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Found 15 papers, 0 papers with code
Stochastic Compositional Gradient Descent: Algorithms for Minimizing Compositions of Expected-Value Functions
For smooth convex problems, the SCGD can be accelerated to converge at a rate of $O(k^{-2/7})$ in the general case and $O(k^{-4/5})$ in the strongly convex case.
Testing and Confidence Intervals for High Dimensional Proportional Hazards Model
This paper proposes a decorrelation-based approach to test hypotheses and construct confidence intervals for the low dimensional component of high dimensional proportional hazards models.
Accelerating Stochastic Composition Optimization
The ASC-PG is the first proximal gradient method for the stochastic composition problem that can deal with nonsmooth regularization penalty.
Max-Norm Optimization for Robust Matrix Recovery
This paper studies the matrix completion problem under arbitrary sampling schemes.
Misspecified Nonconvex Statistical Optimization for Phase Retrieval
Existing nonconvex statistical optimization theory and methods crucially rely on the correct specification of the underlying "true" statistical models.
Inductive Bias of Gradient Descent based Adversarial Training on Separable Data
Specifically, we show that when the adversarial perturbation during training has bounded $\ell_2$-norm, the classifier learned by gradient descent based adversarial training converges in direction to the maximum $\ell_2$-norm margin classifier at the rate of $\tilde{\mathcal{O}}(1/\sqrt{T})$, significantly faster than the rate $\mathcal{O}(1/\log T)$ of training with clean data.
Implicit Bias of Gradient Descent based Adversarial Training on Separable Data
Specifically, we show that for any fixed iteration $T$, when the adversarial perturbation during training has proper bounded L2 norm, the classifier learned by gradient descent based adversarial training converges in direction to the maximum L2 norm margin classifier at the rate of $O(1/\sqrt{T})$, significantly faster than the rate $O(1/\log T}$ of training with clean data.
Nearly Dimension-Independent Sparse Linear Bandit over Small Action Spaces via Best Subset Selection
We consider the stochastic contextual bandit problem under the high dimensional linear model.
Risk-Sensitive Deep RL: Variance-Constrained Actor-Critic Provably Finds Globally Optimal Policy
In particular, we focus on a variance-constrained policy optimization problem where the goal is to find a policy that maximizes the expected value of the long-run average reward, subject to a constraint that the long-run variance of the average reward is upper bounded by a threshold.
Implicit Regularization of Bregman Proximal Point Algorithm and Mirror Descent on Separable Data
For any BPPA instantiated with a fixed Bregman divergence, we provide a lower bound of the margin obtained by BPPA with respect to an arbitrarily chosen norm.
Lagrangian Inference for Ranking Problems
Our proposed method aims to infer general ranking properties of the BTL model.
Stochastic Compositional Optimization with Compositional Constraints
Stochastic compositional optimization (SCO) has attracted considerable attention because of its broad applicability to important real-world problems.
Combinatorial Inference on the Optimal Assortment in Multinomial Logit Models
Assortment optimization has received active explorations in the past few decades due to its practical importance.
PASTA: Pessimistic Assortment Optimization
The objective is to use the offline dataset to find an optimal assortment.
Pivotal Estimation of Linear Discriminant Analysis in High Dimensions
We consider the linear discriminant analysis problem in the high-dimensional settings.
