Timezone: »
Bayesian Optimisation (BO) refers to a class of methods for global optimisation of a function f which is only accessible via point evaluations. It is typically used in settings where f is expensive to evaluate. A common use case for BO in machine learning is model selection, where it is not possible to analytically model the generalisation performance of a statistical model, and we resort to noisy and expensive training and validation procedures to choose the best model. Conventional BO methods have focused on Euclidean and categorical domains, which, in the context of model selection, only permits tuning scalar hyper-parameters of machine learning algorithms. However, with the surge of interest in deep learning, there is an increasing demand to tune neural network architectures. In this work, we develop NASBOT, a Gaussian process based BO framework for neural architecture search. To accomplish this, we develop a distance metric in the space of neural network architectures which can be computed efficiently via an optimal transport program. This distance might be of independent interest to the deep learning community as it may find applications outside of BO. We demonstrate that NASBOT outperforms other alternatives for architecture search in several cross validation based model selection tasks on multi-layer perceptrons and convolutional neural networks.
Author Information
Kirthevasan Kandasamy (Carnegie Mellon University)
Willie Neiswanger (Carnegie Mellon University)
Jeff Schneider (CMU)
Barnabas Poczos (Carnegie Mellon University)
Eric Xing (Petuum Inc. / Carnegie Mellon University)
Related Events (a corresponding poster, oral, or spotlight)
-
2018 Poster: Neural Architecture Search with Bayesian Optimisation and Optimal Transport »
Tue. Dec 4th through Wed the 5th Room Room 517 AB #166
More from the Same Authors
-
2021 : Personalized Benchmarking with the Ludwig Benchmarking Toolkit »
Avanika Narayan · Piero Molino · Karan Goel · Willie Neiswanger · Christopher Ré -
2021 : Synthetic Benchmarks for Scientific Research in Explainable Machine Learning »
Yang Liu · Sujay Khandagale · Colin White · Willie Neiswanger -
2021 : Multi-modal Self-supervised Pre-training for Large-scale Genome Data »
Shentong Mo · Xi Fu · Chenyang Hong · Yizhen Chen · Yuxuan Zheng · Xiangru Tang · Yanyan Lan · Zhiqiang Shen · Eric Xing -
2022 : Improving Molecule Properties Through 2-Stage VAE »
Chenghui Zhou · Barnabas Poczos -
2022 : Exploring Transformer Backbones for Heterogeneous Treatment Effect Estimation »
yifan zhang · Hanlin Zhang · Zachary Lipton · Li Erran Li · Eric Xing -
2023 Poster: PID-Inspired Inductive Biases for Deep Reinforcement Learning in Partially Observable Control Tasks »
Ian Char · Jeff Schneider -
2022 : Sample-Specific Contextualized Graphical Models Using Clinical and Molecular Data Reveal Transcriptional Network Heterogeneity Across 7000 Tumors »
Caleb Ellington · Ben Lengerich · Thomas Watkins · Jiekun Yang · Manolis Kellis · Eric Xing -
2022 Poster: Exploration via Planning for Information about the Optimal Trajectory »
Viraj Mehta · Ian Char · Joseph Abbate · Rory Conlin · Mark Boyer · Stefano Ermon · Jeff Schneider · Willie Neiswanger -
2021 : Bayesian Active Reinforcement Learning »
Viraj Mehta · Biswajit Paria · Jeff Schneider · Willie Neiswanger -
