arXiv research

Model predicts counterfactuals under domain shift and inaccessible variables.

Vecchia approximations provide the best accuracy-runtime trade-off for Gaussian process approximations.

Stochastic momentum methods trade compute efficiency for serial runtime.

Bayesian method predicts runtime metrics for fog manufacturing.

The runtime for Kernel Partial Least Squares (KPLS) to compute the fit is quadratic in the number of examples. However, the necessity of obtaining sensitivity measures as degrees of freedom for model selection or confidence intervals for more detailed analysis requires cubic runtime, and thus constitutes a computationa…

Asynchronous SGD can speed up training with a trade-off of gradient staleness.

Algorithm decides if two hyperbolic 3-manifolds are homeomorphic.

This work tackles runtime complexity prediction for code, using machine learning and a new dataset.

For well over a quarter century, detection systems have been driven by models learned from input features collected from real or simulated environments. An artifact (e.g., network event, potential malware sample, suspicious email) is deemed malicious or non-malicious based on its similarity to the learned model at runt…

In many recent applications, data is plentiful. By now, we have a rather clear understanding of how more data can be used to improve the accuracy of learning algorithms. Recently, there has been a growing interest in understanding how more data can be leveraged to reduce the required training runtime. In this paper, we…

Separating the short jobs from the long is a known technique to improve scheduling performance. In this paper we describe a method we developed for accurately predicting the runtimes classes of the jobs to enable this separation. Our method uses the fact that the runtimes can be represented as a mixture of overlapping …

GNMR controls runtime stability in low-precision language model training.

Perhaps surprisingly, it is possible to predict how long an algorithm will take to run on a previously unseen input, using machine learning techniques to build a model of the algorithm's runtime as a function of problem-specific instance features. Such models have important applications to algorithm analysis, portfolio…

Run2Survive uses survival analysis for algorithm selection, outperforming traditional methods.

The event-driven and elastic nature of serverless runtimes makes them a very efficient and cost-effective alternative for scaling up computations. So far, they have mostly been used for stateless, data parallel and ephemeral computations. In this work, we propose using serverless runtimes to solve generic, large-scale …

Training neural network often uses a machine learning framework such as TensorFlow and Caffe2. These frameworks employ a dataflow model where the NN training is modeled as a directed graph composed of a set of nodes. Operations in neural network training are typically implemented by the frameworks as primitives and rep…

Validates neural networks inputs to protect against adversarial examples.

Quantum machine learning can't achieve polylogarithmic runtimes, even with quantum data access.

Fully Homomorphic Encryption (FHE) refers to a set of encryption schemes that allow computations to be applied directly on encrypted data without requiring a secret key. This enables novel application scenarios where a client can safely offload storage and computation to a third-party cloud provider without having to t…

A new runtime for AI agents calculates risks in real-time.

New method for valid prediction intervals in counterfactual outcomes with runtime confounding.

Distributed Stochastic Gradient Descent (SGD) when run in a synchronous manner, suffers from delays in waiting for the slowest learners (stragglers). Asynchronous methods can alleviate stragglers, but cause gradient staleness that can adversely affect convergence. In this work we present a novel theoretical characteriz…

This paper presents Rudra, a parameter server based distributed computing framework tuned for training large-scale deep neural networks. Using variants of the asynchronous stochastic gradient descent algorithm we study the impact of synchronization protocol, stale gradient updates, minibatch size, learning rates, and n…

New algorithm reduces runtime for robust sparse mean estimation.

Research on nearest-neighbor methods tends to focus somewhat dichotomously either on the statistical or the computational aspects -- either on, say, Bayes consistency and rates of convergence or on techniques for speeding up the proximity search. This paper aims at bridging these realms: to reap the advantages of fast …

d3p package enables efficient Bayesian inference with differential privacy.

Rewiring networks using discrete geometry improves GNN training accuracy and reduces runtime.

Compress++ speeds up distribution compression to near-linear time.

Framework insures AI actions with reserve capital, preventing loss.

New DP algorithm improves privacy and efficiency for convex optimization.

Flexible deep learning models for dynamic accuracy and speed trade-offs.

Improved computational efficiency for estimating Wasserstein distance.

A new method estimates generative model mappings using kernel transfer operators, reducing costs and improving performance.

New method predicts outcomes even when some factors are not used in models.

Scorio.jl ranks systems from repeated tasks using various methods.

Max-convolution is an important problem closely resembling standard convolution; as such, max-convolution occurs frequently across many fields. Here we extend the method with fastest known worst-case runtime, which can be applied to nonnegative vectors by numerically approximating the Chebyshev norm $\| \cdot \|_\infty…

UMFI improves feature importance methods by reducing runtime and enhancing performance.

A new algorithm reduces online exp-concave optimization runtime.

A distributed algorithm for online multi-task learning reduces communication and runtime costs.

Improved algorithm for optimal stopping problems reduces runtime.

Improved biclustering algorithm reduces memory usage and runtime.

We developed a caching method to speed up concept learning in complex knowledge bases.

SDSR reconstructs species trees from genetic markers efficiently.

Improved EXACT strategy reduces GNN memory consumption and runtime.

"How much energy is consumed for an inference made by a convolutional neural network (CNN)?" With the increased popularity of CNNs deployed on the wide-spectrum of platforms (from mobile devices to workstations), the answer to this question has drawn significant attention. From lengthening battery life of mobile device…

QBSD optimizes KPI forecasting for RAN networks with fast runtime and accuracy.

Enhances reward specification in RL with a novel language-based approach.

We introduce "TriMap"; a dimensionality reduction technique based on triplet constraints, which preserves the global structure of the data better than the other commonly used methods such as t-SNE, LargeVis, and UMAP. To quantify the global accuracy of the embedding, we introduce a score that roughly reflects the relat…