2 citations · 3 across the 13 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…