21 citations · 83 across the 13 of their papers we have counts for
15 papers
Bet on Features: Anytime-Valid and Feature-Aware Auditing of Conditional Quantile Forecasters
Ivane Antonov, Sohom Mukherjee, Richard Pibernik +1
Black-box conditional quantile forecasts are widely used for sequential decisions under asymmetric costs, such as inventory planning in supply chain management. Once deployed, such…
Betting on Bets: Anytime-Valid Tests for Stochastic Dominance
Sebastian Arnold, Yo Joong Choe, Marco Scarsini +1
How can we monitor, in real time, whether one uncertain prospect has any upside over another? To answer this question, we develop a novel family of sequential, anytime-valid tests…
The Information Geometry of Softmax: Probing and Steering
Kiho Park, Todd Nief, Yo Joong Choe +1
This paper concerns the question of how AI systems encode semantic structure into the geometric structure of their representation spaces. The motivating observation is that the nat…
The Geometry of Categorical and Hierarchical Concepts in Large Language Models
Kiho Park, Yo Joong Choe, Yibo Jiang +1
The linear representation hypothesis is the informal idea that semantic concepts are encoded as linear directions in the representation spaces of large language models (LLMs). Prev…
Combining Evidence Across Filtrations
Yo Joong Choe, Aaditya Ramdas
In sequential anytime-valid inference, any admissible procedure must be based on e-processes: generalizations of test martingales that quantify the accumulated evidence against a c…
The Linear Representation Hypothesis and the Geometry of Large Language Models
Kiho Park, Yo Joong Choe, Victor Veitch
Informally, the 'linear representation hypothesis' is the idea that high-level concepts are represented linearly as directions in some representation space. In this paper, we addre…
Counterfactually Comparing Abstaining Classifiers
Yo Joong Choe, Aditya Gangrade, Aaditya Ramdas
Abstaining classifiers have the option to abstain from making predictions on inputs that they are unsure about. These classifiers are becoming increasingly popular in high-stakes d…
Comparing Sequential Forecasters
Yo Joong Choe, Aaditya Ramdas
Consider two forecasters, each making a single prediction for a sequence of events over time. We ask a relatively basic question: how might we compare these forecasters, either onl…
An Empirical Study of Invariant Risk Minimization
Yo Joong Choe, Jiyeon Ham, Kyubyong Park
Invariant risk minimization (IRM) (Arjovsky et al., 2019) is a recently proposed framework designed for learning predictors that are invariant to spurious correlations across diffe…
KorNLI and KorSTS: New Benchmark Datasets for Korean Natural Language Understanding
Jiyeon Ham, Yo Joong Choe, Kyubyong Park +2
Natural language inference (NLI) and semantic textual similarity (STS) are key tasks in natural language understanding (NLU). Although several benchmark datasets for those tasks ha…
Jejueo Datasets for Machine Translation and Speech Synthesis
Kyubyong Park, Yo Joong Choe, Jiyeon Ham
Jejueo was classified as critically endangered by UNESCO in 2010. Although diverse efforts to revitalize it have been made, there have been few computational approaches. Motivated…
word2word: A Collection of Bilingual Lexicons for 3,564 Language Pairs
Yo Joong Choe, Kyubyong Park, Dongwoo Kim
We present word2word, a publicly available dataset and an open-source Python package for cross-lingual word translations extracted from sentence-level parallel corpora. Our dataset…
A Neural Grammatical Error Correction System Built On Better Pre-training and Sequential Transfer Learning
Yo Joong Choe, Jiyeon Ham, Kyubyong Park +1
Grammatical error correction can be viewed as a low-resource sequence-to-sequence task, because publicly available parallel corpora are limited. To tackle this challenge, we first…
Predicting drug-target interaction using 3D structure-embedded graph representations from graph neural networks
Jaechang Lim, Seongok Ryu, Kyubyong Park +3
Accurate prediction of drug-target interaction (DTI) is essential for in silico drug design. For the purpose, we propose a novel approach for predicting DTI using a GNN that direct…
Discovery of Natural Language Concepts in Individual Units of CNNs
Seil Na, Yo Joong Choe, Dong-Hyun Lee +1
Although deep convolutional networks have achieved improved performance in many natural language tasks, they have been treated as black boxes because they are difficult to interpre…