arXiv research

ATA optimizes task allocation in distributed machine learning.

Paper proposes OPF policy for fair resource allocation with sublinear regret.

Greedy algorithm performs well in online matching despite non-i.i.d. connections.

New loss function reduces outage probability in ML-assisted resource allocation.

Efficiently allocate budgets for LLM-assisted virtual screening to reduce costs.

AEGiS optimizes expensive function evaluations asynchronously.

New algorithms improve best-arm identification with varying rewards.

OOMP selects features online for sparse linear regression.

Register allocation, which is a crucial phase of a good optimizing compiler, relies on graph coloring. Hence, an efficient graph coloring algorithm is of paramount importance. In this work we try to learn a good heuristic for coloring interference graphs that are used in the register allocation phase. We aim to handle …

In urban environments, supply resources have to be constantly matched to the "right" locations (where customer demand is present) so as to improve quality of life. For instance, ambulances have to be matched to base stations regularly so as to reduce response time for emergency incidents in EMS (Emergency Management Sy…

A new algorithm reduces memory usage for deep learning models.

Proposes a greedy algorithm for telecom offers to retain subscribers.

DE is a new exploration method that limits resource usage based on expected improvement and surprise.

The paper studies how to allocate human validation in AI-assisted tasks to minimize errors.

We propose a new probabilistic graphical model that jointly models the difficulties of questions, the abilities of participants and the correct answers to questions in aptitude testing and crowdsourcing settings. We devise an active learning/adaptive testing scheme based on a greedy minimization of expected model entro…

HATCH learns optimal recommendations with resource constraints.

Bridges uplift modeling and sequential decision-making with online budget allocation.

This paper studies the problem of optimally allocating a cash injection into a financial system in distress. Given a one-period borrower-lender network in which all debts are due at the same time and have the same seniority, we address the problem of allocating a fixed amount of cash among the nodes to minimize the wei…

We introduce the community exploration problem that has many real-world applications such as online advertising. In the problem, an explorer allocates limited budget to explore communities so as to maximize the number of members he could meet. We provide a systematic study of the community exploration problem, from off…

The paper extends a learning heuristic to high-dimensional contexts, reducing the risk of unusual actions.

Optimal online data collection for semiparametric inference reduces regret.

Greedy algorithm achieves sublinear regret for various distributions.

Greedy algorithm optimizes consumption habits with power utility.

We present an information-theoretic framework for sequential adaptive compressed sensing, Info-Greedy Sensing, where measurements are chosen to maximize the extracted information conditioned on the previous measurements. We show that the widely used bisection approach is Info-Greedy for a family of $k$-sparse signals b…

New framework tackles submodular welfare with multi-agent combinatorial bandits.

The expected improvement (EI) algorithm is a popular strategy for information collection in optimization under uncertainty. The algorithm is widely known to be too greedy, but nevertheless enjoys wide use due to its simplicity and ability to handle uncertainty and noise in a coherent decision theoretic framework. To pr…

New distributions allow greedy arm selection in sparse bandit problems.

Simpler ε-greedy with longer action durations improves exploration.

This paper is a follow up to the previous author's paper on convex optimization. In that paper we began the process of adjusting greedy-type algorithms from nonlinear approximation for finding sparse solutions of convex optimization problems. We modified there three the most popular in nonlinear approximation in Banach…

Introduces greedy feature selection for classifier-dependent feature ranking.

New method shows multi-objective bandits are not harder than single-objective ones.

Greedy policy maximizes information in unknown linear systems.

Greedy selection works well in a toy model of independent increments.

This paper proposes and evaluates the k-greedy equivalence search algorithm (KES) for learning Bayesian networks (BNs) from complete data. The main characteristic of KES is that it allows a trade-off between greediness and randomness, thus exploring different good local optima. When greediness is set at maximum, KES co…

Study optimal policies under budget and coverage constraints.

Multiple-step lookahead policies have demonstrated high empirical competence in Reinforcement Learning, via the use of Monte Carlo Tree Search or Model Predictive Control. In a recent work \cite{efroni2018beyond}, multiple-step greedy policies and their use in vanilla Policy Iteration algorithms were proposed and analy…

Paper analyzes Greedy-GQ for reinforcement learning with Markovian noise.

New algorithm speeds up learning of graphical models.

Improved greedy 2-coordinate updates for optimization problems with constraints.

We propose a new concept named adaptive submodularity ratio to study the greedy policy for sequential decision making. While the greedy policy is known to perform well for a wide variety of adaptive stochastic optimization problems in practice, its theoretical properties have been analyzed only for a limited class of p…

The contextual bandit literature has traditionally focused on algorithms that address the exploration-exploitation tradeoff. In particular, greedy algorithms that exploit current estimates without any exploration may be sub-optimal in general. However, exploration-free greedy algorithms are desirable in practical setti…

Greedy algorithm nearly outperforms exploration in contextual bandits.

A major problem in data augmentation is to ensure that the generated new samples cover the search space. This is a challenging problem and requires exploration for data augmentation policies to ensure their effectiveness in covering the search space. In this paper, we propose Greedy AutoAugment as a highly efficient se…

Greedy training of recursive partitioning estimators faces a computational barrier when the true function doesn't satisfy a specific property.

In this paper we consider the task of estimating the non-zero pattern of the sparse inverse covariance matrix of a zero-mean Gaussian random vector from a set of iid samples. Note that this is also equivalent to recovering the underlying graph structure of a sparse Gaussian Markov Random Field (GMRF). We present two no…

Breathing k-means outperforms greedy k-means++ in clustering.

The paper gives a systematic study of the approximate versions of three greedy-type algorithms that are widely used in convex optimization. By approximate version we mean the one where some of evaluations are made with an error. Importance of such versions of greedy-type algorithms in convex optimization and in approxi…

We consider interactive learning and covering problems, in a setting where actions may incur different costs, depending on the response to the action. We propose a natural greedy algorithm for response-dependent costs. We bound the approximation factor of this greedy algorithm in active learning settings as well as in …