2.1k citations · 4.3k across the 48 of their papers we have counts for
51 papers
Density-Reweighted Entropic Optimal Transport: Decoupling Geometry from Sampling Density
Keyi Li, Yuval Kluger, Boris Landa
Dataset alignment is a central step in data analysis across science and engineering, where the goal is to match observations between datasets. Entropic Optimal Transport (EOT) offe…
Understanding and Enhancing Mask-Based Pretraining towards Universal Representations
Mingze Dong, Leda Wang, Yuval Kluger
Mask-based pretraining has become a cornerstone of modern large-scale models across language, vision, and recently biology. Despite its empirical success, its role and limits in le…
Injecting Measurement Information Yields a Fast and Noise-Robust Diffusion-Based Inverse Problem Solver
Jonathan Patsenker, Henry Li, Myeongseob Ko +2
Diffusion models have been firmly established as principled zero-shot solvers for linear and nonlinear inverse problems, owing to their powerful image prior and iterative sampling…
Euclidean Distance Deflation Under High-Dimensional Heteroskedastic Noise
Keyi Li, Yuval Kluger, Boris Landa
Pairwise Euclidean distance calculation is a fundamental step in many machine learning and data analysis algorithms. In real-world applications, however, these distances are freque…
Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps
Henry Li, Ronen Basri, Yuval Kluger
Cascaded models are multi-scale generative models with a marked capacity for producing perceptually impressive samples at high resolutions. In this work, we show that they can also…
Dual Diffusion for Unified Image Generation and Understanding
Zijie Li, Henry Li, Yichun Shi +4
Diffusion models have gained tremendous success in text-to-image generation, yet still lag behind with visual understanding tasks, an area dominated by autoregressive vision-langua…
Entropic Optimal Transport Eigenmaps for Nonlinear Alignment and Joint Embedding of High-Dimensional Datasets
Boris Landa, Yuval Kluger, Rong Ma
Embedding high-dimensional data into a low-dimensional space is an indispensable component of data analysis. In numerous applications, it is necessary to align and jointly embed mu…
Exponential weight averaging as damped harmonic motion
Jonathan Patsenker, Henry Li, Yuval Kluger
The exponential moving average (EMA) is a commonly used statistic for providing stable estimates of stochastic quantities in deep learning optimization. Recently, EMA has seen cons…
The Dyson Equalizer: Adaptive Noise Stabilization for Low-Rank Signal Detection and Recovery
Boris Landa, Yuval Kluger
Detecting and recovering a low-rank signal in a noisy data matrix is a fundamental task in data analysis. Typically, this task is addressed by inspecting and manipulating the spect…
Multi-modal Differentiable Unsupervised Feature Selection
Junchen Yang, Ofir Lindenbaum, Yuval Kluger +1
Multi-modal high throughput biological data presents a great scientific opportunity and a significant computational challenge. In multi-modal measurements, every sample is observed…
Autoregressive Generative Modeling with Noise Conditional Maximum Likelihood Estimation
Henry Li, Yuval Kluger
We introduce a simple modification to the standard maximum likelihood estimation (MLE) framework. Rather than maximizing a single unconditional likelihood of the data under the mod…
ManiFeSt: Manifold-based Feature Selection for Small Data Sets
David Cohen, Tal Shnitzer, Yuval Kluger +1
In this paper, we present a new method for few-sample supervised feature selection (FS). Our method first learns the manifold of the feature space of each class using kernels captu…
Neural Inverse Transform Sampler
Henry Li, Yuval Kluger
Any explicit functional representation of a density is hampered by two main obstacles when we wish to use it as a generative model: designing so that sampling is fast, and…
Deep Unsupervised Feature Selection by Discarding Nuisance and Correlated Features
Uri Shaham, Ofir Lindenbaum, Jonathan Svirsky +1
Modern datasets often contain large subsets of correlated features and nuisance features, which are not or loosely related to the main underlying structures of the data. Nuisance f…
Probabilistic Robust Autoencoders for Outlier Detection
Ofir Lindenbaum, Yariv Aizenbud, Yuval Kluger
Anomalies (or outliers) are prevalent in real-world empirical observations and potentially mask important underlying structures. Accurate identification of anomalous samples is cru…
Locally Sparse Neural Networks for Tabular Biomedical Data
Junchen Yang, Ofir Lindenbaum, Yuval Kluger
Tabular datasets with low-sample-size or many variables are prevalent in biomedicine. Practitioners in this domain prefer linear or tree-based models over neural networks since the…
On the Efficient Evaluation of the Azimuthal Fourier Components of the Green's Function for Helmholtz's Equation in Cylindrical Coordinates
James Garritano, Yuval Kluger, Vladimir Rokhlin +1
In this manuscript, we develop an efficient algorithm to evaluate the azimuthal Fourier components of the Green's function for the Helmholtz equation in cylindrical coordinates. A…
Biwhitening Reveals the Rank of a Count Matrix
Boris Landa, Thomas T. C. K. Zhang, Yuval Kluger
Estimating the rank of a corrupted data matrix is an important task in data analysis, most notably for choosing the number of components in PCA. Significant progress on this task w…
Spectral Top-Down Recovery of Latent Tree Models
Yariv Aizenbud, Ariel Jaffe, Meng Wang +5
Modeling the distribution of high dimensional data by a latent tree graphical model is a prevalent approach in multiple scientific domains. A common task is to infer the underlying…
Local Two-Sample Testing over Graphs and Point-Clouds by Random-Walk Distributions
Boris Landa, Rihao Qu, Joseph Chang +1
Rejecting the null hypothesis in two-sample testing is a fundamental tool for scientific discovery. Yet, aside from concluding that two samples do not come from the same probabilit…
-based Sparse Canonical Correlation Analysis
Ofir Lindenbaum, Moshe Salhov, Amir Averbuch +1
Canonical Correlation Analysis (CCA) models are powerful for studying the associations between two sets of variables. The canonically correlated representations, termed \textit{can…
Differentiable Unsupervised Feature Selection based on a Gated Laplacian
Ofir Lindenbaum, Uri Shaham, Jonathan Svirsky +2
Scientific observations may consist of a large number of variables (features). Identifying a subset of meaningful features is often ignored in unsupervised learning, despite its po…
Doubly-Stochastic Normalization of the Gaussian Kernel is Robust to Heteroskedastic Noise
Boris Landa, Ronald R. Coifman, Yuval Kluger
A fundamental step in many data-analysis techniques is the construction of an affinity matrix describing similarities between data points. When the data points reside in Euclidean…
Spectral neighbor joining for reconstruction of latent tree models
Ariel Jaffe, Noah Amsel, Yariv Aizenbud +3
A common assumption in multiple scientific applications is that the distribution of observed data can be modeled by a latent tree graphical model. An important example is phylogene…