9 papers
MatrixFormer: A Foundation Model for Matrix Completion
Dwaipayan Saha, Jacob Feitelberg, Kyuseong Choi +2
Matrix completion underlies problems from tabular imputation to causal inference, yet existing tabular foundation models treat it as entry-by-entry prediction, repeating context fo…
ORCA-bench: How Ready Are Language Model Agents for Oncall?
Albert Gong, Kyuseong Choi, Abhineet Agarwal +5
Large language models can write, patch, and search code, but oncall root cause analysis (RCA) demands something different: reasoning over noisy metrics, logs, traces, and source co…
GOPO: Policy Optimization using Ranked Rewards
Kyuseong Choi, Dwaipayan Saha, Woojeong Kim +2
Standard reinforcement learning from human feedback (RLHF) trains a reward model on pairwise preference data and then uses it for policy optimization. However, while reward models…
Wild regenerative block bootstrap for Harris recurrent Markov chains
Kyuseong Choi, Gabriella Ciolek
We consider Gaussian and bootstrap approximations for the supremum of additive functionals of aperiodic Harris recurrent Markov chains. The supremum is taken over a function class…
One Pipeline, Many Transformers: Pattern-Specific Imputation Specialists for Tabular Missing Data
Jacob Feitelberg, Dwaipayan Saha, Kyuseong Choi +3
Missing data in tabular datasets forces practitioners into a hard choice: deploy a general-purpose imputer that may perform poorly for the problem at hand, or wait for someone to d…
N: A Unified Python Package and Test Bench for Nearest Neighbor-Based Matrix Completion
Caleb Chin, Aashish Khubchandani, Harshvardhan Maskara +8
Nearest neighbor (NN) methods have re-emerged as competitive tools for matrix completion, offering strong empirical performance and recent theoretical guarantees, including entry-w…
Supervised Kernel Thinning
Albert Gong, Kyuseong Choi, Raaz Dwivedi
The kernel thinning algorithm of Dwivedi & Mackey (2024) provides a better-than-i.i.d. compression of a generic set of points. By generating high-fidelity coresets of size signific…
Learning Counterfactual Distributions via Kernel Nearest Neighbors
Kyuseong Choi, Jacob Feitelberg, Caleb Chin +2
Consider a setting with multiple units (e.g., individuals, cohorts, geographic locations) and outcomes (e.g., treatments, times, items), where the goal is to learn a multivariate d…
Distributional Matrix Completion via Nearest Neighbors in the Wasserstein Space
Jacob Feitelberg, Kyuseong Choi, Anish Agarwal +1
We study the problem of distributional matrix completion: Given a sparsely observed matrix of empirical distributions, we seek to impute the true distributions associated with both…