A modification of the gradient boosting algorithm, which is called parallel gradient boosting, designed to circumvent all limitations of classical implementations, is studied, which provides predictions of similar quality to state-of-the-art boosting libraries such as XGBoost, while being faster by several orders of ma...
Rémy Chapelle, Nicolas Vayatis, B. Falissard et al.· arXiv.org· 1 citation
We revisit a neural negative binomial model for weekly earthquake counts on a 3 x 3 degree grid over the Tian Shan and Pamir, and ask which of its conclusions survive a careful evaluation. At the completeness magnitude of 4.5, the 144-week test period holds 2448 cell-weeks; six cell-weeks, in five distinct weeks, have...
The learning dynamics of Stochastic Gradient Descent (SGD) are believed to play an important role in influencing the generalization properties of trained models. While the precise dynamics of how online (one-pass) SGD optimizes the population risk are now understood in a broad range of high-dimensional problems, a comp...
Fair regression methods have the potential to mitigate societal bias concerns in health care, but there has been little work on penalized fair regression when multiple groups experience such bias. We propose a general regression framework that addresses this gap with unfairness penalties for multiple groups. Our approa...
Carter H. Nakamoto, Lucia Lushi Chen, Agata Foryciarz et al.· 0 citations
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We introduce a bandit framework for stochastic matching under the multinomial logit (MNL) choice model. In this setting, $N$ agents on one side are assigned to $K$ arms on the other side, where each arm stochastically selects an agent from its assigned pool according to unknown preferences and yields a corresponding re...
Two meta-learning algorithms are introduced: Meta-Variational Quantum Thermalizer (Meta-VQT) and Neural Network Meta-VQT (NN-Meta VQT) for efficient thermal state preparation of parametrized Hamiltonians on Noisy Intermediate-Scale Quantum (NISQ) devices and a Quantum Boltzmann Machine (QBM) on a 2-qubit Heisenberg mod...
R. V. Bhat, Rahul Bhowmick, Avinash Singh et al.· arXiv.org· 3 citations
Score-based methods have recently seen increasing popularity in modeling and generation. Methods have been constructed to perform hypothesis testing and change-point detection with score functions, but these methods are in general not as powerful as their likelihood-based peers. Recent works consider generalizing the s...
Sean Moushegian, Taposh Banerjee, Vahid Tarokh· 0 citations
Contextual bandits with stage-wise constraints model decisions that must control risk while learning from their outcomes. We study this problem when only the selected action's reward and cost are observed, with a general reward class and a convex class of nonlinear cost functions. We propose CS-UCCB, which combines off...
Haichen Hu, David Simchi-Levi, Navid Azizan· 0 citations
This work evaluates ground-state estimation of 6- to 18-qubit Hamiltonians using the Variational Quantum Eigensolver (VQE) and quantum phase classification for Hamiltonians up to 12 qubits and uses these methods as a warm-start pretraining strategy for Hamiltonians with exponential-dimensional dynamical Lie algebras.
Rahul Bhowmick, Harsh Wadhwa, Avinash Singh et al.· 4 citations
We study clustering for multi-layer networks, where each observation is an undirected network measured on the same set of vertices. Under the mixture multi-layer stochastic block model (MMSBM), each network is generated from one of two stochastic block models, and the goal is to recover the latent network labels. We es...
K-Means is usually treated as a discrete procedure outside gradient-based learning, whereas normalized radial basis function (RBF) layers are smooth. We make precise the sense in which K-Means is the zero-temperature limit of such a layer. Relaxing the K-Means assignments to the simplex with a Shannon entropy penalty y...
Felipe de Jesus Felix Arredondo, Manuel Alejandro Ucan Puc, Carlos Astengo Nouguez· 0 citations
Recent studies have shown that reinforcement learning with KL-regularized objectives can enjoy faster rates of convergence or logarithmic regret, in contrast to the classical $\sqrt{T}$-type regret in the unregularized setting. However, the statistical efficiency of online learning with respect to KL-regularized object...
Kaixuan Ji, Qingyue Zhao, Heyang Zhao et al.· 0 citations
Is your company’s AI sovereign? A practical guide for CIOs on AI vendor lock-in, EU Data Act switching, cloud sovereignty, and testing your exit plan before you need it. The post Sovereignty Is the Option to Leave appeared first on GPT-Lab.
