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<ul>
<li><p>A. R. Hsu, G. Zhou, Y. Cherapanamjeri, Y. Huang, A. Odisho, P. R. Carroll, B. Yu (2024). Efficient Automated Circuit Discovery in Transformers using Contextual Decomposition. <a href="https://arxiv.org/pdf/2407.00886">ICLR</a> </p><li>
<li><p>Y. S. Tan, O. Ronen, T. Saarinen, B. Yu (2024). The Computational Curse of Big Data for Bayesian Additive Regression Trees: a Hitting Time Analysis. <a href="https://arxiv.org/pdf/2406.19958">arXiv</a> </p><li>
<li><p>O. Ronen, A. I. Humayun, R. Balestriero, R. Baraniuk, B. Yu (2024). ScaLES: Scalable Latent Exploration Score for Pre-Trained Generative Networks. <a href="https://arxiv.org/pdf/2406.09657">arXiv</a> </p><li>
<li><p>S. Hayou, N. Ghosh, B. Yu (2024). The Impact of Initialization on LoRA Fineuning Dynamics. <a href="https://arxiv.org/pdf/2406.08447">NeurIPS</a> </p><li>
<li><p>B. Yu (2024). After Computational Reproducibility: Scientific Reproducibility and Trustworthy AI (discussion of Donoho's paper "Data Science at the Singularity"). <a href="https://hdsr.mitpress.mit.edu/pub/8qexde24/release/1">Harvard Data Science Review (HDSR)</a> </p><li>
<li><p>S. Hayou, N. Ghosh, B. Yu (2024). LoRA+: Efficient Low Rank Adaptation of Large Models. <a href="https://arxiv.org/abs/2402.12354">ICML</a> </p><li>
<li><p>C. F. Elliott, J. Duncan, T. M. Tang, M. Behr, K. Kumbier, B. Yu (2024). Designing a data science simulation with MERITS: a primer. <a href="https://arxiv.org/abs/2403.08971">arXiv</a> </p><li>
<li><p>Y. Chen, C. Singh, X. Liu, S. Zuo, B. Yu, H. He, J. G (2024). Towards Consistent Natural-Language Explanations via Explanation-Consistency Finetuning. <a href="https://arxiv.org/abs/2401.13986">COLING</a> </p><li>
<li><p>N. R. Mallinar, A. Zane, S. Frei, B. Yu (2024). Minimum-Norm Interpolation Under Covariate Shift. <a href="https://arxiv.org/pdf/2404.00522">ICML</a> </p><li>
<li><p>L. Sun, A. Agarwal, A. Kornblith, B. Yu, C. Xiong (2024). ED-Copilot: Reduce Emergency Department Wait Time with Language Model Diagnostic Assistance. <a href="https://arxiv.org/abs/2402.13448">ICML</a> </p><li>
<li><p>Q. Zhang, C. Singh, L. Liu, X. Liu, B. Yu, J. Gao, T. Zhao (2023). Tell your model where to attend: post-hoc attention steering for LLMs. <a href="https://arxiv.org/abs/2311.02262">ICLR</a> </p><li>
<li><p>Q. Wang*, T. M. Tang*, N. Youlton, C. S. Weldy, A. M. Kenney, O. Ronen, J. W. Hughes, E. T. Chin, S. C. Sutton. A. Agarwal, X. Li, M. Behr, K. Kumbier, C. S. Moravec, W. H. W. Tang, K. B. Margulies, T. P. Cappola, A. J. Buitte, R. Arnaout, J. B. Brown, J. R. Priest, V. N. Parikh, B. Yu*, E. Ashley* (2023). Epistasis regulates genetic control of cardiac hypertrophy. <a href="https://www.medrxiv.org/content/10.1101/2023.11.06.23297858v1 ">MedrXiv</a> </p><li>
<li><p>E. Irajizad, A. Kenney, T. Tang, J. Vykoukal, R. Wu, E. Murage, J. B. Dennison, M. Sans, J. P. Long, M. Loftus, J. A. Chabot, M. D. Kluger, F. Kastrinos, L. Brais, A. Babic, K. Jajoo, L. S. Lee, T. E. Clancy, K. Ng, A. Bullock, J. M. Genkinger, A. Maitra, K. A. Do, B. Yu, B. W. Wolpin, S. Hanash, J. F. Fahrmann. (2023) A blood-based metabolomic signature predictive of risk for pancreatic cancer. <a href="https://pubmed.ncbi.nlm.nih.gov/37729870/">Cell Reports Medicine</a> </p><li>
<li><p>R. Dwivedi, C. Singh, B. Yu, M. Wainwright (2023). Revisiting minimum description length complexity in overparametrized models. <a href="https://jmlr.org/papers/v24/21-1133.html">JMLR</a> </p><li>
