Pradeep Ravikumar

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The University of Texas at Austin ∙ Carnegie Mellon University ∙ National Taiwan University

Associate Professor, School of Computer Science, Carnegie Mellon University

Sharp Statistical Guarantees for Adversarially Robust Gaussian Classification

Adversarial robustness has become a fundamental requirement in modern ma...
06/29/2020 ∙ by Chen Dan, et al. ∙ 9 ∙ share

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Learning Minimax Estimators via Online Learning

We consider the problem of designing minimax estimators for estimating t...
06/19/2020 ∙ by Kartik Gupta, et al. ∙ 8 ∙ share

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Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances

Sub-seasonal climate forecasting (SSF) focuses on predicting key climate...
06/14/2020 ∙ by Sijie He, et al. ∙ 0 ∙ share

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Evaluations and Methods for Explanation through Robustness Analysis

Among multiple ways of interpreting a machine learning model, measuring ...
05/31/2020 ∙ by Cheng-Yu Hsieh, et al. ∙ 15 ∙ share

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Class-Weighted Classification: Trade-offs and Robust Approaches

We address imbalanced classification, the problem in which a label may h...
05/26/2020 ∙ by Ziyu Xu, et al. ∙ 2 ∙ share

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Minimizing FLOPs to Learn Efficient Sparse Representations

Deep representation learning has become one of the most widely adopted a...
04/12/2020 ∙ by Biswajit Paria, et al. ∙ 20 ∙ share

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Certified Robustness to Label-Flipping Attacks via Randomized Smoothing

Machine learning algorithms are known to be susceptible to data poisonin...
02/07/2020 ∙ by Elan Rosenfeld, et al. ∙ 0 ∙ share

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MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius

Adversarial training is one of the most popular ways to learn robust mod...
01/08/2020 ∙ by Runtian Zhai, et al. ∙ 4 ∙ share

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Game Design for Eliciting Distinguishable Behavior

The ability to inferring latent psychological traits from human behavior...
12/12/2019 ∙ by Fan Yang, et al. ∙ 6 ∙ share

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Diagnostic Curves for Black Box Models

In safety-critical applications of machine learning, it is often necessa...
12/02/2019 ∙ by David I. Inouye, et al. ∙ 0 ∙ share

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Optimal Analysis of Subset-Selection Based L_p Low Rank Approximation

We study the low rank approximation problem of any given matrix A over R...
10/30/2019 ∙ by Chen Dan, et al. ∙ 0 ∙ share

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On Concept-Based Explanations in Deep Neural Networks

Deep neural networks (DNNs) build high-level intelligence on low-level r...
10/17/2019 ∙ by Chih-Kuan Yeh, et al. ∙ 21 ∙ share

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Learning Sparse Nonparametric DAGs

We develop a framework for learning sparse nonparametric directed acycli...
09/29/2019 ∙ by Xun Zheng, et al. ∙ 18 ∙ share

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A Unified Approach to Robust Mean Estimation

In this paper, we develop connections between two seemingly disparate, b...
07/01/2019 ∙ by Adarsh Prasad, et al. ∙ 0 ∙ share

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Adaptive Hard Thresholding for Near-optimal Consistent Robust Regression

We study the problem of robust linear regression with response variable ...
03/19/2019 ∙ by Arun Sai Suggala, et al. ∙ 0 ∙ share

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How Sensitive are Sensitivity-Based Explanations?

We propose a simple objective evaluation measure for explanations of a c...
01/27/2019 ∙ by Chih-Kuan Yeh, et al. ∙ 18 ∙ share

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Towards Aggregating Weighted Feature Attributions

Current approaches for explaining machine learning models fall into two ...
01/20/2019 ∙ by Umang Bhatt, et al. ∙ 0 ∙ share

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Representer Point Selection for Explaining Deep Neural Networks

We propose to explain the predictions of a deep neural network, by point...
11/23/2018 ∙ by Chih-Kuan Yeh, et al. ∙ 0 ∙ share

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Word Mover's Embedding: From Word2Vec to Document Embedding

While the celebrated Word2Vec technique yields semantically rich represe...
10/30/2018 ∙ by Lingfei Wu, et al. ∙ 0 ∙ share

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Learning Tensor Latent Features

We study the problem of learning latent feature models (LFMs) for tensor...
10/10/2018 ∙ by Sung-En Chang, et al. ∙ 0 ∙ share

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Sample Complexity of Nonparametric Semi-Supervised Learning

We study the sample complexity of semi-supervised learning (SSL) and int...
09/10/2018 ∙ by Chen Dan, et al. ∙ 0 ∙ share

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On Adversarial Risk and Training

In this work we formally define the notions of adversarial perturbations...
06/07/2018 ∙ by Arun Sai Suggala, et al. ∙ 0 ∙ share

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Binary Classification with Karmic, Threshold-Quasi-Concave Metrics

Complex performance measures, beyond the popular measure of accuracy, ar...
06/02/2018 ∙ by Bowei Yan, et al. ∙ 0 ∙ share

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Robust Nonparametric Regression under Huber's ε-contamination Model

