40 citations · 147 across the 23 of their papers we have counts for
24 papers
A Systematic Study of Joint Representation Learning on Protein Sequences and Structures
Zuobai Zhang, Chuanrui Wang, Minghao Xu +4
Learning effective protein representations is critical in a variety of tasks in biology such as predicting protein functions. Recent sequence representation learning methods based…
Pre-Training Protein Encoder via Siamese Sequence-Structure Diffusion Trajectory Prediction
Zuobai Zhang, Minghao Xu, Aurélie Lozano +3
Self-supervised pre-training methods on proteins have recently gained attention, with most approaches focusing on either protein sequences or structures, neglecting the exploration…
AlphaFold Distillation for Protein Design
Igor Melnyk, Aurelie Lozano, Payel Das +1
Inverse protein folding, the process of designing sequences that fold into a specific 3D structure, is crucial in bio-engineering and drug discovery. Traditional methods rely on ex…
Protein Representation Learning by Geometric Structure Pretraining
Zuobai Zhang, Minghao Xu, Arian Jamasb +4
Learning effective protein representations is critical in a variety of tasks in biology such as predicting protein function or structure. Existing approaches usually pretrain prote…
Directed Graph Auto-Encoders
Georgios Kollias, Vasileios Kalantzis, Tsuyoshi Idé +2
We introduce a new class of auto-encoders for directed graphs, motivated by a direct extension of the Weisfeiler-Leman algorithm to pairs of node labels. The proposed model learns…
Benchmarking deep generative models for diverse antibody sequence design
Igor Melnyk, Payel Das, Vijil Chenthamarakshan +1
Computational protein design, i.e. inferring novel and diverse protein sequences consistent with a given structure, remains a major unsolved challenge. Recently, deep generative mo…
A Revision of Neural Tangent Kernel-based Approaches for Neural Networks
Kyung-Su Kim, Aurélie C. Lozano, Eunho Yang
Recent theoretical works based on the neural tangent kernel (NTK) have shed light on the optimization and generalization of over-parameterized networks, and partially bridge the ga…
A General Family of Stochastic Proximal Gradient Methods for Deep Learning
Jihun Yun, Aurelie C. Lozano, Eunho Yang
We study the training of regularized neural networks where the regularizer can be non-smooth and non-convex. We propose a unified framework for stochastic proximal gradient descent…
Stochastic Gradient Methods with Block Diagonal Matrix Adaptation
Jihun Yun, Aurelie C. Lozano, Eunho Yang
Adaptive gradient approaches that automatically adjust the learning rate on a per-feature basis have been very popular for training deep networks. This rich class of algorithms inc…
On Extensions of CLEVER: A Neural Network Robustness Evaluation Algorithm
Tsui-Wei Weng, Huan Zhang, Pin-Yu Chen +3
CLEVER (Cross-Lipschitz Extreme Value for nEtwork Robustness) is an Extreme Value Theory (EVT) based robustness score for large-scale deep neural networks (DNNs). In this paper, we…
M-estimation with the Trimmed l1 Penalty
Jihun Yun, Peng Zheng, Eunho Yang +2
We study high-dimensional estimators with the trimmed penalty, which leaves the largest parameter entries penalty-free. While optimization techniques for this nonconve…
Simultaneous Parameter Learning and Bi-Clustering for Multi-Response Models
Ming Yu, Karthikeyan Natesan Ramamurthy, Addie Thompson +1
We consider multi-response and multitask regression models, where the parameter matrix to be estimated is expected to have an unknown grouping structure. The groupings can be along…
Multitask Learning using Task Clustering with Applications to Predictive Modeling and GWAS of Plant Varieties
Ming Yu, Addie M. Thompson, Karthikeyan Natesan Ramamurthy +2
Inferring predictive maps between multiple input and multiple output variables or tasks has innumerable applications in data science. Multi-task learning attempts to learn the maps…
Learning task structure via sparsity grouped multitask learning
Meghana Kshirsagar, Eunho Yang, Aurélie C. Lozano
Sparse mapping has been a key methodology in many high-dimensional scientific problems. When multiple tasks share the set of relevant features, learning them jointly in a group dra…
Neurogenesis-Inspired Dictionary Learning: Online Model Adaption in a Changing World
Sahil Garg, Irina Rish, Guillermo Cecchi +1
In this paper, we focus on online representation learning in non-stationary environments which may require continuous adaptation of model architecture. We propose a novel online di…
Generalized Kalman Smoothing: Modeling and Algorithms
A. Y. Aravkin, J. V. Burke, L. Ljung +2
State-space smoothing has found many applications in science and engineering. Under linear and Gaussian assumptions, smoothed estimates can be obtained using efficient recursions,…
Understanding Innovation to Drive Sustainable Development
Prasanna Sattigeri, Aurélie Lozano, Aleksandra Mojsilović +2
Innovation is among the key factors driving a country's economic and social growth. But what are the factors that make a country innovative? How do they differ across different par…
A General Family of Trimmed Estimators for Robust High-dimensional Data Analysis
Eunho Yang, Aurelie Lozano, Aleksandr Aravkin
We consider the problem of robustifying high-dimensional structured estimation. Robust techniques are key in real-world applications which often involve outliers and data corruptio…
Removing Clouds and Recovering Ground Observations in Satellite Image Sequences via Temporally Contiguous Robust Matrix Completion
Jialei Wang, Peder A. Olsen, Andrew R. Conn +1
We consider the problem of removing and replacing clouds in satellite image sequences, which has a wide range of applications in remote sensing. Our approach first detects and remo…
Robust Gaussian Graphical Modeling with the Trimmed Graphical Lasso
Eunho Yang, Aurélie C. Lozano
Gaussian Graphical Models (GGMs) are popular tools for studying network structures. However, many modern applications such as gene network discovery and social interactions analysi…
Scalable Matrix-valued Kernel Learning for High-dimensional Nonlinear Multivariate Regression and Granger Causality
Vikas Sindhwani, Ha Quang Minh, Aurelie Lozano
We propose a general matrix-valued multiple kernel learning framework for high-dimensional nonlinear multivariate regression problems. This framework allows a broad class of mixed…
Sparse Quantile Huber Regression for Efficient and Robust Estimation
Aleksandr Y. Aravkin, Anju Kambadur, Aurelie C. Lozano +1
We consider new formulations and methods for sparse quantile regression in the high-dimensional setting. Quantile regression plays an important role in many applications, including…
Minimum Distance Estimation for Robust High-Dimensional Regression
Aurélie C. Lozano, Nicolai Meinshausen
We propose a minimum distance estimation method for robust regression in sparse high-dimensional settings. The traditional likelihood-based estimators lack resilience against outli…
Scalable Matrix-valued Kernel Learning for High-dimensional Nonlinear Multivariate Regression and Granger Causality
Vikas Sindhwani, Minh Ha Quang, Aurelie C. Lozano
We propose a general matrix-valued multiple kernel learning framework for high-dimensional nonlinear multivariate regression problems. This framework allows a broad class of mixed…