<li><p>N. Ghosh, S. Frei, W. Ha, B. Yu (2023). The effect of SGD batch size on autoencoder learning: sparsity, sharpness and feature learning. <a href="https://arxiv.org/abs/2308.03215">arXiv</a> </p><li>
<li><p>J. Wu, P. L. Bartlett, M. Telgarsky, B. Yu (2024). Large Stepsize Gradient Descent for Logistic Loss: Non-Monotonicity of the Loss Improves Optimization Efficiency. <a href="https://arxiv.org/abs/2402.15926">arXiv</a> </p><li>
<li><p>R. Netzorg, J. Li, B. Yu (2024). Improving prototypical part networks with reward reweighting, reselection, and retraining. <a href="https://arxiv.org/abs/2307.03887">ICML</a> </p><li>
<li><p>A. Agarwal, A. M. Kenny, Y. S. Tan, T. M. Tang, B. Yu (2023). MDI+: a flexible random forest-based feature importance framework. <a href="https://arxiv.org/abs/2307.01932">arXiv</a> </p><li>
<li><p>A. R. Hsu, Y. Cherapanamjeri, B. Park, T. Naumann, A. Odisho, and B. Yu (2023). Diagnosing transformers: illuminating feature space for clinical decison-making. <a href="https://arxiv.org/abs/2305.17588">ICLR</a> </p><li>
<li><p>C. Singh*, A. R. Hsu*, R. Antonello, S. Jain, A. G. Huth, B. Yu and J. Gao (2023). Explaining black box text modules in natural language with language models. <a href="https://arxiv.org/abs/2305.09863">NeurIPS XAI In Action Workshop</a> </p><li>
<li><p>B. Park, X. Wu, B. Yu, A. Zhou (2022). Offline evaluation in RL: soft stability weighting to combine fitted Q-learning and model-based methods. <a href="https://openreview.net/forum?id=R3h3-bt0mq">NeurIPS 3rd Offline Reinforcement Learning Workshop</a> </p><li>
<li><p>Y. Tan, C. Singh, K. Nasseri, A. Agarwal, B. Yu (2022). Fast interpretable greedy-tree sums (FIGS). <a href="https://arxiv.org/abs/2201.11931">arXiv</a> </p><li>
<li><p>A. Agarwal, Y. Tan, O. Ronen, C. Singh, B. Yu (2022). Hierarchical shrinkage: improving accuracy and interpretability of tree-based methods. <a href="https://arxiv.org/abs/2202.00858">arXiv</a> </p><li>
<li><p>N. Ghosh, S. Mei, and B. Yu (2021). The three stages of dynamics in high-dimensional kernel methods. <a href="https://arxiv.org/abs/2111.07167">arXiv</a> </p><li>
<li><p>Y. Tan, A. Agarwal, and B. Yu (2021). A cautionary tale on fitting decision trees to data from additive models: generalization lower bounds. <a href="https://arxiv.org/abs/2110.09626">arXiv</a> </p><li>
<li><p>N. Altieri, B. Park, J. DeNero, A. Odisho, B. Yu. (2021). Improving natural language information extraction from cancer pathology reports using transfer learning and zero-shot string similarity. JAMIA Open. 2021 Sept. 30 4(3).</p><li>
<li><p> C. Singh, W. Ha and B. Yu (2021). Interpreting and Improving Deep-Learning Models with Reality Checks. <a href="https://arxiv.org/abs/2108.06847">arXiv</a> </p></li>
<li><p> B. Yu and C. Singh (2021). Seven Principles for Rapid-Response Data Science: Lessons from Covid-19 Forecasting. <a href="https://arxiv.org/abs/2108.08445">arXiv</a> </p></li>
<li><p> Ha, W., Singh, C., Lanusse, F., Song, E., Dang, S., He, K., Upadhyayula, S. and Yu, B., 2021. Adaptive wavelet distillation from neural networks through interpretations. <a href="https://arxiv.org/abs/2107.09145">NeurIPS</a> </p></li>
<li><p>
M. Behr, Y. Wang, X. Li, B. Yu (2021). Provable Boolean Interaction Recovery from Tree Ensemble obtained via Random Forests. <a href="https://arxiv.org/abs/2102.11800">arXiv</a></p></li>
<li><p>N. Altieri, B. Park, M. Olson, J. DeNero, A. Odisho, B. Yu. (2020). Enriched Annotations for Tumor Attribute Classification from Pathology Reports with Limited Labeled Data. https://arxiv.org/abs/2012.08113</p></li>
