
Interpretable machine learning: definitions, methods, and applications
Machinelearning models have demonstrated great success in learning comp...
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Interpretations are useful: penalizing explanations to align neural networks with prior knowledge
For an explanation of a deep learning model to be effective, it must pro...
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Three principles of data science: predictability, computability, and stability (PCS)
We propose the predictability, computability, and stability (PCS) framew...
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Hierarchical interpretations for neural network predictions
Deep neural networks (DNNs) have achieved impressive predictive performa...
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CharBot: A Simple and Effective Method for Evading DGA Classifiers
Domain generation algorithms (DGAs) are commonly leveraged by malware to...
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Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees
Tree ensembles, such as random forests and AdaBoost, are ubiquitous mach...
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A Debiased MDI Feature Importance Measure for Random Forests
Tree ensembles such as Random Forests have achieved impressive empirical...
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Interpreting Convolutional Neural Networks Through Compression
Convolutional neural networks (CNNs) achieve stateoftheart performanc...
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Fast MCMC sampling algorithms on polytopes
We propose and analyze two new MCMC sampling algorithms, the Vaidya walk...
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Iterative Random Forests to detect predictive and stable highorder interactions
Genomics has revolutionized biology, enabling the interrogation of whole...
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Structural Compression of Convolutional Neural Networks Based on Greedy Filter Pruning
Convolutional neural networks (CNNs) have stateoftheart performance o...
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Formulas for Counting the Sizes of Markov Equivalence Classes of Directed Acyclic Graphs
The sizes of Markov equivalence classes of directed acyclic graphs play ...
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Optimal Subsampling Approaches for Large Sample Linear Regression
A significant hurdle for analyzing large sample data is the lack of effe...
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Local identifiability of l_1minimization dictionary learning: a sufficient and almost necessary condition
We study the theoretical properties of learning a dictionary from N sign...
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Error Rate Bounds and Iterative Weighted Majority Voting for Crowdsourcing
Crowdsourcing has become an effective and popular tool for humanpowered...
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Statistical guarantees for the EM algorithm: From population to samplebased analysis
We develop a general framework for proving rigorous guarantees on the pe...
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The geometry of kernelized spectral clustering
Clustering of data sets is a standard problem in many areas of science a...
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Impact of regularization on Spectral Clustering
The performance of spectral clustering can be considerably improved via ...
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Error Rate Bounds in Crowdsourcing Models
Crowdsourcing is an effective tool for humanpowered computation on many...
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A Statistical Perspective on Algorithmic Leveraging
One popular method for dealing with largescale data sets is sampling. F...
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Early stopping and nonparametric regression: An optimal datadependent stopping rule
The strategy of early stopping is a regularization technique based on ch...
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Estimation Stability with Cross Validation (ESCV)
Crossvalidation (CV) is often used to select the regularization paramet...
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Supplement to "Reversible MCMC on Markov equivalence classes of sparse directed acyclic graphs"
This supplementary material includes three parts: some preliminary resul...
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Reversible MCMC on Markov equivalence classes of sparse directed acyclic graphs
Graphical models are popular statistical tools which are used to represe...
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Complexity Analysis of the Lasso Regularization Path
The regularization path of the Lasso can be shown to be piecewise linear...
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Supervised Feature Selection in Graphs with Path Coding Penalties and Network Flows
We consider supervised learning problems where the features are embedded...
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Coclustering for directed graphs: the Stochastic coBlockmodel and spectral algorithm DiSim
Directed graphs have asymmetric connections, yet the current graph clust...
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The Lasso under Heteroscedasticity
The performance of the Lasso is well understood under the assumptions of...
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Data spectroscopy: Eigenspaces of convolution operators and clustering
This paper focuses on obtaining clustering information about a distribut...
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Artificial Intelligence and Statistics
Artificial intelligence (AI) is intrinsically datadriven. It calls for ...
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Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs
The driving force behind the recent success of LSTMs has been their abil...
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Towards the Standardization of Nonorthogonal Multiple Access for Next Generation Wireless Networks
Nonorthogonal multiple access (NoMA) as an efficient way of radio resou...
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Improved OpenCLbased Implementation of Social Field Pedestrian Model
Two aspects of improvements are proposed for the OpenCLbased implementa...
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Stability and Convergence Tradeoff of Iterative Optimization Algorithms
The overall performance or expected excess risk of an iterative machine ...
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Highspeed Tracking with Multikernel Correlation Filters
Correlation filter (CF) based trackers are currently ranked top in terms...
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High Speed Kernelized Correlation Filters without Boundary Effect
Recently, correlation filter based trackers (CF trackers) have attracted...
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Logconcave sampling: MetropolisHastings algorithms are fast!
We consider the problem of sampling from a strongly logconcave density ...
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Refining interaction search through signed iterative Random Forests
Advances in supervised learning have enabled accurate prediction in biol...
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Singularity, Misspecification, and the Convergence Rate of EM
A line of recent work has characterized the behavior of the EM algorithm...
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Mobile Robot Localisation and Navigation Using LEGO NXT and Ultrasonic Sensor
Mobile robots are becoming increasingly important both for individuals a...
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Unique Sharp Local Minimum in ℓ_1minimization Complete Dictionary Learning
We study the problem of globally recovering a dictionary from a set of s...
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Challenges with EM in application to weakly identifiable mixture models
We study a class of weakly identifiable locationscale mixture models fo...
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Incremental causal effects
This is a draft. The ignorability assumption is a key assumption in caus...
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Fast mixing of Metropolized Hamiltonian Monte Carlo: Benefits of multistep gradients
Hamiltonian Monte Carlo (HMC) is a stateoftheart Markov chain Monte C...
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Bin Yu
verfied profile
Bin Yu is an American statistician and data scientist. She is Chancellor's Professor of Statistics and EECS at UC Berkeley and also an Investigator at ChanZuckerberg Biohub in San Francisco. Her research focuses on practice, algorithm, and theory of statistical machine learning and causal inference. Her group is engaged in interdisciplinary research with scientists from genomics, neuroscience, and precision medicine.
In order to augment empirical evidence for decisionmaking, she and her group are investigating methods/algorithms (and associated statistical inference problems) such as dictionary learning, nonnegative matrix factorization (NMF), EM and deep learning (CNNs and LSTMs), and heterogeneous effect estimation in randomized experiments (Xlearner). Their recent algorithms include staNMF for unsupervised learning, iterative Random Forests (iRF) and signed iRF (siRF) for discovering predictive and stable highorder interactions in supervised learning, and contextual decomposition (CD) and aggregated contextual decomposition (ACD) for phrase or patch importance extraction from an LSTM or a CNN.
She is a member of the U.S. National Academy of Sciences and Fellow of the American Academy of Arts and Sciences. She was a Guggenheim Fellow in 2006, and the Tukey Memorial Lecturer of the Bernoulli Society in 2012. She was President of IMS (Institute of Mathematical Statistics) in 20132014 and the Rietz Lecturer of IMS in 2016. She received the E. L. Scott Award from COPSS (Committee of Presidents of Statistical Societies) in 2018.