2021 Workshop: Math AI for Education (MATHAI4ED): Bridging the Gap Between Research and Smart Education »
Pan Lu · Yuhuai Wu · Sean Welleck · Xiaodan Liang · Eric Xing · James McClelland -
2021 : Reinforcement Learning for Autonomous Driving »
Jeff Schneider · Jeff Schneider -
2021 Poster: Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification »
Youngseog Chung · Willie Neiswanger · Ian Char · Jeff Schneider -
2021 Poster: Multi-task Learning of Order-Consistent Causal Graphs »
Xinshi Chen · Haoran Sun · Caleb Ellington · Eric Xing · Le Song -
2020 : Panel Discussion & Closing »
Yejin Choi · Alexei Efros · Chelsea Finn · Kristen Grauman · Quoc V Le · Yann LeCun · Ruslan Salakhutdinov · Eric Xing -
2020 Workshop: Self-Supervised Learning -- Theory and Practice »
Pengtao Xie · Shanghang Zhang · Pulkit Agrawal · Ishan Misra · Cynthia Rudin · Abdelrahman Mohamed · Wenzhen Yuan · Barret Zoph · Laurens van der Maaten · Xingyi Yang · Eric Xing -
2020 Poster: Modeling Task Effects on Meaning Representation in the Brain via Zero-Shot MEG Prediction »
Mariya Toneva · Otilia Stretcu · Barnabas Poczos · Leila Wehbe · Tom Mitchell -
2020 Poster: Regularizing Black-box Models for Improved Interpretability »
Gregory Plumb · Maruan Al-Shedivat · Ángel Alexander Cabrera · Adam Perer · Eric Xing · Ameet Talwalkar -
2020 Poster: AutoSync: Learning to Synchronize for Data-Parallel Distributed Deep Learning »
Hao Zhang · Yuan Li · Zhijie Deng · Xiaodan Liang · Lawrence Carin · Eric Xing -
2020 Poster: Improving GAN Training with Probability Ratio Clipping and Sample Reweighting »
Yue Wu · Pan Zhou · Andrew Wilson · Eric Xing · Zhiting Hu -
2020 Poster: Robust Density Estimation under Besov IPM Losses »
Ananya Uppal · Shashank Singh · Barnabas Poczos -
2020 Spotlight: Robust Density Estimation under Besov IPM Losses »
Ananya Uppal · Shashank Singh · Barnabas Poczos -
2019 : Coffee + Posters »
Benjamin Caine · Renhao Wang · Nazmus Sakib · Nana Otawara · Meha Kaushik · elmira amirloo · Nemanja Djuric · Johanna Rock · Tanmay Agarwal · Angelos Filos · Panagiotis Tigkas · Donsuk Lee · Wootae Jeon · Nikita Jaipuria · Pin Wang · Jinxin Zhao · Liangjun Zhang · Ashutosh Singh · Ershad Banijamali · Mohsen Rohani · Aman Sinha · Ameya Joshi · Ching-Yao Chan · Mohammed Abdou · Changhao Chen · Jong-Chan Kim · eslam mohamed · Matt OKelly · Nirvan Singhania · Hiroshi Tsukahara · Atsushi Keyaki · Praveen Palanisamy · Justin Norden · Micol Marchetti-Bowick · Yiming Gu · Hitesh Arora · Shubhankar Deshpande · Jeff Schneider · Shangling Jui · Vaneet Aggarwal · Tryambak Gangopadhyay · Qiaojing Yan -
2019 : Poster Presentations »
Rahul Mehta · Andrew Lampinen · Binghong Chen · Sergio Pascual-Diaz · Jordi Grau-Moya · Aldo Faisal · Jonathan Tompson · Yiren Lu · Khimya Khetarpal · Martin Klissarov · Pierre-Luc Bacon · Doina Precup · Thanard Kurutach · Aviv Tamar · Pieter Abbeel · Jinke He · Maximilian Igl · Shimon Whiteson · Wendelin Boehmer · Raphaël Marinier · Olivier Pietquin · Karol Hausman · Sergey Levine · Chelsea Finn · Tianhe Yu · Lisa Lee · Benjamin Eysenbach · Emilio Parisotto · Eric Xing · Ruslan Salakhutdinov · Hongyu Ren · Anima Anandkumar · Deepak Pathak · Christopher Lu · Trevor Darrell · Alexei Efros · Phillip Isola · Feng Liu · Bo Han · Gang Niu · Masashi Sugiyama · Saurabh Kumar · Janith Petangoda · Johan Ferret · James McClelland · Kara Liu · Animesh Garg · Robert Lange -