<li><p>
M. Behr*, K. Kumbier*, A. Cordova-Palomera, M. Aguirre, E. Ashley, A. Butte, R. Arnaout, J. B. Brown, J. Preist*, B. Yu* (2020) Learning epistatic polygenic phenotypes with Boolean interactions https://www.biorxiv.org/content/10.1101/2020.11.24.396846v1</p></li>
<li><p>B. Norgeot*, G. Quer, B. K. Beaulieu-Jones, A. Torkamani, R. Dias, M. Gianfrancesco, R. Arnaout, I. S. Kohane, S. Saria, E. Topol, Z. Obermeyer, B. Yu & A. Butte* (2020). Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist, Nature Medicine, 26, 1320–1324.</p></li>
<li><p>B. Yu (2020). Stability expanded, in reality. Harvard Data Science Review (HDSR).</p></li>
<li><p>B. Yu and R. Barter (2020). Data science process: one culture. JASA.</p></li>
<li><p>
R. Dwivedi*, Y. Tan*, B. Park, M. Wei, K. Horgan, D. Madigan*, B. Yu* (2020). Stable discovery of interpretable subgroups via calibration in causal studies (staDISC). International Statistical Review and also at arxiv.org/abs/2008.10109</p></li>
<li><p>
X. Li, T. M. Tang, X. Wang, J. A. Kocher, B. Yu (2020). A stability-driven protocol for drug response interpretable prediction (staDRIP). NeurISP workshop on ML4H (Machine learning for Health) Extended Abstract. https://arxiv.org/abs/2011.06593</p></li>
<li><p>
A. Y. Odisho, B. Park, N. Altieri, J. DeNero, M. R Cooperberg, P. R .Carroll, B. Yu (2020). Natural language processing systems for pathology parsing in limited data environments with uncertainty estimation. Journal of American Medical Informatics Association (JAMIA) Open.
</p></li>
<li>
<p>
L. Reiger, J. W. Murdoch, S. Singh, B. Yu (2020). Interpretations are Useful: Penalizing Explanations to Align Neural Networks with Prior Knowledge. ICML Proceedings.
</p>
</li>
<li>
<p>
C. Singh, W. Ha, F. Lanusse, V. Boehm , J. Liu, B. Yu (2020). Transformation Importance with Applications to Cosmology ICLR Workshop paper.</p>
</li>
<li>
<p>
Raaz Dwivedi, Chandan Singh, Bin Yu, Martin J. Wainwright
(2020)
<a href="https://arxiv.org/abs/2006.10189">
Revisiting complexity and the bias-variance tradeoff.
</a>
<em>
arxiv.org/abs/2006.10189
</em>
</p>
</li>
<li>
<p>
Nick Altieri, Rebecca Barter, James Duncan (UCB-biostats), Raaz Dwivedi, Karl Kumbier (UCSF), Xiao Li, Robbie Netzorg, Briton Park, Chandan Singh, Yan Shuo Tan, Tiffany Tang, Yu Wang, Bin Yu.
(2020)
<a href="https://arxiv.org/abs/2005.07882">
Curating a COVID-19 data repository and forecasting county-level death counts in the United States.
</a>
<em>
arxiv.org/abs/2005.07882.
</em>
<a href="https://covidseverity.com">
7-day prediction results through visualizations and maps.
</a>
<a href="/~binyu/ps/papers2020/RDSS20-yu.mp4">
Short talk video at Responsible Data Science Summit, July 28, 2020.
</a>
</p>
</li>
<li>
<p>
B. Yu and K. Kumbier (2020)
<a href="/~binyu/ps/papers2020/VDS20-YuKumbier.pdf">
Verdical data science
</a>
<em>
PNAS.
</em>
117 (8), 3920-3929.
<a href="/~binyu/ps/papers2020/VDS20-QnAsBinYu.pdf">
QnAs with Bin Yu.
</a>
</p>
</li>
<li>
<p>
R. Dwivedi, N. Ho, K. Khamaru, M. J. Wainwright, M. I. Jordan and B. Yu
(2020)
<a href="https://arxiv.org/abs/1902.00194">
Sharp Analysis of Expectation-Maximization for Weakly Identifiable Mixture
Models
</a>
<em>
AISTATS.
https://arxiv.org/abs/1902.00194
</em>
</p>
</li>
<li>
<p>
R. Dwivedi, N. Ho, K. Khamaru, M. J. Wainwright, M. I. Jordan and B. Yu
(2020)
<a href="https://arxiv.org/abs/1810.00828">
Singularity, Misspecification and the Convergence Rate of EM
</a>
<em>
Annals of Statistics.
