A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Study hypothesis testing under quantized samples with communication constraints, achieving near-optimal sample complexity.
We study distributed estimation methods under communication constraints in a distributed version of the nonparametric random design regression model. We derive minimax lower bounds and exhibit methods that attain those bounds. Moreover, we show that adaptive estimation is possible in this setting.
Study binary hypothesis testing with privacy and communication constraints.
New framework for distributed nonparametric estimation under slow communication.
This work proposes ACTC for adaptive distributed learning under communication constraints.
Optimal distributed testing under communication constraints with shared randomness.
We study distributed estimation of a Gaussian mean under communication constraints in a decision theoretical framework. Minimax rates of convergence, which characterize the tradeoff between the communication costs and statistical accuracy, are established in both the univariate and multivariate settings. Communication-…
Distributed sensors compress and send features to a fusion center for linear regression.
Study quantile reward identification with 1-bit feedback constraints.
SHIFT method optimally estimates heterogeneous discrete distributions with limited communication.
We break dimension dependence in sparse distribution estimation with communication constraints.
A decentralized policy achieves logarithmic regret for multi-agent MAB problems with communication constraints.
Paper addresses privacy and communication in distributed learning, achieving optimal performance.
New method for distributed online learning with communication constraints reduces joint regret.
Many machine learning approaches are characterized by information constraints on how they interact with the training data. These include memory and sequential access constraints (e.g. fast first-order methods to solve stochastic optimization problems); communication constraints (e.g. distributed learning); partial acce…
Study on distributed nonparametric function estimation with optimal rate and cost of adaptation.
This study analyzes communication constraints in MoE architectures using information theory.
New algorithm tackles batched stochastic linear bandits with 1-bit communication constraints.
Paper studies distributed learning with limited communication bits, achieving optimal error exponents.
We consider a decentralized multi-agent Multi Armed Bandit (MAB) setup consisting of agents, solving the same MAB instance to minimize individual cumulative regret. In our model, agents collaborate by exchanging messages through pairwise gossip style communications on an arbitrary connected graph. We develop two no…
Network agents solve adaptive regression problems with compressed signals.
As convolutional neural networks (CNNs) enable state-of-the-art computer vision applications, their high energy consumption has emerged as a key impediment to their deployment on embedded and mobile devices. Towards efficient image classification under hardware constraints, prior work has proposed adaptive CNNs, i.e., …
A central result in statistical theory is Pinsker's theorem, which characterizes the minimax rate in the normal means model of nonparametric estimation. In this paper, we present an extension to Pinsker's theorem where estimation is carried out under storage or communication constraints. In particular, we place limits …
A new approach for cooperative multi-agent reinforcement learning with limited communication, reducing the number of communication rounds.
Paper develops an efficient mean estimator for 1-bit communication constraints.
We develop model free PAC performance guarantees for multiple concurrent MDPs, extending recent works where a single learner interacts with multiple non-interacting agents in a noise free environment. Our framework allows noisy and resource limited communication between agents, and develops novel PAC guarantees in this…
This work addresses the instability in asynchronous data parallel optimization. It does so by introducing a novel distributed optimizer which is able to efficiently optimize a centralized model under communication constraints. The optimizer achieves this by pushing a normalized sequence of first-order gradients to a pa…
In many real-world applications of machine learning, data are distributed across many clients and cannot leave the devices they are stored on. Furthermore, each client's data, computational resources and communication constraints may be very different. This setting is known as federated learning, in which privacy is a …
In this paper, learning of tree-structured Gaussian graphical models from distributed data is addressed. In our model, samples are stored in a set of distributed machines where each machine has access to only a subset of features. A central machine is then responsible for learning the structure based on received messag…
Centralised training with decentralised execution is an important setting for cooperative deep multi-agent reinforcement learning due to communication constraints during execution and computational tractability in training. In this paper, we analyse value-based methods that are known to have superior performance in com…
The paper analyzes how disturbances affect the convergence of algorithms in complex systems.
This paper studies the problem of nonparametric estimation of a smooth function with data distributed across multiple machines. We assume an independent sample from a white noise model is collected at each machine, and an estimator of the underlying true function needs to be constructed at a central machine. We place l…
We formulate the notion of minimax estimation under storage or communication constraints, and prove an extension to Pinsker's theorem for nonparametric estimation over Sobolev ellipsoids. Placing limits on the number of bits used to encode any estimator, we give tight lower and upper bounds on the excess risk due to qu…
The goal of decentralized optimization over a network is to optimize a global objective formed by a sum of local (possibly nonsmooth) convex functions using only local computation and communication. It arises in various application domains, including distributed tracking and localization, multi-agent co-ordination, est…
A communication-efficient method controls FDR in network settings.
Distributed Thompson sampling improves regret convergence in constrained communication networks.
Optimization results are one method for understanding neural computation from Nature's perspective and for defining the physical limits on neuron-like engineering. Earlier work looks at individual properties or performance criteria and occasionally a combination of two, such as energy and information. Here we make use …
Paper proposes a distributed sampling method for Bayesian inference.
Unified approach for federated learning using MM optimization.
A new discrete privacy mechanism for federated learning.
MARL algorithm uses regularization to avoid explicit structures, improving performance.
In many real-world settings, a team of agents must coordinate their behaviour while acting in a decentralised way. At the same time, it is often possible to train the agents in a centralised fashion in a simulated or laboratory setting, where global state information is available and communication constraints are lifte…
Asynchronous federated modeling improves spatial data sharing without centralizing raw data.
FLIX simplifies federated learning with efficient communication.
We consider distributed statistical optimization in one-shot setting, where there are machines each observing i.i.d. samples. Based on its observed samples, each machine sends a -bit-long message to a server. The server then collects messages from all machines, and estimates a parameter that minimizes an exp…
Local differential privacy (LDP) is a model where users send privatized data to an untrusted central server whose goal it to solve some data analysis task. In the non-interactive version of this model the protocol consists of a single round in which a server sends requests to all users then receives their responses. Th…
New algorithms for Bayesian inference in decentralized learning.
Compressed Federated Distillation reduces communication in federated learning.