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Found 29 papers, 3 papers with code
Minimax Rate for Learning From Pairwise Comparisons in the BTL Model
We consider the problem of learning the qualities w_1, ... , w_n of a collection of items by performing noisy comparisons among them.
Sample Complexity of the Linear Quadratic Regulator: A Reinforcement Learning Lens
We provide the first known algorithm that provably achieves $\varepsilon$-optimality within $\widetilde{\mathcal{O}}(1/\varepsilon)$ function evaluations for the discounted discrete-time LQR problem with unknown parameters, without relying on two-point gradient estimates.
One-Shot Averaging for Distributed TD($λ$) Under Markov Sampling
We consider a distributed setup for reinforcement learning, where each agent has a copy of the same Markov Decision Process but transitions are sampled from the corresponding Markov chain independently by each agent.
Convex SGD: Generalization Without Early Stopping
We consider the generalization error associated with stochastic gradient descent on a smooth convex function over a compact set.
On the Performance of Temporal Difference Learning With Neural Networks
Neural Temporal Difference (TD) Learning is an approximate temporal difference method for policy evaluation that uses a neural network for function approximation.
Distributed TD(0) with Almost No Communication
We provide a new non-asymptotic analysis of distributed temporal difference learning with linear function approximation.
Closing the gap between SVRG and TD-SVRG with Gradient Splitting
Our main result is a geometric convergence bound with predetermined learning rate of $1/8$, which is identical to the convergence bound available for SVRG in the convex setting.
A Small Gain Analysis of Single Timescale Actor Critic
We consider a version of actor-critic which uses proportional step-sizes and only one critic update with a single sample from the stationary distribution per actor step.
Communication-efficient SGD: From Local SGD to One-Shot Averaging
While it is possible to obtain a linear reduction in the variance by averaging all the stochastic gradients at every step, this requires a lot of communication between the workers and the server, which can dramatically reduce the gains from parallelism.
Distributed TD(0) with Almost No Communication
In the global state model, we show that the convergence rate of our distributed one-shot averaging method matches the known convergence rate of TD(0).
Temporal Difference Learning as Gradient Splitting
Temporal difference learning with linear function approximation is a popular method to obtain a low-dimensional approximation of the value function of a policy in a Markov Decision Process.
Adversarial Crowdsourcing Through Robust Rank-One Matrix Completion
We consider the problem of reconstructing a rank-one matrix from a revealed subset of its entries when some of the revealed entries are corrupted with perturbations that are unknown and can be arbitrarily large.
Asymptotic Convergence Rate of Alternating Minimization for Rank One Matrix Completion
We study alternating minimization for matrix completion in the simplest possible setting: completing a rank-one matrix from a revealed subset of the entries.
Local SGD With a Communication Overhead Depending Only on the Number of Workers
While the initial analysis of Local SGD showed it needs $\Omega ( \sqrt{T} )$ communications for $T$ local gradient steps in order for the error to scale proportionately to $1/(nT)$, this has been successively improved in a string of papers, with the state-of-the-art requiring $\Omega \left( n \left( \mbox{ polynomial in log } (T) \right) \right)$ communications.
Asymptotic Network Independence in Distributed Stochastic Optimization for Machine Learning
We provide a discussion of several recent results which, in certain scenarios, are able to overcome a barrier in distributed stochastic optimization for machine learning.
A Non-Asymptotic Analysis of Network Independence for Distributed Stochastic Gradient Descent
This paper is concerned with minimizing the average of $n$ cost functions over a network, in which agents may communicate and exchange information with their peers in the network.
Gradient Descent for Sparse Rank-One Matrix Completion for Crowd-Sourced Aggregation of Sparsely Interacting Workers
We consider worker skill estimation for the single-coin Dawid-Skene crowdsourcing model.
Graph Resistance and Learning from Pairwise Comparisons
The algorithm has a relative error decay that scales with the square root of the graph resistance, and provide a matching lower bound (up to log factors).
Robust Asynchronous Stochastic Gradient-Push: Asymptotically Optimal and Network-Independent Performance for Strongly Convex Functions
We consider the standard model of distributed optimization of a sum of functions $F(\bz) = \sum_{i=1}^n f_i(\bz)$, where node $i$ in a network holds the function $f_i(\bz)$.
Optimization and Control
Network Topology and Communication-Computation Tradeoffs in Decentralized Optimization
In decentralized optimization, nodes cooperate to minimize an overall objective function that is the sum (or average) of per-node private objective functions.
Optimization and Control Distributed, Parallel, and Cluster Computing Multiagent Systems
Crowdsourcing with Sparsely Interacting Workers
We then formulate a weighted rank-one optimization problem to estimate skills based on observations on an irreducible, aperiodic interaction graph.
Distributed Learning for Cooperative Inference
We study the problem of cooperative inference where a group of agents interact over a network and seek to estimate a joint parameter that best explains a set of observations.
Distributed Gaussian Learning over Time-varying Directed Graphs
We show a convergence rate of $O(1/k)$ with the constant term depending on the number of agents and the topology of the network.
A Tutorial on Distributed (Non-Bayesian) Learning: Problem, Algorithms and Results
We overview some results on distributed learning with focus on a family of recently proposed algorithms known as non-Bayesian social learning.
Geometrically Convergent Distributed Optimization with Uncoordinated Step-Sizes
A recent algorithmic family for distributed optimization, DIGing's, have been shown to have geometric convergence over time-varying undirected/directed graphs.
Distributed Learning with Infinitely Many Hypotheses
We consider a distributed learning setup where a network of agents sequentially access realizations of a set of random variables with unknown distributions.
Stochastic Gradient-Push for Strongly Convex Functions on Time-Varying Directed Graphs
We investigate the convergence rate of the recently proposed subgradient-push method for distributed optimization over time-varying directed graphs.
Optimization and Control Systems and Control
Cooperative learning in multi-agent systems from intermittent measurements
Motivated by the problem of tracking a direction in a decentralized way, we consider the general problem of cooperative learning in multi-agent systems with time-varying connectivity and intermittent measurements.
Distributed Subgradient Methods and Quantization Effects
We consider a convex unconstrained optimization problem that arises in a network of agents whose goal is to cooperatively optimize the sum of the individual agent objective functions through local computations and communications.
Optimization and Control
