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Found 21 papers, 2 papers with code
Optimal Compression of Unit Norm Vectors in the High Distortion Regime
We approach this problem in a worst-case scenario, without any prior information on the vector, but allowing for the use of randomized compression maps.
An Improved Algorithm for Clustered Federated Learning
\texttt{SR-FCA} treats each user as a singleton cluster as an initialization, and then successively refine the cluster estimation via exploiting similar users belonging to the same cluster.
Exploration in Linear Bandits with Rich Action Sets and its Implications for Inference
Furthermore, while the previous result is shown to hold only in the asymptotic regime (as $n \to \infty$), our result for these "locally rich" action spaces is any-time.
Model Selection in Reinforcement Learning with General Function Approximations
We consider model selection for classic Reinforcement Learning (RL) environments -- Multi Armed Bandits (MABs) and Markov Decision Processes (MDPs) -- under general function approximations.
Decentralized Competing Bandits in Non-Stationary Matching Markets
We propose and analyze a decentralized and asynchronous learning algorithm, namely Decentralized Non-stationary Competing Bandits (\texttt{DNCB}), where the agents play (restrictive) successive elimination type learning algorithms to learn their preference over the arms.
On Learning Mixture of Linear Regressions in the Non-Realizable Setting
In this paper we show that a version of the popular alternating minimization (AM) algorithm finds the best fit lines in a dataset even when a realizable model is not assumed, under some regularity conditions on the dataset and the initial points, and thereby provides a solution for the ERM.
Breaking the $\sqrt{T}$ Barrier: Instance-Independent Logarithmic Regret in Stochastic Contextual Linear Bandits
The (poly) logarithmic regret of \texttt{LR-SCB} stems from two crucial facts: (a) the application of a norm adaptive algorithm to exploit the parameter estimation and (b) an analysis of the shifted linear contextual bandit algorithm, showing that shifting results in increasing regret.
Model Selection for Generic Reinforcement Learning
We propose and analyze a novel algorithm, namely \emph{Adaptive Reinforcement Learning (General)} (\texttt{ARL-GEN}) that adapts to the smallest such family where the true transition kernel $P^*$ lies.
Model Selection for Generic Contextual Bandits
We consider the problem of model selection for the general stochastic contextual bandits under the realizability assumption.
Adaptive Clustering and Personalization in Multi-Agent Stochastic Linear Bandits
We show that, for any agent, the regret scales as $\mathcal{O}(\sqrt{T/N})$, if the agent is in a `well separated' cluster, or scales as $\mathcal{O}(T^{\frac{1}{2} + \varepsilon}/(N)^{\frac{1}{2} -\varepsilon})$ if its cluster is not well separated, where $\varepsilon$ is positive and arbitrarily close to $0$.
LocalNewton: Reducing Communication Bottleneck for Distributed Learning
To enhance practicability, we devise an adaptive scheme to choose L, and we show that this reduces the number of local iterations in worker machines between two model synchronizations as the training proceeds, successively refining the model quality at the master.
Escaping Saddle Points in Distributed Newton's Method with Communication Efficiency and Byzantine Resilience
Moreover, we validate our theoretical findings with experiments using standard datasets and several types of Byzantine attacks, and obtain an improvement of $25\%$ with respect to first order methods in iteration complexity.
Distributed Newton Can Communicate Less and Resist Byzantine Workers
We develop a distributed second order optimization algorithm that is communication-efficient as well as robust against Byzantine failures of the worker machines.
An Efficient Framework for Clustered Federated Learning
We address the problem of federated learning (FL) where users are distributed and partitioned into clusters.
Problem-Complexity Adaptive Model Selection for Stochastic Linear Bandits
This is the first algorithm that achieves such model selection guarantees.
Alternating Minimization Converges Super-Linearly for Mixed Linear Regression
Furthermore, we compare AM with a gradient based heuristic algorithm empirically and show that AM dominates in iteration complexity as well as wall-clock time.
Communication-Efficient and Byzantine-Robust Distributed Learning with Error Feedback
Moreover, we analyze the compressed gradient descent algorithm with error feedback (proposed in \cite{errorfeed}) in a distributed setting and in the presence of Byzantine worker machines.
Max-Affine Regression: Provable, Tractable, and Near-Optimal Statistical Estimation
Max-affine regression refers to a model where the unknown regression function is modeled as a maximum of $k$ unknown affine functions for a fixed $k \geq 1$.
Robust Federated Learning in a Heterogeneous Environment
Then, leveraging the statistical model, we solve the robust heterogeneous Federated Learning problem \emph{optimally}; in particular our algorithm matches the lower bound on the estimation error in dimension and the number of data points.
Online Scoring with Delayed Information: A Convex Optimization Viewpoint
We argue that the error in the score estimate accumulated over $T$ iterations is small if the regret of the online convex game is small.
Misspecified Linear Bandits
Regret guarantees for state-of-the-art linear bandit algorithms such as Optimism in the Face of Uncertainty Linear bandit (OFUL) hold under the assumption that the arms expected rewards are perfectly linear in their features.