https://arxiv.org/abs/1810.00828
</em>
</p>
</li>
<li>
<p>
Y. Chen, R. Dwivedi, M. J. Wainwright and B. Yu
(2020)
<a href="https://arxiv.org/abs/1905.12247">
Fast Mixing of Metropolized Hamiltonian Monte Carlo: Benefits of Multi-Step Gradients
</a>
<em>
arXiv preprint
https://arxiv.org/abs/1905.12247
</em>
</p>
</li>
<li>
<p>
R. Dwivedi, Y. Chen, M. J. Wainwright and B. Yu
(2020)
<a href="http://jmlr.org/papers/v20/19-306.html">
Log-concave Sampling: Metropolis Hastings Algorithms are Fast (2019)
</a>
<em>
JMLR (accepted).
http://jmlr.org/papers/v20/19-306.html
</em>
</p>
</li>
<li>
<p>
Y. Chen, R. Dwivedi, M. J. Wainwright and B. Yu
(2020)
<a href="http://jmlr.org/papers/v19/18-158.html">
Fast MCMC Algorithms on Polytopes
</a>
<em>
JMLR (accepted).
http://jmlr.org/papers/v19/18-158.html
</em>
</p>
</li>
<li>
<p>
Y. Chen, R. Dwivedi, M. J. Wainwright and B. Yu
(2020)
<a href="https://ieeexplore.ieee.org/abstract/document/8262876/">
Vaidya Walk: A Sampling Algorithm Based on Volumetric-Logarithmic Barrier
</a>
<em>
Allerton Conference 2017
https://ieeexplore.ieee.org/abstract/document/8262876/
</em>
</p>
</li>
<li>
<p>
W. J. Murdoch, C. Singh, K. Kumbier, R. Abbasi-Asl, and B. Yu (2019)
<a href="/~binyu/ps/papers2020/iML19-Murdochetal.pdf">
Interpretable machine learning: definitions, methods, and applications.
</a>
<em>
PNAS,
</em>
116 (44) 22071-22080.
</p>
</li>
<li>
<p>
Y. Wang, S. Wu and B. Yu (2019)
<a href="
https://arxiv.org/abs/1902.08380
">
Unique Sharp Local Minimum in â1-minimization Complete Dictionary Learning
</a>
<em>
JMLR (accepted with minor revision).
https://arxiv.org/abs/1902.08380
</em>
</p>
</li>
<li>
<p>
Y. Chen, R. Abbasi-Asl, A. Bloniarz, M. Oliver, B. Willmore, J. Gallant, and B. Yu (2018)
<a href="
https://www.biorxiv.org/content/10.1101/465534v1
">
The DeepTune framework for modeling and characterizing neurons in visual cortex area V4
</a>
<em>
https://www.biorxiv.org/content/10.1101/465534v1
</em>
</p>
</li>
<li>
<p>
K. Kumbier, S. Sumanta, J. B. Brown, S. Celniker, and B. Yu (2018)
<a href="
https://arxiv.org/abs/1810.07287
">
Refining interaction search through signed iterative Random Forests.
</a>
<em>
https://arxiv.org/abs/1810.07287
</em>
</p>
</li>
<li>
<p>
C. Singh, W. J. Murdoch, and B. Yu (2018)
<a href="
https://arxiv.org/abs/1806.05337
">
Hierarchical interpretations for neural network predictions.
</a>
<em>
https://arxiv.org/abs/1806.05337
</em>
</p>
</li>
<li>
<p>
Y. Chen C. Jin, and B. Yu (2018)
<a href="https://arxiv.org/abs/1804.01619">
Stability and Convergence Trade-off of Iterative
Optimization Algorithms.
</a>
<em>
https://arxiv.org/abs/1804.01619
</em>
</p>
</li>
<li>
<p>
J. Murdoch, P. Liu, and B. Yu (2018)
<a href="https://arxiv.org/abs/1801.05453">
Beyond word importance: contextual decomposition to extract interactions from LSTMs.
</a>
<em>
Proc. ICLR 2018. https://arxiv.org/abs/1705.07356
</em>
</p>
</li>
<li>
<p>
R. Diwivedi, Y. Chen, M. J. Wainwright, and B. Yu (2018)
<a href="
https://arxiv.org/abs/1801.02309
">
Log-concave sampling: Metropolis-Hastings algorithms are fast!
</a>
<em>
https://arxiv.org/abs/1801.02309.
</em>
</p>
</li>
<li>
<p>
Y. Chen, R. Dwivedi, M. J. Wainwright, and B. Yu (2017)
<a href="
https://arxiv.org/abs/1710.08165
">
Fast MCMC sampling algorithms on polytopes
</a>
<em>
https://arxiv.org/abs/1710.08165.