2019 : Morning Coffee Break & Poster Session »
Eric Metodiev · Keming Zhang · Markus Stoye · Randy Churchill · Soumalya Sarkar · Miles Cranmer · Johann Brehmer · Danilo Jimenez Rezende · Peter Harrington · AkshatKumar Nigam · Nils Thuerey · Lukasz Maziarka · Alvaro Sanchez Gonzalez · Atakan Okan · James Ritchie · N. Benjamin Erichson · Harvey Cheng · Peihong Jiang · Seong Ho Pahng · Samson Koelle · Sami Khairy · Adrian Pol · Rushil Anirudh · Jannis Born · Benjamin Sanchez-Lengeling · Brian Timar · Rhys Goodall · Tamás Kriváchy · Lu Lu · Thomas Adler · Nathaniel Trask · Noëlie Cherrier · Tomohiko Konno · Muhammad Kasim · Tobias Golling · Zaccary Alperstein · Andrei Ustyuzhanin · James Stokes · Anna Golubeva · Ian Char · Ksenia Korovina · Youngwoo Cho · Chanchal Chatterjee · Tom Westerhout · Gorka Muñoz-Gil · Juan Zamudio-Fernandez · Jennifer Wei · Brian Lee · Johannes Kofler · Bruce Power · Nikita Kazeev · Andrey Ustyuzhanin · Artem Maevskiy · Pascal Friederich · Arash Tavakoli · Willie Neiswanger · Bohdan Kulchytskyy · sindhu hari · Paul Leu · Paul Atzberger -
2019 : Opening Remarks »
Manzil Zaheer · Nicholas Monath · Ari Kobren · Junier Oliva · Barnabas Poczos · Ruslan Salakhutdinov · Andrew McCallum -
2019 Workshop: Sets and Partitions »
Nicholas Monath · Manzil Zaheer · Andrew McCallum · Ari Kobren · Junier Oliva · Barnabas Poczos · Ruslan Salakhutdinov -
2019 : Poster Session »
Rishav Chourasia · Yichong Xu · Corinna Cortes · Chien-Yi Chang · Yoshihiro Nagano · So Yeon Min · Benedikt Boecking · Phi Vu Tran · Kamyar Ghasemipour · Qianggang Ding · Shouvik Mani · Vikram Voleti · Rasool Fakoor · Miao Xu · Kenneth Marino · Lisa Lee · Volker Tresp · Jean-Francois Kagy · Marvin Zhang · Barnabas Poczos · Dinesh Khandelwal · Adrien Bardes · Evan Shelhamer · Jiacheng Zhu · Ziming Li · Xiaoyan Li · Dmitrii Krasheninnikov · Ruohan Wang · Mayoore Jaiswal · Emad Barsoum · Suvansh Sanjeev · Theeraphol Wattanavekin · Qizhe Xie · Sifan Wu · Yuki Yoshida · David Kanaa · Sina Khoshfetrat Pakazad · Mehdi Maasoumy -
2019 Workshop: Learning with Rich Experience: Integration of Learning Paradigms »
Zhiting Hu · Andrew Wilson · Chelsea Finn · Lisa Lee · Taylor Berg-Kirkpatrick · Ruslan Salakhutdinov · Eric Xing -
2019 Poster: Nonparametric Density Estimation & Convergence Rates for GANs under Besov IPM Losses »
Ananya Uppal · Shashank Singh · Barnabas Poczos -
2019 Oral: Nonparametric Density Estimation & Convergence Rates for GANs under Besov IPM Losses »
Ananya Uppal · Shashank Singh · Barnabas Poczos -
2019 Poster: Learning Robust Global Representations by Penalizing Local Predictive Power »
Haohan Wang · Songwei Ge · Zachary Lipton · Eric Xing -
2019 Poster: Learning Data Manipulation for Augmentation and Weighting »
Zhiting Hu · Bowen Tan · Russ Salakhutdinov · Tom Mitchell · Eric Xing -
2019 Poster: Learning Sample-Specific Models with Low-Rank Personalized Regression »
Ben Lengerich · Bryon Aragam · Eric Xing -
2019 Poster: Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels »
Simon Du · Kangcheng Hou · Russ Salakhutdinov · Barnabas Poczos · Ruosong Wang · Keyulu Xu -
2019 Poster: Offline Contextual Bayesian Optimization »
