2 citations · 3 across the 13 of their papers we have counts for
13 papers
Characterizing the Predictive Impact of Modalities with Supervised Latent-Variable Modeling
Divyam Madaan, Sumit Chopra, Kyunghyun Cho
Despite the recent success of Multimodal Large Language Models (MLLMs), existing approaches predominantly assume the availability of multiple modalities during training and inferen…
Paradox of De-identification: A Critique of HIPAA Safe Harbour in the Age of LLMs
Lavender Y. Jiang, Xujin Chris Liu, Kyunghyun Cho +1
Privacy is a human right that sustains patient-provider trust. Clinical notes capture a patient's private vulnerability and individuality, which are used for care coordination and…
Group Contrastive Learning for Weakly Paired Multimodal Data
Aditya Gorla, Hugues Van Assel, Jan-Christian Huetter +4
We present GROOVE, a semi-supervised multi-modal representation learning approach for high-content perturbation data where samples across modalities are weakly paired through share…
Training Dynamics of Learning 3D-Rotational Equivariance
Max W. Shen, Ewa Nowara, Michael Maser +1
While data augmentation is widely used to train symmetry-agnostic models, it remains unclear how quickly and effectively they learn to respect symmetries. We investigate this by de…
None To Optima in Few Shots: Bayesian Optimization with MDP Priors
Diantong Li, Kyunghyun Cho, Chong Liu
Bayesian Optimization (BO) is an efficient tool for optimizing black-box functions, but its theoretical guarantees typically hold in the asymptotic regime. In many critical real-wo…
MIST: Mutual Information Estimation Via Supervised Training
German Gritsai, Megan Richards, Maxime Méloux +2
We propose a fully data-driven approach to designing mutual information (MI) estimators. Since any MI estimator is a function of the observed sample from two random variables, we p…
The Geometry of Prompting: Unveiling Distinct Mechanisms of Task Adaptation in Language Models
Artem Kirsanov, Chi-Ning Chou, Kyunghyun Cho +1
Decoder-only language models have the ability to dynamically switch between various computational tasks based on input prompts. Despite many successful applications of prompting, t…
Meta-Statistical Learning: Supervised Learning of Statistical Estimators
Maxime Peyrard, Kyunghyun Cho
Statistical inference, a central tool of science, revolves around the study and the usage of statistical estimators: functions that map finite samples to predictions about unknown…
How To Think About End-To-End Encryption and AI: Training, Processing, Disclosure, and Consent
Mallory Knodel, Andrés Fábrega, Daniella Ferrari +6
End-to-end encryption (E2EE) has become the gold standard for securing communications, bringing strong confidentiality and privacy guarantees to billions of users worldwide. Howeve…
Deep Autoregressive Models as Causal Inference Engines
Daniel Jiwoong Im, Kevin Zhang, Nakul Verma +1
Existing causal inference (CI) models are often restricted to data with low-dimensional confounders and singleton actions. We propose an autoregressive (AR) CI framework capable of…
A Brief Introduction to Causal Inference in Machine Learning
Kyunghyun Cho
This is a lecture note produced for DS-GA 3001.003 "Special Topics in DS - Causal Inference in Machine Learning" at the Center for Data Science, New York University in Spring, 2024…
Let's Go Shopping (LGS) -- Web-Scale Image-Text Dataset for Visual Concept Understanding
Yatong Bai, Utsav Garg, Apaar Shanker +10
Vision and vision-language applications of neural networks, such as image classification and captioning, rely on large-scale annotated datasets that require non-trivial data-collec…
PaperCard for Reporting Machine Assistance in Academic Writing
Won Ik Cho, Eunjung Cho, Kyunghyun Cho
Academic writing process has benefited from various technological developments over the years including search engines, automatic translators, and editing tools that review grammar…