: Fair Active Learning using Multi-armed Bandits

1 code implementation23 Jan 2024 Ki Hyun Tae, Hantian Zhang, Jaeyoung Park, Kexin Rong, Steven Euijong Whang

Given a user-specified group fairness measure, Falcon identifies samples from "target groups" (e. g., (attribute=female, label=positive)) that are the most informative for improving fairness.

Active Learning Attribute +4

A Hybrid Antenna Switching Scheme for Dynamic Channel Sounding

no code implementations20 Apr 2023 Ali Al-Ameri, Jaeyoung Park, Juan Sanchez, Xuesong Cai, Fredrik Tufvesson

The primary purpose is to maintain the estimation accuracy of angles of MPCs while decreasing the resolution of Doppler frequencies for minimized complexity of channel parameter estimation.

iFlipper: Label Flipping for Individual Fairness

1 code implementation15 Sep 2022 Hantian Zhang, Ki Hyun Tae, Jaeyoung Park, Xu Chu, Steven Euijong Whang

We then propose an approximate linear programming algorithm and provide theoretical guarantees on how close its result is to the optimal solution in terms of the number of label flips.

Fairness

Robust and flexible learning of a high-dimensional classification rule using auxiliary outcomes

no code implementations11 Nov 2020 Muxuan Liang, Jaeyoung Park, Qing Lu, Xiang Zhong

The proposed method includes an MTL step using all outcomes to gain efficiency, and a subsequent calibration step using only the outcome of interest to correct both types of biases.

Classification General Classification +1

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