arXiv research

Improved computational efficiency for estimating Wasserstein distance.

Lasso performs poorly with correlated covariates, but a rescaled approach fixes this.

Improved MMD test for two-sample testing with random Fourier features.

We extend the Fourier cosine method to discrete probability distributions, achieving faster convergence rates.

Model predicts counterfactuals under domain shift and inaccessible variables.

ALMAB-DC optimizes expensive black-box experiments using active learning and distributed computing.

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…

We present two paradigms relating algebraic, topological and quantum computational statistics for the topological model for quantum computation. In particular we suggest correspondences between the computational power of topological quantum computers, computational complexity of link invariants and images of braid grou…

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…

This monograph presents a class of algorithms called coordinate descent algorithms for mathematicians, statisticians, and engineers outside the field of optimization. This particular class of algorithms has recently gained popularity due to their effectiveness in solving large-scale optimization problems in machine lea…

Efficiently bootstraps massive distributed data without over-resampling.

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.

Study phase transitions in identifying infected individuals using group testing.

Study shows inefficiency of sparse linear regression learning with fewer than Ω(k^2) samples.

We simplify information measure computation using learned features.

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 lower bound shows RL with linear approximations is computationally hard.

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 …

Neural networks can achieve optimal sample complexity for learning single-index models.

An important task in computational statistics and machine learning is to approximate a posterior distribution $p(x)$ with an empirical measure supported on a set of representative points $\{x_i\}_{i=1}^n$. This paper focuses on methods where the selection of points is essentially deterministic, with an emphasis on achi…

Nonparametric two sample testing is a decision theoretic problem that involves identifying differences between two random variables without making parametric assumptions about their underlying distributions. We refer to the most common settings as mean difference alternatives (MDA), for testing differences only in firs…

We present a scalable approach to performing approximate fully Bayesian inference in generic state space models. The proposed method is an alternative to particle MCMC that provides fully Bayesian inference of both the dynamic latent states and the static parameters of the model. We build up on recent advances in compu…

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.

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