</em>
</p>
</li>
<li>
<p>
B. Yu and K. Kumbier (2018)
<a href=" /~binyu/ps/papers2018/AI+Stat18.pdf">
Artificial Intelligence and Statistics
</a>
<em>
Frontiers of Information
Technology and Electronic Engineering. 19(1), 6-9.
</em>
</p>
</li>
<li>
<p>
R. Abbasi-Asl and B. Yu (2017)
<a href="
https://arxiv.org/abs/1705.07356
">
Structural Compression of Convolutional Neural Networks Based on Greedy Filter Pruning
</a>
<em>
https://arxiv.org/abs/1705.07356
</em>
</p>
</li>
<li>
<p>
R. Abbasi-Asl and B. Yu (2017)
<a href="
https://arxiv.org/abs/1711.02329
">
Interpreting Convolutional Neural Networks Through Compression.
</a>
<em>
NIPS 2017. Symposium on Interpretable Machine Learning.
(also https://arxiv.org/abs/1711.02329)
</em>
</p>
</li>
<li>
<p>
S. Kunzel, J. Sekhon, P. Bickel, and B. Yu (2017)
<a href="
https://arxiv.org/abs/1706.03461
">
Meta-learners for Estimating Heterogeneous
Treatment Effects using Machine Learning
</a>
<em>
https://arxiv.org/abs/1706.03461.
</em>
</p>
</li>
<li>
<p>
S. Basu, K. Kumbier, J. B. Brown, and B. Yu (2018)
<a href="/~binyu/ps/papers2018/iRF+SI18.pdf">
iterative Random Forests to discover predictive and stable high-order interactions
</a>
<em>
PNAS (early edition).
</em>
</p>
</li>
<li>
<p>
S. Balakrishnan, M. Wainwright, B. Yu (2017)
<a href="/~binyu/ps/papers2017/EM17.pdf">
Statistical Guarantees for the EM algorithm: from population to sample-based analysis.
</a>
<em>
Annals of Statistics, 45(1), 77 - 120.
</em>
</p>
</li>
<li>
<p>
R. Barter and B. Yu (2017)
<a href="/~binyu/ps/papers2017/superheat17.pdf">
Superheat: An R package for creating beautiful and extendable heatmaps for visualizing complex data
</a>
<em>
JCGS (revised). https://github.com/rlbarter/superheat
</em>
</p>
</li>
<li>
<p>
H. Liu and B. Yu (2017)
<a href="/~binyu/ps/papers2017/LiuYu-test17.pdf">
Comments on: High-dimensional simultaneous
inference with the bootstrap by Dezeure et al
</a>
<em>
Test. 26: 740-750.
</em>
</p>
</li>
<li>
C. Carson et al (2016).
<a href="/~binyu/ps/FAB2016.pdf">
UC Berkeley Data Science Planning Initiative Faculty Advisory Board (FAB)
Report.
</a>
<a href="/~binyu/ps/FAB-Summary2016.pdf">
FAB Report Executive Summary
</a>
<p>
</p>
</li>
<li>
S. Wu and B. Yu (2018).
<a href="../ps/papers2018/local-iden18.pdf">
Local identifiability of l1-minimization dictionary learning: a sufficient and almost necessary condition.
</a>
<em>
JMLR. 18, 1 - 56.
</em>
<p>
</p>
</li>
<li>
K. Rohe, T. Qin and B. Yu (2016).
<a href="http://www.pnas.org/content/113/45/12679.full.pdf?with-ds=yes">
Co-clustering directed graphs to discover asymmetries
and directional communities.
</a>
<em>
Proc. National Academy of Sciences (PNAS), 113(45), 12679 - 12684.
</em>
<p>
</p>
</li>
<li>
R. E. Kass, B. S. Caffo, M. Davidian, X. Meng, B. Yu, Nancy Reid* (2016).
<a href="../ps/papers2016/Ten-Simple-Rules.pdf">
Ten simple rules for effective statistical practice.
</a>
<em>
PLoS Comput. Biol., 12(6): e1004961. doi:10.1371/journal.pcbi.1004961
</em>
<p>
</p>
</li>
<li>
Siqi Wu, Antony Joseph, Ann S. Hammonds, Susan E. Celniker, Bin Yu*, and Erwin Frise* (2016).
<a href="../ps/papers2016/staNMF+SI.pdf">
Stability-driven nonnegative matrix factorization to
interpret spatial gene expression and build local
gene networks (with support information).
</a>
<em>
PNAS, pp. 4290 - 4295.