We consider the non-parametric regression problem under Huber's ϵ-contam...
05/26/2018 ∙ by Simon S. Du, et al. ∙ 0 ∙ share

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DAGs with NO TEARS: Smooth Optimization for Structure Learning

Estimating the structure of directed acyclic graphs (DAGs, also known as...
03/04/2018 ∙ by Xun Zheng, et al. ∙ 0 ∙ share

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Robust Estimation via Robust Gradient Estimation

We provide a new computationally-efficient class of estimators for risk ...
02/19/2018 ∙ by Adarsh Prasad, et al. ∙ 0 ∙ share

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Identifiability of Nonparametric Mixture Models and Bayes Optimal Clustering

Motivated by problems in data clustering, we establish general condition...
02/12/2018 ∙ by Bryon Aragam, et al. ∙ 0 ∙ share

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A Voting-Based System for Ethical Decision Making

We present a general approach to automating ethical decisions, drawing o...
09/20/2017 ∙ by Ritesh Noothigattu, et al. ∙ 0 ∙ share

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Online Classification with Complex Metrics

We present a framework and analysis of consistent binary classification ...
10/23/2016 ∙ by Bowei Yan, et al. ∙ 0 ∙ share

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A Review of Multivariate Distributions for Count Data Derived from the Poisson Distribution

The Poisson distribution has been widely studied and used for modeling u...
08/31/2016 ∙ by David I. Inouye, et al. ∙ 0 ∙ share

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Kernel Ridge Regression via Partitioning

In this paper, we investigate a divide and conquer approach to Kernel Ri...
08/05/2016 ∙ by Rashish Tandon, et al. ∙ 0 ∙ share

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Generalized Root Models: Beyond Pairwise Graphical Models for Univariate Exponential Families

We present a novel k-way high-dimensional graphical model called the Gen...
06/02/2016 ∙ by David I. Inouye, et al. ∙ 0 ∙ share

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Square Root Graphical Models: Multivariate Generalizations of Univariate Exponential Families that Permit Positive Dependencies

We develop Square Root Graphical Models (SQR), a novel class of parametr...
03/11/2016 ∙ by David I. Inouye, et al. ∙ 0 ∙ share

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Exponential Family Matrix Completion under Structural Constraints

We consider the matrix completion problem of recovering a structured mat...
09/15/2015 ∙ by Suriya Gunasekar, et al. ∙ 0 ∙ share

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Vector-Space Markov Random Fields via Exponential Families

We present Vector-Space Markov Random Fields (VS-MRFs), a novel class of...
05/19/2015 ∙ by Wesley Tansey, et al. ∙ 0 ∙ share

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Optimal Decision-Theoretic Classification Using Non-Decomposable Performance Metrics

We provide a general theoretical analysis of expected out-of-sample util...
05/07/2015 ∙ by Nagarajan Natarajan, et al. ∙ 0 ∙ share

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A General Framework for Mixed Graphical Models

"Mixed Data" comprising a large number of heterogeneous variables (e.g. ...
11/02/2014 ∙ by Eunho Yang, et al. ∙ 0 ∙ share

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Proximal Quasi-Newton for Computationally Intensive L1-regularized M-estimators

We consider the class of optimization problems arising from computationa...
06/27/2014 ∙ by Kai Zhong, et al. ∙ 0 ∙ share

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Sparse Inverse Covariance Matrix Estimation Using Quadratic Approximation

The L1-regularized Gaussian maximum likelihood estimator (MLE) has been ...
06/13/2013 ∙ by Cho-Jui Hsieh, et al. ∙ 0 ∙ share

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On Graphical Models via Univariate Exponential Family Distributions

Undirected graphical models, or Markov networks, are a popular class of ...
01/17/2013 ∙ by Eunho Yang, et al. ∙ 0 ∙ share

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A Hierarchical Graphical Model for Record Linkage

The task of matching co-referent records is known among other names as r...
07/12/2012 ∙ by Pradeep Ravikumar, et al. ∙ 0 ∙ share

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Variational Chernoff Bounds for Graphical Models

Recent research has made significant progress on the problem of bounding...
07/11/2012 ∙ by Pradeep Ravikumar, et al. ∙ 0 ∙ share

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High-dimensional Sparse Inverse Covariance Estimation using Greedy Methods

In this paper we consider the task of estimating the non-zero pattern of...
12/29/2011 ∙ by Christopher C. Johnson, et al. ∙ 0 ∙ share

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On Learning Discrete Graphical Models Using Greedy Methods

In this paper, we address the problem of learning the structure of a pai...
07/16/2011 ∙ by Ali Jalali, et al. ∙ 0 ∙ share

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A Dirty Model for Multiple Sparse Regression

Sparse linear regression -- finding an unknown vector from linear measur...
06/29/2011 ∙ by Ali Jalali, et al. ∙ 0 ∙ share

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Information-theoretic lower bounds on the oracle complexity of stochastic convex optimization

Relative to the large literature on upper bounds on complexity of convex...
09/03/2010 ∙ by Alekh Agarwal, et al. ∙ 0 ∙ share

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Pradeep Ravikumar

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