2 citations · 3 across the 13 of their papers we have counts for
13 papers
Tabular Numeric Stretch Transformation
Zihao Ye, Juyong Kim, Johnna Sundberg +2
Tabular data presents unique challenges for deep learning due to its heterogeneous nature, where numeric features exhibit diverse distributions, scales, and statistical properties.…
Context-Informed Ship Trajectory Prediction via Conditional Attention
Yuan Guan, Chandler Squires, Timothy Hu +1
Long-term ship trajectory prediction is a fundamental capability for maritime safety and autonomous navigation. While recent Transformer-based architectures have improved forecasti…
Few-Step Boltzmann Generators via Scalable Likelihood Flow Maps
RuiKang OuYang, Hanlin Yu, Xinyue Ai +7
Recent progress in flow-based generative modeling has led to models that output high-quality samples while using only a small number of function evaluations. However, at present, t…
Catastrophic Compositional Generation: Why Vanilla Diffusion Models Fail to Extrapolate
Duncan Soiffer, Chandler Squires, Yuan Guan +2
The task of compositional generation involves using a conditional generative model, trained only on a subset of the possible conditions, to produce samples from compositionally-def…
Concept Modulation Models: A Unified Framework for Identifiability and Extrapolation
Soheun Yi, Yizhou Lu, Chandler Squires +1
Reliable generalization in conditional latent variable models requires understanding both identifiability and extrapolation: how observed variation across attributes determines lat…
A Unifying Framework for Unsupervised Concept Extraction
Chandler Squires, Pradeep Ravikumar
Techniques for concept extraction, such as sparse autoencoders and transcoders, aim to extract high-level symbolic concepts from low-level nonsymbolic representations. When these e…
Eigenfunction Extraction for Ordered Representation Learning
Burak Varıcı, Che-Ping Tsai, Ritabrata Ray +2
Recent advances in representation learning reveal that widely used objectives, such as contrastive and non-contrastive, implicitly perform spectral decomposition of a contextual ke…
Contextures: Representations from Contexts
Runtian Zhai, Kai Yang, Che-Ping Tsai +3
Despite the empirical success of foundation models, we do not have a systematic characterization of the representations that these models learn. In this paper, we establish the con…
Spectrally Transformed Kernel Regression
Runtian Zhai, Rattana Pukdee, Roger Jin +2
Unlabeled data is a key component of modern machine learning. In general, the role of unlabeled data is to impose a form of smoothness, usually from the similarity information enco…
An Interventional Perspective on Identifiability in Gaussian LTI Systems with Independent Component Analysis
Goutham Rajendran, Patrik Reizinger, Wieland Brendel +1
We investigate the relationship between system identification and intervention design in dynamical systems. While previous research demonstrated how identifiable representation lea…
Responsible AI (RAI) Games and Ensembles
Yash Gupta, Runtian Zhai, Arun Suggala +1
Several recent works have studied the societal effects of AI; these include issues such as fairness, robustness, and safety. In many of these objectives, a learner seeks to minimiz…
Sample based Explanations via Generalized Representers
Che-Ping Tsai, Chih-Kuan Yeh, Pradeep Ravikumar
We propose a general class of sample based explanations of machine learning models, which we term generalized representers. To measure the effect of a training sample on a model's…
Identifying Representations for Intervention Extrapolation
Sorawit Saengkyongam, Elan Rosenfeld, Pradeep Ravikumar +2
The premise of identifiable and causal representation learning is to improve the current representation learning paradigm in terms of generalizability or robustness. Despite recent…