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Found 14 papers, 1 papers with code
In-Context Former: Lightning-fast Compressing Context for Large Language Model
With the rising popularity of Transformer-based large language models (LLMs), reducing their high inference costs has become a significant research focus.
Boosting Gradient Ascent for Continuous DR-submodular Maximization
The fundamental idea of our boosting technique is to exploit non-oblivious search to derive a novel auxiliary function $F$, whose stationary points are excellent approximations to the global maximum of the original DR-submodular objective $f$.
An Online Algorithm for Chance Constrained Resource Allocation
This paper studies the online stochastic resource allocation problem (RAP) with chance constraints.
Communication-Efficient Decentralized Online Continuous DR-Submodular Maximization
Maximizing a monotone submodular function is a fundamental task in machine learning, economics, and statistics.
Online Learning for Non-monotone Submodular Maximization: From Full Information to Bandit Feedback
In this paper, we revisit the online non-monotone continuous DR-submodular maximization problem over a down-closed convex set, which finds wide real-world applications in the domain of machine learning, economics, and operations research.
Stochastic Continuous Submodular Maximization: Boosting via Non-oblivious Function
In the online setting, for the first time we consider the adversarial delays for stochastic gradient feedback, under which we propose a boosting online gradient algorithm with the same non-oblivious function $F$.
Online Allocation Problem with Two-sided Resource Constraints
As a result, only limited violations of constraints or pessimistic competitive bounds could be guaranteed.
Adam revisited: a weighted past gradients perspective
Though many algorithms, such as AMSGRAD and ADAMNC, have been proposed to fix the non-convergence issues, achieving a data-dependent regret bound similar to or better than ADAGRAD is still a challenge to these methods.
Symmetric Cross Entropy for Robust Learning with Noisy Labels
In this paper, we show that DNN learning with Cross Entropy (CE) exhibits overfitting to noisy labels on some classes ("easy" classes), but more surprisingly, it also suffers from significant under learning on some other classes ("hard" classes).
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Joint Semantic Domain Alignment and Target Classifier Learning for Unsupervised Domain Adaptation
Unsupervised domain adaptation aims to transfer the classifier learned from the source domain to the target domain in an unsupervised manner.
Efficient Rank Minimization via Solving Non-convexPenalties by Iterative Shrinkage-Thresholding Algorithm
Rank minimization (RM) is a wildly investigated task of finding solutions by exploiting low-rank structure of parameter matrices.
Universal Stagewise Learning for Non-Convex Problems with Convergence on Averaged Solutions
For example, there is still a lack of theories of convergence for SGD and its variants that use stagewise step size and return an averaged solution in practice.
Fast Stochastic AUC Maximization with $O(1/n)$-Convergence Rate
In this paper, we consider statistical learning with AUC (area under ROC curve) maximization in the classical stochastic setting where one random data drawn from an unknown distribution is revealed at each iteration for updating the model.
SADAGRAD: Strongly Adaptive Stochastic Gradient Methods
Although the convergence rates of existing variants of ADAGRAD have a better dependence on the number of iterations under the strong convexity condition, their iteration complexities have a explicitly linear dependence on the dimensionality of the problem.