</em>
<p>
</p>
</li>
<li>
A. Bloniarz, C. Wu, B. Yu, A. Talwalkar (2016).
<a href="../ps/papers2016/AISTATS16.pdf">
Supervised neighborhoods for distributed nonparametric regression.
</a>
<em>
Proc. of AISTATS, Barcelona, Spain.
</em>
<p>
</p>
</li>
<li>
B. Yu (2015).
<a href="http://www.odbms.org/2015/04/data-wisdom-for-data-science/">
Data wisdom for data science.
</a>
<em>
Operational Database Management Systems (ODBMS.ORG).
</em>
<p>
</p>
</li>
<li>
A. Bloniarz, H. Liu, C. Zhang, J. Sekhon, and B. Yu* (2015).
<a href="../ps/papers2016/lasso-adj+SI16.pdf">
Lasso adjustments of treatment effect estimates in randomized experiments.
</a>
<em>
PNAS. 113, 7383 - 7390.
</em>
<p>
</p>
</li>
<li>
P. Ma, M. W. Mahoney and B. Yu (2015).
<a href="../ps/papers2015/MaMahoneyYu2015.pdf">
A Statistical Perspective on Algorithmic Leveraging.
</a>
<em>
Journal of Machine Learning Research,
</em>
16, (2015), 861-911.
<p>
</p>
</li>
<li>
T. Moon, Y. Wang, Y. Liu, and B. Yu (2015).
<a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7047927">
Evaluation of a MISR-based high-resolution aerosol retrieval method using AERONET DRAGON campaign data.
</a>
<em>
IEEE Transactions on Geoscience and Remote Sensing, 53, 4328-4339.
</em>
<p>
</p>
</li>
<li>
B. Yu (2014).
<a href="../ps/papers2014/IMS-pres-address14-yu.pdf">
Let us own data science.
</a>
<a href="https://www.youtube.com/watch?v=92OjsYQJC1U">
video
</a>
<a href="http://bulletin.imstat.org/2014/10/ims-presidential-address-let-us-own-data-science/">
IMS Bulletin
</a>
<em>
Institute of Mathematical Statistics (IMS) Presidental Address, ASC-IMS Joint Co
nference, Sydney, July, 2014.
</em>
<p>
</p>
</li>
<li>
G. Schiebinger, M. J. Wainwright and B. Yu (2014).
<a href="http://arxiv.org/pdf/1404.7552v3.pdf">
The geometry of kernelized spectral clustering.
</a>
<em>
Annals of Statistics, 43, 819-846.
</em>
<p>
</p>
</li>
<li>
L. Miratrix, J. Jia, B. Yu, B. Gawalt, L. El Ghaoui, L. Barnesmoore, S. Clavier (2014).
<a href="../ps/papers2014/CCS14.pdf">
Concise comparative summaries (CCS) of large text corpora with a human experiment.
</a>
<em>
Ann. Applied Statist., 8, 499-529.
</em>
<p>
</p>
</li>
<li>
Y. Benjamini and B. Yu (2014).
<a href="../ps/papers2012/BenjaminiY12.pdf">
The shuttle estimator for explainable variance in fMRI experiments.
</a>
<em>
Annals of Applied Statistics, 7, 2007-2033.
</em>
<p>
</p>
</li>
<li>
D. Bean, P. Bickel, N. El Karoui and B. Yu (2014).
<a href="http://www.pnas.org/content/early/2013/08/08/1307845110.full.pdf+html">
Optimal M-estimation in high-dimensional regression.
</a>
<em>
Proceedings of National Academy of Sciences, 110, 1456314568.
</em>
<p>
</p>
</li>
<li>
N. El Karoui, D. Bean, P. Bickel, C. Lim, and B. Yu (2014).
<a href="http://www.pnas.org/content/early/2013/08/15/1307842110.full.pdf+html">
On robust regression with high-dimensional predictors.
</a>
<em>
Proceedings of National Academy of Sciences, 110, 1455714562.
</em>
<p>
</p>
</li>
<li>
P. Ma, M. W. Mahoney, B. Yu (2014).
<a href="http://arxiv.org/abs/1306.5362">
A Statistical Perspective on Algorithmic Leveraging.
</a>
<em>
Proc. of International Conference on Machine Learning (ICML)
</em>
(This conference paper contains some of preliminary results of the journal submission
<em>
Ma et al. (2015)
</em>
)
<p>
</p>
</li>
<li>
A. Bloniarz, A. Talwalkar, J. Terhorst, M. Jordan, D. Patterson, B. Yu and Y. Song (2014).
<a href="../ps/papers2014/Bloniarzetal14.pdf">
Changepoint Analysis for Efficient Variant Calling.