Ian Char · Youngseog Chung · Willie Neiswanger · Kirthevasan Kandasamy · Oak Nelson · Mark Boyer · Egemen Kolemen · Jeff Schneider -
2019 Poster: Learning Local Search Heuristics for Boolean Satisfiability »
Emre Yolcu · Barnabas Poczos -
2018 Poster: The Sample Complexity of Semi-Supervised Learning with Nonparametric Mixture Models »
Chen Dan · Liu Leqi · Bryon Aragam · Pradeep Ravikumar · Eric Xing -
2018 Poster: Nonparametric Density Estimation under Adversarial Losses »
Shashank Singh · Ananya Uppal · Boyue Li · Chun-Liang Li · Manzil Zaheer · Barnabas Poczos -
2018 Poster: Symbolic Graph Reasoning Meets Convolutions »
Xiaodan Liang · Zhiting Hu · Hao Zhang · Liang Lin · Eric Xing -
2018 Poster: DAGs with NO TEARS: Continuous Optimization for Structure Learning »
Xun Zheng · Bryon Aragam · Pradeep Ravikumar · Eric Xing -
2018 Spotlight: DAGs with NO TEARS: Continuous Optimization for Structure Learning »
Xun Zheng · Bryon Aragam · Pradeep Ravikumar · Eric Xing -
2018 Poster: Learning Pipelines with Limited Data and Domain Knowledge: A Study in Parsing Physics Problems »
Mrinmaya Sachan · Kumar Avinava Dubey · Tom Mitchell · Dan Roth · Eric Xing -
2018 Poster: Deep Generative Models with Learnable Knowledge Constraints »
Zhiting Hu · Zichao Yang · Russ Salakhutdinov · LIANHUI Qin · Xiaodan Liang · Haoye Dong · Eric Xing -
2018 Poster: Hybrid Retrieval-Generation Reinforced Agent for Medical Image Report Generation »
Yuan Li · Xiaodan Liang · Zhiting Hu · Eric Xing -
2018 Poster: Unsupervised Text Style Transfer using Language Models as Discriminators »
Zichao Yang · Zhiting Hu · Chris Dyer · Eric Xing · Taylor Berg-Kirkpatrick -
2017 : Distribution Regression and its Applications. »
Barnabas Poczos -
2017 Oral: Deep Sets »
Manzil Zaheer · Satwik Kottur · Siamak Ravanbakhsh · Barnabas Poczos · Ruslan Salakhutdinov · Alexander Smola -
2017 Poster: Hypothesis Transfer Learning via Transformation Functions »
Simon Du · Jayanth Koushik · Aarti Singh · Barnabas Poczos -
2017 Poster: MMD GAN: Towards Deeper Understanding of Moment Matching Network »
Chun-Liang Li · Wei-Cheng Chang · Yu Cheng · Yiming Yang · Barnabas Poczos -
2017 Poster: Deep Sets »
Manzil Zaheer · Satwik Kottur · Siamak Ravanbakhsh · Barnabas Poczos · Ruslan Salakhutdinov · Alexander Smola -
2017 Poster: Structured Generative Adversarial Networks »
Zhijie Deng · Hao Zhang · Xiaodan Liang · Luona Yang · Shizhen Xu · Jun Zhu · Eric Xing -
2017 Poster: Gradient Descent Can Take Exponential Time to Escape Saddle Points »
Simon Du · Chi Jin · Jason D Lee · Michael Jordan · Aarti Singh · Barnabas Poczos -
2017 Spotlight: Gradient Descent Can Take Exponential Time to Escape Saddle Points »
Simon Du · Chi Jin · Jason D Lee · Michael Jordan · Aarti Singh · Barnabas Poczos -
2016 : Eric Xing »
Eric Xing -
2016 Poster: Variance Reduction in Stochastic Gradient Langevin Dynamics »
Kumar Avinava Dubey · Sashank J. Reddi · Sinead Williamson · Barnabas Poczos · Alexander Smola · Eric Xing -
2016 Poster: The Multi-fidelity Multi-armed Bandit »
Kirthevasan Kandasamy · Gautam Dasarathy · Barnabas Poczos · Jeff Schneider -
2016 Poster: Finite-Sample Analysis of Fixed-k Nearest Neighbor Density Functional Estimators »
Shashank Singh · Barnabas Poczos -
2016 Poster: Learning HMMs with Nonparametric Emissions via Spectral Decompositions of Continuous Matrices »
