Re assumption 1: Shifting the data points is a good idea, but it might cause problems. In our current work, we focus on theory and datasets satisfying assumption 1. We will rephrase the sentence as follows: "In these scenarios, In the present work, we aim to improve the efficiency of the MIPS problem in algorithmic perspective. GPU can process multiple queries in parallel. Algorithm 1) and the indices of visited vertices can be arbitrarily large.

We present simple differentially private estimators for the mean and covariance of multivariate sub-Gaussian data that are accurate at small sample sizes. We demonstrate the effectiveness of our algorithms both theoretically and empirically using synthetic and real-world datasets--showing that their asymptotic error rates match the state-of-the-art theoretical bounds, and that they concretely outperform all previous methods. Specifically, previous estimators either have weak empirical accuracy at small sample sizes, perform poorly for multivariate data, or require the user to provide strong a priori estimates for the parameters.

This paper develops a new class of nonconvex regularizers for low-rank matrix recovery. Many regularizers are motivated as convex relaxations of the matrix rank function. Our new factor group-sparse regularizers are motivated as a relaxation of the number of nonzero columns in a factorization of the matrix. These nonconvex regularizers are sharper than the nuclear norm; indeed, we show they are related to Schatten-p norms with arbitrarily small 0 < p 1. Moreover, these factor group-sparse regularizers can be written in a factored form that enables efficient and effective nonconvex optimization; notably, the method does not use singular value decomposition. We provide generalization error bounds for low-rank matrix completion which show improved upper bounds for Schatten-p norm reglarization as p decreases. Compared to the max norm and the factored formulation of the nuclear norm, factor group-sparse regularizers are more efficient, accurate, and robust to the initial guess of rank. Experiments show promising performance of factor group-sparse regularization for low-rank matrix completion and robust principal component analysis.

A.1 Stochastic gradient descent We extend Algorithm 1 to stochastic gradient descent (SGD). Algorithm 2 provides the framework for teleportation in SGD. A.2 Data transformation Algorithm 3 here modifies Algorithm 1 to allow transformations on both parameters and data. The group actions on data at all teleportation steps can be precomposed as a function f and applied to the input data at inference time. In this section, we derive the group actions for the test functions and multi-layer neural networks.

Existing gradient-based optimization methods update parameters locally, in a direction that minimizes the loss function. We study a different approach, symmetry teleportation, that allows parameters to travel a large distance on the loss level set, in order to improve the convergence speed in subsequent steps. Teleportation exploits symmetries in the loss landscape of optimization problems. We derive loss-invariant group actions for test functions in optimization and multi-layer neural networks, and prove a necessary condition for teleportation to improve convergence rate. We also show that our algorithm is closely related to second order methods. Experimentally, we show that teleportation improves the convergence speed of gradient descent and AdaGrad for several optimization problems including test functions, multi-layer regressions, and MNIST classification.

The constrained Markov decision process (CMDP) framework emerges as an important reinforcement learning approach for imposing safety or other critical objectives while maximizing cumulative reward. However, the current understanding of how to learn efficiently in a CMDP environment with a potentially infinite number of states remains under investigation, particularly when function approximation is applied to the value functions.

Imbalanced learning (IL), i.e., learning unbiased models from class-imbalanced data, is a challenging problem. Typical IL methods including resampling and reweighting were designed based on some heuristic assumptions. They often suffer from unstable performance, poor applicability, and high computational cost in complex tasks where their assumptions do not hold.

We thank all reviewers for the constructive comments! We will carefully resolve all writing, format, and notation issues. These results will be included in the camera-ready version. R: Our main goal is to design an efficient, concise, and practical IL framework. It is nearly impossible to make instance-level decisions by using a complex meta-sampler (e.g., set a large output layer R: For clarity, Eq. 3 shows the unnormalized sampling weights (noted in the paper).

Video generation primarily aims to model authentic and customized motion across frames, making understanding and controlling the motion a crucial topic.

Diffusion models have emerged as effective distribution estimators in vision, language, and reinforcement learning, but their use as priors in downstream tasks poses an intractable posterior inference problem.