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06/11/2023
Fast, Distribution-free Predictive Inference for Neural Networks with Coverage Guarantees
This paper introduces a novel, computationally-efficient algorithm for p...
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04/18/2023
High-dimensional Multi-class Classification with Presence-only Data
Classification with positive and unlabeled (PU) data frequently arises i...
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07/19/2022
Lazy Estimation of Variable Importance for Large Neural Networks
As opaque predictive models increasingly impact many areas of modern lif...
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11/19/2021
Gaussian Process Inference Using Mini-batch Stochastic Gradient Descent: Convergence Guarantees and Empirical Benefits
Stochastic gradient descent (SGD) and its variants have established them...
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11/09/2021
The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learning
The Internet of Things (IoT) is on the verge of a major paradigm shift. ...
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06/28/2021
Improved Prediction and Network Estimation Using the Monotone Single Index Multi-variate Autoregressive Model
Network estimation from multi-variate point process or time series data ...
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05/17/2021
Convergence guarantee for the sparse monotone single index model
We consider a high-dimensional monotone single index model (hdSIM), whic...
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03/25/2021
Prediction in the presence of response-dependent missing labels
In a variety of settings, limitations of sensing technologies or other s...
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08/06/2020
A Sharp Blockwise Tensor Perturbation Bound for Orthogonal Iteration
In this paper, we develop novel perturbation bounds for the high-order o...
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03/16/2020
Context-dependent self-exciting point processes: models, methods, and risk bounds in high dimensions
High-dimensional autoregressive point processes model how current events...
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11/09/2019
ISLET: Fast and Optimal Low-rank Tensor Regression via Importance Sketching
In this paper, we develop a novel procedure for low-rank tensor regressi...
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10/06/2019
Convex and Non-convex Approaches for Statistical Inference with Noisy Labels
We study the problem of estimation and testing in logistic regression wi...
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08/13/2019
The bias of isotonic regression
We study the bias of the isotonic regression estimator. While there is e...
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01/31/2019
Minimizing Negative Transfer of Knowledge in Multivariate Gaussian Processes: A Scalable and Regularized Approach
Recently there has been an increasing interest in the multivariate Gauss...
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12/10/2018
Testing for high-dimensional network parameters in auto-regressive models
High-dimensional auto-regressive models provide a natural way to model i...
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12/10/2018
Testing for high-dimensional network paramaters in auto-regressive models
High-dimensional auto-regressive models provide a natural way to model i...
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11/07/2018
Estimating Network Structure from Incomplete Event Data
Multivariate Bernoulli autoregressive (BAR) processes model time series ...
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03/20/2018
Graph-based regularization for regression problems with highly-correlated designs
Sparse models for high-dimensional linear regression and machine learnin...
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02/13/2018
Network Estimation from Point Process Data
Consider observing a collection of discrete events within a network that...
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01/23/2018
Non-parametric sparse additive auto-regressive network models
Consider a multi-variate time series (X_t)_t=0^T where X_t ∈R^d which ma...
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11/22/2017
PULasso: High-dimensional variable selection with presence-only data
In various real-world problems, we are presented with positive and unlab...
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04/28/2017
Learning Quadratic Variance Function (QVF) DAG models via OverDispersion Scoring (ODS)
Learning DAG or Bayesian network models is an important problem in multi...
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05/09/2016
Inference of High-dimensional Autoregressive Generalized Linear Models
Vector autoregressive models characterize a variety of time series in wh...
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02/14/2016
Identifiability Assumptions and Algorithm for Directed Graphical Models with Feedback
Directed graphical models provide a useful framework for modeling causal...
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05/25/2015
Statistical and Algorithmic Perspectives on Randomized Sketching for Ordinary Least-Squares -- ICML
We consider statistical and algorithmic aspects of solving large-scale l...
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06/23/2014
A Statistical Perspective on Randomized Sketching for Ordinary Least-Squares
We consider statistical as well as algorithmic aspects of solving large-...
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06/15/2013
