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Found 11 papers, 2 papers with code
Improving Efficiency of Diffusion Models via Multi-Stage Framework and Tailored Multi-Decoder Architectures
This is due to the necessity of tracking extensive forward and reverse diffusion trajectories, and employing a large model with numerous parameters across multiple timesteps (i. e., noise levels).
Community Detection in High-Dimensional Graph Ensembles
Detecting communities in high-dimensional graphs can be achieved by applying random matrix theory where the adjacency matrix of the graph is modeled by a Stochastic Block Model (SBM).
Efficient Compression of Overparameterized Deep Models through Low-Dimensional Learning Dynamics
We empirically evaluate the effectiveness of our compression technique on matrix recovery problems.
Algebraic and Statistical Properties of the Ordinary Least Squares Interpolator
Deep learning research has uncovered the phenomenon of benign overfitting for over-parameterized statistical models, which has drawn significant theoretical interest in recent years.
Errors-in-variables Fréchet Regression with Low-rank Covariate Approximation
Fr\'echet regression has emerged as a promising approach for regression analysis involving non-Euclidean response variables.
Robustness-preserving Lifelong Learning via Dataset Condensation
Most work in this learning paradigm has focused on resolving the problem of 'catastrophic forgetting,' which refers to a notorious dilemma between improving model accuracy over new data and retaining accuracy over previous data.
Local Minima Structures in Gaussian Mixture Models
As the objective function is non-convex, there can be multiple local minima that are not globally optimal, even for well-separated mixture models.
Sample Efficient Reinforcement Learning via Low-Rank Matrix Estimation
As our key contribution, we develop a simple, iterative learning algorithm that finds $\epsilon$-optimal $Q$-function with sample complexity of $\widetilde{O}(\frac{1}{\epsilon^{\max(d_1, d_2)+2}})$ when the optimal $Q$-function has low rank $r$ and the discounting factor $\gamma$ is below a certain threshold.
On Robustness of Principal Component Regression
As an important contribution to the Synthetic Control literature, we establish that an (approximate) linear synthetic control exists in the setting of a generalized factor model; traditionally, the existence of a synthetic control needs to be assumed to exist as an axiom.
Learning RUMs: Reducing Mixture to Single Component via PCA
Despite the success of RUMs in various domains and the versatility of mixture RUMs to capture the heterogeneity in preferences, there has been only limited progress in learning a mixture of RUMs from partial data such as pairwise comparisons.
Blind Regression: Nonparametric Regression for Latent Variable Models via Collaborative Filtering
In contrast with classical regression, the features $x = (x_1(u), x_2(i))$ are not observed, making it challenging to apply standard regression methods to predict the unobserved ratings.
