5 papers
Downside-Controlled Online Forecast Combination under Delayed and Revised Outcomes
Minkyoung Kim, Hyunjung Byun, Yohan Lee +1
Post-hoc correction adjusts a forecaster that cannot be retrained, such as a foundation model, but a correction fitted where errors are stable can hurt where they shift. We aim for…
CTRL: Control-Based Time Series Forecasting with LLM-Guided Residual Learning
Minkyoung Kim, Daeun Ji, Yohan Lee +2
Time series forecasting underpins critical decision-making across diverse domains. While large language models (LLMs) offer promising reasoning capabilities, existing LLM-based tim…
Beyond Similarity: Coverage-Aware Prompt Selection for Time Series Forecasting with LLMs
Daeun Ji, Minkyoung Kim, Dongkuk Kim +3
Similarity-based retrieval is the dominant rule for conditioning large language models (LLMs) in in-context learning, retrieval-augmented generation, and prompt-based time series f…
Chance-constrained selection of sequential intervention strategies from counterfactual estimates
Minkyoung Kim, Beakcheol Jang
Many operational decisions are sequences of interventions under a cumulative resource limit, such as a maintenance schedule within a crew-hour budget. Choosing among them calls for…
Bilevel Graph Structure Learning, Revisited: Inner-Channel Origins of the Reported Gain
Minkyoung Kim, Beakcheol Jang
Bilevel graph structure learning is widely understood to improve graph neural networks by jointly optimizing model parameters and a learned graph structure, with the resulting perf…