Kirthevasan Kandasamy · Maruan Al-Shedivat · Eric Xing -
2016 Poster: Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization »
Sashank J. Reddi · Suvrit Sra · Barnabas Poczos · Alexander Smola -
2016 Poster: Stochastic Variational Deep Kernel Learning »
Andrew Wilson · Zhiting Hu · Russ Salakhutdinov · Eric Xing -
2016 Poster: Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations »
Kirthevasan Kandasamy · Gautam Dasarathy · Junier B Oliva · Jeff Schneider · Barnabas Poczos -
2016 Poster: Efficient Nonparametric Smoothness Estimation »
Shashank Singh · Simon Du · Barnabas Poczos -
2015 : Bayesian Optimization and Embedded Learning Systems »
Jeff Schneider -
2015 Workshop: Nonparametric Methods for Large Scale Representation Learning »
Andrew G Wilson · Alexander Smola · Eric Xing -
2015 Poster: Nonparametric von Mises Estimators for Entropies, Divergences and Mutual Informations »
Kirthevasan Kandasamy · Akshay Krishnamurthy · Barnabas Poczos · Larry Wasserman · james m robins -
2015 Poster: On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants »
Sashank J. Reddi · Ahmed Hefny · Suvrit Sra · Barnabas Poczos · Alexander Smola -
2015 Poster: The Human Kernel »
Andrew Wilson · Christoph Dann · Chris Lucas · Eric Xing -
2015 Spotlight: The Human Kernel »
Andrew Wilson · Christoph Dann · Chris Lucas · Eric Xing -
2014 Workshop: Modern Nonparametrics 3: Automating the Learning Pipeline »
Eric Xing · Mladen Kolar · Arthur Gretton · Samory Kpotufe · Han Liu · Zoltán Szabó · Alan Yuille · Andrew G Wilson · Ryan Tibshirani · Sasha Rakhlin · Damian Kozbur · Bharath Sriperumbudur · David Lopez-Paz · Kirthevasan Kandasamy · Francesco Orabona · Andreas Damianou · Wacha Bounliphone · Yanshuai Cao · Arijit Das · Yingzhen Yang · Giulia DeSalvo · Dmitry Storcheus · Roberto Valerio -
2014 Workshop: Modern Machine Learning and Natural Language Processing »
Ankur P Parikh · Avneesh Saluja · Chris Dyer · Eric Xing -
2014 Poster: Flexible Transfer Learning under Support and Model Shift »
Xuezhi Wang · Jeff Schneider -
2014 Poster: On Model Parallelization and Scheduling Strategies for Distributed Machine Learning »
Seunghak Lee · Jin Kyu Kim · Xun Zheng · Qirong Ho · Garth Gibson · Eric Xing -
2014 Poster: Exponential Concentration of a Density Functional Estimator »
Shashank Singh · Barnabas Poczos -
2014 Poster: Dependent nonparametric trees for dynamic hierarchical clustering »
Kumar Avinava Dubey · Qirong Ho · Sinead Williamson · Eric Xing -
2013 Poster: More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server »
Qirong Ho · James Cipar · Henggang Cui · Seunghak Lee · Jin Kyu Kim · Phillip B. Gibbons · Garth Gibson · Greg Ganger · Eric Xing -
2013 Oral: More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server »
Qirong Ho · James Cipar · Henggang Cui · Seunghak Lee · Jin Kyu Kim · Phillip B. Gibbons · Garth Gibson · Greg Ganger · Eric Xing -
2013 Poster: Variance Reduction for Stochastic Gradient Optimization »
Chong Wang · Xi Chen · Alexander Smola · Eric Xing -
2013 Poster: Learning Hidden Markov Models from Non-sequence Data via Tensor Decomposition »
Tzu-Kuo Huang · Jeff Schneider -
2013 Poster: Restricting exchangeable nonparametric distributions »
Sinead Williamson · Steven MacEachern · Eric Xing -
