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Found 20 papers, 1 papers with code
$O(d/T)$ Convergence Theory for Diffusion Probabilistic Models under Minimal Assumptions
Our analysis shows that, provided $\ell_{2}$-accurate estimates of the score functions, the total variation distance between the target and generated distributions is upper bounded by $O(d/T)$ (ignoring logarithmic factors), where $d$ is the data dimensionality and $T$ is the number of steps.
A Score-Based Density Formula, with Applications in Diffusion Generative Models
Score-based generative models (SGMs) have revolutionized the field of generative modeling, achieving unprecedented success in generating realistic and diverse content.
Analysis of the ICML 2023 Ranking Data: Can Authors' Opinions of Their Own Papers Assist Peer Review in Machine Learning?
We focus on the Isotonic Mechanism, which calibrates raw review scores using author-provided rankings.
When can weak latent factors be statistically inferred?
Another notable advancement of our theory is on PCA inference $ - $ for example, under the regime where $N\asymp T$, we show that the asymptotic normality for the PCA-based estimator holds as long as the signal-to-noise ratio (SNR) grows faster than a polynomial rate of $\log N$.
Adapting to Unknown Low-Dimensional Structures in Score-Based Diffusion Models
This paper investigates score-based diffusion models when the underlying target distribution is concentrated on or near low-dimensional manifolds within the higher-dimensional space in which they formally reside, a common characteristic of natural image distributions.
Enhancing AI Diagnostics: Autonomous Lesion Masking via Semi-Supervised Deep Learning
This model is then iteratively refined for the domain adaptation task, generating pseudo-masks for our private, unannotated breast US dataset.
Entrywise Inference for Missing Panel Data: A Simple and Instance-Optimal Approach
Longitudinal or panel data can be represented as a matrix with rows indexed by units and columns indexed by time.
The Isotonic Mechanism for Exponential Family Estimation
We demonstrate that an author is incentivized to provide accurate rankings when her utility takes the form of a convex additive function of the adjusted review scores.
Minimax-Optimal Reward-Agnostic Exploration in Reinforcement Learning
This paper studies reward-agnostic exploration in reinforcement learning (RL) -- a scenario where the learner is unware of the reward functions during the exploration stage -- and designs an algorithm that improves over the state of the art.
Learning Gaussian Mixtures Using the Wasserstein-Fisher-Rao Gradient Flow
Gaussian mixture models form a flexible and expressive parametric family of distributions that has found applications in a wide variety of applications.
Model-Based Reinforcement Learning for Offline Zero-Sum Markov Games
This paper makes progress towards learning Nash equilibria in two-player zero-sum Markov games from offline data.
Model-based Reinforcement Learning
reinforcement-learning
+1
The Efficacy of Pessimism in Asynchronous Q-Learning
This paper is concerned with the asynchronous form of Q-learning, which applies a stochastic approximation scheme to Markovian data samples.
Inference for Heteroskedastic PCA with Missing Data
This paper studies how to construct confidence regions for principal component analysis (PCA) in high dimension, a problem that has been vastly under-explored.
Sample-Efficient Reinforcement Learning for Linearly-Parameterized MDPs with a Generative Model
Our results show that for arbitrarily large-scale MDP, both the model-based approach and Q-learning are sample-efficient when $K$ is relatively small, and hence the title of this paper.
Convex and Nonconvex Optimization Are Both Minimax-Optimal for Noisy Blind Deconvolution under Random Designs
We investigate the effectiveness of convex relaxation and nonconvex optimization in solving bilinear systems of equations under two different designs (i. e.$~$a sort of random Fourier design and Gaussian design).
Efficient Clustering for Stretched Mixtures: Landscape and Optimality
This paper considers a canonical clustering problem where one receives unlabeled samples drawn from a balanced mixture of two elliptical distributions and aims for a classifier to estimate the labels.
Bridging Convex and Nonconvex Optimization in Robust PCA: Noise, Outliers, and Missing Data
This paper delivers improved theoretical guarantees for the convex programming approach in low-rank matrix estimation, in the presence of (1) random noise, (2) gross sparse outliers, and (3) missing data.
Inference and Uncertainty Quantification for Noisy Matrix Completion
As a byproduct, we obtain a sharp characterization of the estimation accuracy of our de-biased estimators, which, to the best of our knowledge, are the first tractable algorithms that provably achieve full statistical efficiency (including the preconstant).
Noisy Matrix Completion: Understanding Statistical Guarantees for Convex Relaxation via Nonconvex Optimization
This paper studies noisy low-rank matrix completion: given partial and noisy entries of a large low-rank matrix, the goal is to estimate the underlying matrix faithfully and efficiently.
Motion Saliency Based Automatic Delineation of Glottis Contour in High-speed Digital Images
In recent years, high-speed videoendoscopy (HSV) has significantly aided the diagnosis of voice pathologies and furthered the understanding the voice production in recent years.
