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Found 10 papers, 2 papers with code
Online Learning in Dynamically Changing Environments
We study the problem of online learning and online regret minimization when samples are drawn from a general unknown non-stationary process.
Expected Worst Case Regret via Stochastic Sequential Covering
We show that for a hypothesis class of VC-dimension $\mathsf{VC}$ and $i. i. d.$ generated features of length $T$, the cardinality of the stochastic global sequential covering can be upper bounded with high probability (whp) by $e^{O(\mathsf{VC} \cdot \log^2 T)}$.
Precise Regret Bounds for Log-loss via a Truncated Bayesian Algorithm
We study the sequential general online regression, known also as the sequential probability assignments, under logarithmic loss when compared against a broad class of experts.
CKH: Causal Knowledge Hierarchy for Estimating Structural Causal Models from Data and Priors
Using CKH, we present a methodological framework for encoding causal priors from various information sources and combining them to derive an SCM.
Toward Physically Realizable Quantum Neural Networks
Current solutions for QNNs pose significant challenges concerning their scalability, ensuring that the postulates of quantum mechanics are satisfied and that the networks are physically realizable.
Identifying and Analyzing Sepsis States: A Retrospective Study on Patients with Sepsis in ICUs
Sepsis accounts for more than 50% of hospital deaths, and the associated cost ranks the highest among hospital admissions in the US.
Low rank methods for multiple network alignment
While there are a large number of effective techniques for pairwise problems with two networks that scale in terms of edges, these cannot be readily extended to align multiple networks as the computational complexity will tend to grow exponentially with the number of networks. In this paper we introduce a new multiple network alignment algorithm and framework that is effective at aligning thousands of networks with thousands of nodes.
Newton-ADMM: A Distributed GPU-Accelerated Optimizer for Multiclass Classification Problems
First-order optimization methods, such as stochastic gradient descent (SGD) and its variants, are widely used in machine learning applications due to their simplicity and low per-iteration costs.
Constructing Compact Brain Connectomes for Individual Fingerprinting
Recent neuroimaging studies have shown that functional connectomes are unique to individuals, i. e., two distinct fMRIs taken over different sessions of the same subject are more similar in terms of their connectomes than those from two different subjects.
GPU Accelerated Sub-Sampled Newton's Method
In particular, in convex settings, we consider variants of classical Newton\textsf{'}s method in which the Hessian and/or the gradient are randomly sub-sampled.