</a>
<em>
Proc. of RECOMB 2014 (to appear).
</em>
<p>
</p>
</li>
<li>
Tao Shi (2013),
<a href="http://www.yufamily.org/src/ShiTao_AnInterviewWithProfessorBinYu.php?xref=YuFamily_YuBin_interview_cn">
A conversation with Professor Bin Yu
</a>
<em>
International Chinese Statistical Association (ICSA) Bulletin, Vol 25, Issue 2, pp 85-98.
</em>
(
<a href="http://www.statsblogs.com/2013/07/09/a-conversation-with-professor-bin-yu/">
Selected Parts in Statblogs
</a>
)
<p>
</p>
</li>
<li>
A. Joseph and B. Yu (2016).
<a href="../ps/papers2016/reg-spectral16.pdf">
The impact of regularization on spectral clustering.
</a>
<em>
Annals of Statistics. 4, 1765 - 1791.
</em>
<p>
</p>
</li>
<li>
C. Lim and B. Yu (2016).
<a href="../ps/papers2016/ESCV16.pdf">
Estimation Stability with Cross Validation (ESCV)
</a>
<em>
Journal of Computational and Graphical Statistics. 25, 464 - 492.
</em>
<p>
</p>
</li>
<li>
A. S. Hammonds, C. A. Bristow, W. W. Fisher, R. Weiszmann, S. Wu, V. Hartenstein, M. Kellis,
B. Yu, E. Frise, and S. E. Celniker (2013).
<a href="http://genomebiology.com/2013/14/12/R140">
</a>
<a href="../ps/papers2013/Hammondsetal13.pdf">
Spatial expression of transcription factors in Drosophila embryonic organ development.
</a>
<em>
Genome Biology, 14(12), R140.
</em>
<p>
</p>
</li>
<li>
H. Liu and B. Yu (2013).
<a href="../ps/LassoOLSBootstrap13.pdf">
Asymptotic properties of Lasso+mLS and Lasso+Ridge in sparse high-dimensional linear regression.
</a>
<em>
Electron. J. Statist., 7, 312-3169.
</em>
<p>
</p>
</li>
<li>
J. Mairal and B. Yu (2013).
<a href="http://jmlr.org/papers/v14/mairal13a.html">
Supervised Feature Selection in Graphs with Path Coding Penalties and Network Flows.
</a>
<em>
Journal of Machine Learning Research, 14, 2449-2485.
</em>
<p>
</p>
</li>
<li>
Y. Wang, X. Jiang, B. Yu, M. Jiang (2013).
<a href="../ps/papers2013/WangJiangYuJiang2013.pdf">
A Hierarchical Bayesian Approach for Aerosol Retrieval Using MISR Data.
</a>
<em>
J. American Statistical Association, 108, 483-493.
</em>
<p>
</p>
</li>
<li>
Y. He, J. Jia and B. Yu (2013).
<a href="../ps/papers2013/rev-MCMC13.pdf">
Reversible MCMC on Markov equivalence classes of sparse directed acyclic graphs.
</a>
<a href="http://arxiv.org/abs/1209.5860">
</a>
<em>
Annals of Statistics, 41(4), 1742-1779.
</em>
<p>
</p>
</li>
<li>
B. Yu (2013).
<a href="../ps/papers2013/Yu13.pdf">
Stability.
</a>
<em>
Bernoulli, 19 (4), 1484-1500. (Invited paper for the Special Issue commemorating the 300th anniversary of the publication of Jakob Bernoullis Ars Conjectandi in 1712)
</em>
<p>
</p>
</li>
<li>
J. Mairal and B. Yu (2013).
<a>
Discussion on Grouping Strategies and Thresholding for High Dimensional Linear Models
</a>
<em>
Journal of Statistical Planning and Inference, 143, 1451-1453.
</em>
<p>
</p>
</li>
<li>
C. Uhler, G. Raskutti, and P. Buhlmann and B. Yu (2013).
<a href="../ps/papers2013/faithfulness13.pdf">
Geometry of faithfulness assumption in causal inference.
</a>
<em>
Annals of Statistics, 41, 436-463.
</em>
<p>
</p>
</li>
<li>
L. Miratrix, J. Sehkon, and B. Yu (2013).
<a href="../ps/papers2013/post-stratification13.pdf">
Adjusting Treatment Effect Estimates by Post-Stratification in
Randomized Experiments.
</a>
<em>
Journal of Royal Statistical Society, Series B, 75 (part 2), 369-396.
</em>
<p>
</p>
</li>
<li>
J. Jia, K. Rohe and B. Yu (2013)
<a href="../ps/papers2013/lasso-poisson13.pdf">
The Lasso under Poisson-like Heteroscadecity.
