arXiv research

Gradient descent with biased rounding errors converges faster under certain conditions.

A new algorithm reduces communication rounds for distributed convex optimization.

Gradient descent stagnates in low-precision, but unbiased rounding schemes improve convergence.

Training of large-scale deep neural networks is often constrained by the available computational resources. We study the effect of limited precision data representation and computation on neural network training. Within the context of low-precision fixed-point computations, we observe the rounding scheme to play a cruc…

New algorithms for streaming bandits with limited memory.

We study distributed optimization algorithms for minimizing the average of convex functions. The applications include empirical risk minimization problems in statistical machine learning where the datasets are large and have to be stored on different machines. We design a distributed stochastic variance reduced gradien…

Mechanism designs for unknown agent values in stochastic bandit settings.

The paper applies thermodynamics to financial markets to prove no-arbitrage constraints.

A new TS-SA method alleviates non-stationarity in TS algorithms for bandits.

A framework for collaborative learning reduces communication rounds.

Method predicts future rewards from past actions in a linear Gaussian system.

New predictive bandit model with noise for better decision making.

Investigates fast prediction rates with limited expert advice.

New algorithm reduces regret in both adversarial and stochastic contexts.

We study an online learning framework introduced by Mannor and Shamir (2011) in which the feedback is specified by a graph, in a setting where the graph may vary from round to round and is \emph{never fully revealed} to the learner. We show a large gap between the adversarial and the stochastic cases. In the adversaria…

Balances and eliminates base algorithms in bandits and RL to bound total regret.

The stochastic linear bandit problem proceeds in rounds where at each round the algorithm selects a vector from a decision set after which it receives a noisy linear loss parameterized by an unknown vector. The goal in such a problem is to minimize the (pseudo) regret which is the difference between the total expected …

FedAc accelerates Federated Averaging for distributed optimization.

New algorithms reduce matching regret by limiting frequent updates.

We propose an online algorithm for cumulative regret minimization in a stochastic multi-armed bandit. The algorithm adds $O(t)$ i.i.d. pseudo-rewards to its history in round $t$ and then pulls the arm with the highest average reward in its perturbed history. Therefore, we call it perturbed-history exploration (PHE). Th…

This paper optimizes attacks on stochastic bandits and proposes defenses against them.

New techniques improve 16-bit training accuracy without 32-bit units.

We introduce a new model of stochastic bandits with adversarial corruptions which aims to capture settings where most of the input follows a stochastic pattern but some fraction of it can be adversarially changed to trick the algorithm, e.g., click fraud, fake reviews and email spam. The goal of this model is to encour…

Data privacy and security becomes a major concern in building machine learning models from different data providers. Federated learning shows promise by leaving data at providers locally and exchanging encrypted information. This paper studies the vertical federated learning structure for logistic regression where the …

Study on individual regret in cooperative MAB with agents communicating over a graph.

We characterize learnability for stochastic noisy bandits, identifying optimal query complexities.

Algorithm minimizes regret in dueling bandits with contextualized utilities.

New algorithm reduces heavy-tailed linear bandits' computational cost.

Online optimization has been a successful framework for solving large-scale problems under computational constraints and partial information. Current methods for online convex optimization require either a projection or exact gradient computation at each step, both of which can be prohibitively expensive for large-scal…

We study a variant of the stochastic $K$-armed bandit problem, which we call "bandits with delayed, aggregated anonymous feedback". In this problem, when the player pulls an arm, a reward is generated, however it is not immediately observed. Instead, at the end of each round the player observes only the sum of a number…

Optimal trade execution in a fluctuating market with stochastic liquidity.

We investigate online classification with paid stochastic experts. Here, before making their prediction, each expert must be paid. The amount that we pay each expert directly influences the accuracy of their prediction through some unknown Lipschitz "productivity" function. In each round, the learner must decide how mu…

We address the M-best-arm identification problem in multi-armed bandits. A player has a limited budget to explore K arms (M<K), and once pulled, each arm yields a reward drawn (independently) from a fixed, unknown distribution. The goal is to find the top M arms in the sense of expected reward. We develop an algorithm …

In this paper, we consider a class of finite-sum convex optimization problems defined over a distributed multiagent network with $m$ agents connected to a central server. In particular, the objective function consists of the average of $m$ ($\ge 1$) smooth components associated with each network agent together with a s…

This paper considers online convex optimization (OCO) with stochastic constraints, which generalizes Zinkevich's OCO over a known simple fixed set by introducing multiple stochastic functional constraints that are i.i.d. generated at each round and are disclosed to the decision maker only after the decision is made. Th…

A decentralized algorithm minimizes cumulative regret in stochastic linear bandits with safety constraints.

Dynamic SBI improves SBI efficiency without rounds, reducing simulation and training costs.

Improved privacy bounds enhance deep learning training efficiency.

Efficient methods reduce projections in non-stationary online learning.

Mechanisms for fair resource allocation learn user preferences online.

In this work, we address the open problem of finding low-complexity near-optimal multi-armed bandit algorithms for sequential decision making problems. Existing bandit algorithms are either sub-optimal and computationally simple (e.g., UCB1) or optimal and computationally complex (e.g., kl-UCB). We propose a boosting a…

We consider the stochastic multi-armed bandit (MAB) problem in a setting where a player can pay to pre-observe arm rewards before playing an arm in each round. Apart from the usual trade-off between exploring new arms to find the best one and exploiting the arm believed to offer the highest reward, we encounter an addi…

We provide tight finite-time convergence bounds for gradient descent and stochastic gradient descent on quadratic functions, when the gradients are delayed and reflect iterates from $τ$ rounds ago. First, we show that without stochastic noise, delays strongly affect the attainable optimization error: In fact, the error…

New method reduces distributed non-convex optimization rounds and bits.

A new method for distributed optimization reduces communication rounds without minibatches.

LASG improves communication efficiency in distributed learning.

This paper solves the best arm identification problem with both quick commitment and reward maximization.

Efficiently maximizes AUC with deep nets, reducing communication rounds.