
Sharp Statistical Guarantees for Adversarially Robust Gaussian Classification
Adversarial robustness has become a fundamental requirement in modern ma...
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Learning Minimax Estimators via Online Learning
We consider the problem of designing minimax estimators for estimating t...
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SubSeasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances
Subseasonal climate forecasting (SSF) focuses on predicting key climate...
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Evaluations and Methods for Explanation through Robustness Analysis
Among multiple ways of interpreting a machine learning model, measuring ...
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ClassWeighted Classification: Tradeoffs and Robust Approaches
We address imbalanced classification, the problem in which a label may h...
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Minimizing FLOPs to Learn Efficient Sparse Representations
Deep representation learning has become one of the most widely adopted a...
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Certified Robustness to LabelFlipping Attacks via Randomized Smoothing
Machine learning algorithms are known to be susceptible to data poisonin...
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MACER: Attackfree and Scalable Robust Training via Maximizing Certified Radius
Adversarial training is one of the most popular ways to learn robust mod...
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Game Design for Eliciting Distinguishable Behavior
The ability to inferring latent psychological traits from human behavior...
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Diagnostic Curves for Black Box Models
In safetycritical applications of machine learning, it is often necessa...
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Optimal Analysis of SubsetSelection Based L_p Low Rank Approximation
We study the low rank approximation problem of any given matrix A over R...
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On ConceptBased Explanations in Deep Neural Networks
Deep neural networks (DNNs) build highlevel intelligence on lowlevel r...
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Learning Sparse Nonparametric DAGs
We develop a framework for learning sparse nonparametric directed acycli...
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A Unified Approach to Robust Mean Estimation
In this paper, we develop connections between two seemingly disparate, b...
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Adaptive Hard Thresholding for Nearoptimal Consistent Robust Regression
We study the problem of robust linear regression with response variable ...
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How Sensitive are SensitivityBased Explanations?
We propose a simple objective evaluation measure for explanations of a c...
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Towards Aggregating Weighted Feature Attributions
Current approaches for explaining machine learning models fall into two ...
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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...
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Word Mover's Embedding: From Word2Vec to Document Embedding
While the celebrated Word2Vec technique yields semantically rich represe...
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Learning Tensor Latent Features
We study the problem of learning latent feature models (LFMs) for tensor...
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Sample Complexity of Nonparametric SemiSupervised Learning
We study the sample complexity of semisupervised learning (SSL) and int...
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On Adversarial Risk and Training
In this work we formally define the notions of adversarial perturbations...
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Binary Classification with Karmic, ThresholdQuasiConcave Metrics
Complex performance measures, beyond the popular measure of accuracy, ar...
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Robust Nonparametric Regression under Huber's εcontamination Model
We consider the nonparametric regression problem under Huber's ϵcontam...
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DAGs with NO TEARS: Smooth Optimization for Structure Learning
Estimating the structure of directed acyclic graphs (DAGs, also known as...
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Robust Estimation via Robust Gradient Estimation
We provide a new computationallyefficient class of estimators for risk ...
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Identifiability of Nonparametric Mixture Models and Bayes Optimal Clustering
Motivated by problems in data clustering, we establish general condition...
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A VotingBased System for Ethical Decision Making
We present a general approach to automating ethical decisions, drawing o...
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Online Classification with Complex Metrics
We present a framework and analysis of consistent binary classification ...
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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...
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Kernel Ridge Regression via Partitioning
In this paper, we investigate a divide and conquer approach to Kernel Ri...
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Generalized Root Models: Beyond Pairwise Graphical Models for Univariate Exponential Families
We present a novel kway highdimensional graphical model called the Gen...
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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...
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Exponential Family Matrix Completion under Structural Constraints
We consider the matrix completion problem of recovering a structured mat...
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VectorSpace Markov Random Fields via Exponential Families
We present VectorSpace Markov Random Fields (VSMRFs), a novel class of...
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Optimal DecisionTheoretic Classification Using NonDecomposable Performance Metrics
We provide a general theoretical analysis of expected outofsample util...
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A General Framework for Mixed Graphical Models
"Mixed Data" comprising a large number of heterogeneous variables (e.g. ...
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Proximal QuasiNewton for Computationally Intensive L1regularized Mestimators
We consider the class of optimization problems arising from computationa...
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Sparse Inverse Covariance Matrix Estimation Using Quadratic Approximation
The L1regularized Gaussian maximum likelihood estimator (MLE) has been ...
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On Graphical Models via Univariate Exponential Family Distributions
Undirected graphical models, or Markov networks, are a popular class of ...
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A Hierarchical Graphical Model for Record Linkage
The task of matching coreferent records is known among other names as r...
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Variational Chernoff Bounds for Graphical Models
Recent research has made significant progress on the problem of bounding...
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Highdimensional Sparse Inverse Covariance Estimation using Greedy Methods
In this paper we consider the task of estimating the nonzero pattern of...
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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...
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A Dirty Model for Multiple Sparse Regression
Sparse linear regression  finding an unknown vector from linear measur...
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Informationtheoretic lower bounds on the oracle complexity of stochastic convex optimization
Relative to the large literature on upper bounds on complexity of convex...
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Pradeep Ravikumar
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Associate Professor, School of Computer Science, Carnegie Mellon University