</a>
<em>
Statistica Sinica, 23, 99-118.
</em>
<p>
</p>
</li>
<li>
S. Negahban, P. Ravikumar, M. Wainwrigt, and B. Yu (2012)
<a href="../ps/unified-797.pdf">
A unified framework for high-dimensional analysis of
M-estimators with decomposable regularizers.
</a>
<em>
Statistical Science, 27, 538-557.
</em>
<p>
</p>
</li>
<li>
G. Raskutti, M. Wainwrigt, and B. Yu (2012)
<a href="../ps/papers2012/RaskuttiWY12.pdf">
Minimax-optimal rates for sparse additive models over
kernel classes via convex programming.
</a>
<em>
J. Machine Learning Research, 13, 389-427.
</em>
<p>
</p>
</li>
<li>
J. Mairal and B. Yu (2012).
<a href="../ps/Conferencepapers/MairalY12.pdf">
Complexity analysis of the Lasso regularization path.
</a>
<em>
Proc. of International Conference on Machine Learning (ICML) .
</em>
<p>
</p>
</li>
<li>
Yanfeng Gu, Shizhe Wang, Tao Shi, Yinghui Lu, Eugene E. Clothiaux, and Bin Yu (2012).
<a href="../ps/Conferencepapers/GuWSLCY12.pdf">
Multiple-kernel learning-based unmixing algorithm for estimation of cloud fractions with MODIS and CLOUDSAT data.
</a>
<em>
Proc. of IEEE International Geoscience and Remote Sensing Symposium (IGRSS).
</em>
<p>
</p>
</li>
<li>
S. Nishimoto, A. T. Vu, T. Naselaris, Y. Benjamini, B. Yu, J. L. Gallant (2011).
<a href="../ps/papers2011/NishimotoVNBYG11.pdf">
Reconstructing visual experiences from brain activity evoked by natural movies.
</a>
<em>
Current Biology, 21(19), 1641-1646.
</em>
<a href="https://sites.google.com/site/gallantlabucb/publications/nishimoto-et-al-2011">
related videos
</a>
<p>
</p>
</li>
<li>
P. Ravikumar, M. Wainwright, G. Raskutti, B. Yu (2011).
<a href="../ps/papers2011/RavikumarWRY11.pdf">
High-dimensional covariance estimation by minimizing l1-penalized log-determinant divergence.
</a>
<em>
Electronic Journal of Statistics, 5, 935-980.
</em>
<p>
</p>
</li>
<li>
G. Raskutti, M. Wainwright, B. Yu (2011).
<a href="../ps/papers2011/RaskuttiWainwrightYu2011.pdf">
Minimax rates of estimation for high-dimensional linear regression over lq-balls.
</a>
<em>
IEEE Trans. Inform. Th., 57(10), 6976-6994.
</em>
<p>
</p>
</li>
<li>
K. Rohe, S. Chatterjee, and B. Yu (2011).
<a href="../ps/papers2011/RoheCY11.pdf">
Spectral clustering and the high-dimensional Stochastic Block Model.
</a>
<em>
Annals of Statistics, 39 (4), 1878-1915
</em>
<p>
</p>
</li>
<li>
V. Q. Vu, P. Ravikumar, T. Naselaris, K. N. Kay, J. L. Gallant, B. Yu* (2011).
<a href="http://arxiv.org/pdf/1104.2805.pdf">
Encoding and decoding V1 fMRI responses to natural images with sparse nonparametric models.
</a>
<em>
Annals of Applied Statistics, 5, 1150-1182.
(*First senior author as last author in biology tradition)
</em>
<p>
</p>
</li>
<li>
S. N. Pakzad, G. Rocha, and B. Yu (2011).
<a>
Distributed modal identification by regularized auto regressive models.
</a>
<em>
International Journal of Systems Science, 42, 1473-1489.
</em>
<p>
</p>
</li>
<li>
J. Yousafzai, P. Sollich, Z. Cvetkovic, and B. Yu (2011).
<a href="http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5618550">
Combined Features and Kernel Design for Robust Phoneme Classification Using Support Vector Machines.
</a>
<em>
IEEE Trans. Audio, Speech and Language Processing (to appear).
64.
</em>
<p>
</p>
</li>
<li>
X. Dai, J. Jia, B. Yu, El Ghaoui (2011)
<a href="http://www.eecs.berkeley.edu/~elghaoui/Pubs/icsc2011.pdf">
SBA-term: Sparse Bilingual Association for terms.
</a>
<em>
Proc. International Conference on Semantic Computing.