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Found 14 papers, 4 papers with code
Decentralized Collaborative Learning Framework with External Privacy Leakage Analysis
This paper presents two methodological advancements in decentralized multi-task learning under privacy constraints, aiming to pave the way for future developments in next-generation Blockchain platforms.
Cardinality-Regularized Hawkes-Granger Model
In this paper, we propose a mathematically well-defined sparse causal learning framework based on a cardinality-regularized Hawkes process, which remedies the pathological issues of existing approaches.
Ensembling Graph Predictions for AMR Parsing
In many machine learning tasks, models are trained to predict structure data such as graphs.
Ranked #2 on
AMR Parsing
on LDC2020T02
(using extra training data)
FedDR -- Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization
These new algorithms can handle statistical and system heterogeneity, which are the two main challenges in federated learning, while achieving the best known communication complexity.
A Scalable MIP-based Method for Learning Optimal Multivariate Decision Trees
Several recent publications report advances in training optimal decision trees (ODT) using mixed-integer programs (MIP), due to algorithmic advances in integer programming and a growing interest in addressing the inherent suboptimality of heuristic approaches such as CART.
A Hybrid Stochastic Policy Gradient Algorithm for Reinforcement Learning
We propose a novel hybrid stochastic policy gradient estimator by combining an unbiased policy gradient estimator, the REINFORCE estimator, with another biased one, an adapted SARAH estimator for policy optimization.
A Unified Convergence Analysis for Shuffling-Type Gradient Methods
We also study uniformly randomized shuffling variants with different learning rates and model assumptions.
A Hybrid Stochastic Optimization Framework for Stochastic Composite Nonconvex Optimization
We introduce a new approach to develop stochastic optimization algorithms for a class of stochastic composite and possibly nonconvex optimization problems.
Hybrid Stochastic Gradient Descent Algorithms for Stochastic Nonconvex Optimization
We introduce a hybrid stochastic estimator to design stochastic gradient algorithms for solving stochastic optimization problems.
ProxSARAH: An Efficient Algorithmic Framework for Stochastic Composite Nonconvex Optimization
We also specify the algorithm to the non-composite case that covers existing state-of-the-arts in terms of complexity bounds.
DTN: A Learning Rate Scheme with Convergence Rate of $\mathcal{O}(1/t)$ for SGD
This paper has some inconsistent results, i. e., we made some failed claims because we did some mistakes for using the test criterion for a series.
Finite-Sum Smooth Optimization with SARAH
The total complexity (measured as the total number of gradient computations) of a stochastic first-order optimization algorithm that finds a first-order stationary point of a finite-sum smooth nonconvex objective function $F(w)=\frac{1}{n} \sum_{i=1}^n f_i(w)$ has been proven to be at least $\Omega(\sqrt{n}/\epsilon)$ for $n \leq \mathcal{O}(\epsilon^{-2})$ where $\epsilon$ denotes the attained accuracy $\mathbb{E}[ \|\nabla F(\tilde{w})\|^2] \leq \epsilon$ for the outputted approximation $\tilde{w}$ (Fang et al., 2018).
Characterization of Convex Objective Functions and Optimal Expected Convergence Rates for SGD
We study Stochastic Gradient Descent (SGD) with diminishing step sizes for convex objective functions.
When Does Stochastic Gradient Algorithm Work Well?
In this paper, we consider a general stochastic optimization problem which is often at the core of supervised learning, such as deep learning and linear classification.
