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Found 10 papers, 3 papers with code
Large-Scale Non-convex Stochastic Constrained Distributionally Robust Optimization
We prove that our algorithm finds an $\epsilon$-stationary point with a computational complexity of $\mathcal O(\epsilon^{-3k_*-5})$, where $k_*$ is the parameter of the Cressie-Read divergence.
Factorized Tensor Networks for Multi-Task and Multi-Domain Learning
In this paper, we propose a factorized tensor network (FTN) that can achieve accuracy comparable to independent single-task/domain networks with a small number of additional parameters.
Robust Multimodal Learning with Missing Modalities via Parameter-Efficient Adaptation
We conduct a series of experiments to highlight the missing modality robustness of our proposed method on 5 different datasets for multimodal semantic segmentation, multimodal material segmentation, and multimodal sentiment analysis tasks.
MMSFormer: Multimodal Transformer for Material and Semantic Segmentation
Furthermore, our ablation studies also highlight the capacity of different input modalities to improve performance in the identification of different types of materials.
Ranked #1 on
Semantic Segmentation
on MCubeS (P)
Model-Free Robust Average-Reward Reinforcement Learning
Robust Markov decision processes (MDPs) address the challenge of model uncertainty by optimizing the worst-case performance over an uncertainty set of MDPs.
Robust Average-Reward Markov Decision Processes
We derive the robust Bellman equation for robust average-reward MDPs, prove that the optimal policy can be derived from its solution, and further design a robust relative value iteration algorithm that provably finds its solution, or equivalently, the optimal robust policy.
Incremental Task Learning with Incremental Rank Updates
Our method also offers better memory efficiency compared to episodic memory- and mask-based approaches.
Continual Learning via Low-Rank Network Updates
To update the network for a new task, we learn a low-rank (or rank-1) matrix and add that to the weights of every layer.
Scaling and Scalability: Provable Nonconvex Low-Rank Tensor Estimation from Incomplete Measurements
Tensors, which provide a powerful and flexible model for representing multi-attribute data and multi-way interactions, play an indispensable role in modern data science across various fields in science and engineering.
The Proximity Operator of the Log-Sum Penalty
The log-sum penalty is often adopted as a replacement for the $\ell_0$ pseudo-norm in compressive sensing and low-rank optimization.
Compressive Sensing
Optimization and Control
49J53, 49J52, 90C26