2013 Spotlight: Restricting exchangeable nonparametric distributions »
Sinead Williamson · Steven MacEachern · Eric Xing -
2013 Poster: A Scalable Approach to Probabilistic Latent Space Inference of Large-Scale Networks »
Junming Yin · Qirong Ho · Eric Xing -
2013 Poster: Σ-Optimality for Active Learning on Gaussian Random Fields »
Yifei Ma · Roman Garnett · Jeff Schneider -
2012 Workshop: Spectral Algorithms for Latent Variable Models »
Ankur P Parikh · Le Song · Eric Xing -
2012 Poster: Monte Carlo Methods for Maximum Margin Supervised Topic Models »
Qixia Jiang · Jun Zhu · Maosong Sun · Eric Xing -
2012 Poster: On Triangular versus Edge Representations --- Towards Scalable Modeling of Networks »
Qirong Ho · Junming Yin · Eric Xing -
2012 Poster: Symmetric Correspondence Topic Models for Multilingual Text Analysis »
Kosuke Fukumasu · Koji Eguchi · Eric Xing -
2012 Spotlight: Symmetric Correspondence Topic Models for Multilingual Text Analysis »
Kosuke Fukumasu · Koji Eguchi · Eric Xing -
2011 Poster: Group Anomaly Detection using Flexible Genre Models »
Liang Xiong · Barnabas Poczos · Jeff Schneider -
2011 Poster: Infinite Latent SVM for Classification and Multi-task Learning »
Jun Zhu · Ning Chen · Eric Xing -
2011 Poster: Kernel Embeddings of Latent Tree Graphical Models »
Le Song · Ankur P Parikh · Eric Xing -
2011 Poster: Large-Scale Category Structure Aware Image Categorization »
Bin Zhao · Li Fei-Fei · Eric Xing -
2011 Poster: Learning Auto-regressive Models from Sequence and Non-sequence Data »
Tzu-Kuo Huang · Jeff Schneider -
2010 Poster: Large Margin Learning of Upstream Scene Understanding Models »
Jun Zhu · Li-Jia Li · Li Fei-Fei · Eric Xing -
2010 Poster: Learning Multiple Tasks with a Sparse Matrix-Normal Penalty »
Yi Zhang · Jeff Schneider -
2010 Poster: Predictive Subspace Learning for Multi-view Data: a Large Margin Approach »
Ning Chen · Jun Zhu · Eric Xing -
2010 Poster: Object Bank: A High-Level Image Representation for Scene Classification & Semantic Feature Sparsification »
Li-Jia Li · Hao Su · Eric Xing · Li Fei-Fei -
2010 Poster: Estimation of Renyi Entropy and Mutual Information Based on Generalized Nearest-Neighbor Graphs »
David Pal · Barnabas Poczos · Csaba Szepesvari -
2010 Poster: Adaptive Multi-Task Lasso: with Application to eQTL Detection »
Seunghak Lee · Jun Zhu · Eric Xing -
2009 Poster: Heterogeneous multitask learning with joint sparsity constraints »
Xiaolin Yang · Seyoung Kim · Eric Xing -
2009 Poster: Time-Varying Dynamic Bayesian Networks »
Le Song · Mladen Kolar · Eric Xing -
2009 Spotlight: Time-Varying Dynamic Bayesian Networks »
Le Song · Mladen Kolar · Eric Xing -
2009 Poster: Sparsistent Learning of Varying-coefficient Models with Structural Changes »
Mladen Kolar · Le Song · Eric Xing -
2009 Spotlight: Sparsistent Learning of Varying-coefficient Models with Structural Changes »
Mladen Kolar · Le Song · Eric Xing -
2008 Workshop: Analyzing Graphs: Theory and Applications »
Edo M Airoldi · David Blei · Jake M Hofman · Tony Jebara · Eric Xing -
2008 Poster: Mixed Membership Stochastic Blockmodels »
Edo M Airoldi · David Blei · Stephen E Fienberg · Eric Xing -
2008 Spotlight: Mixed Membership Stochastic Blockmodels »
Edo M Airoldi · David Blei · Stephen E Fienberg · Eric Xing -
2008 Poster: Partially Observed Maximum Entropy Discrimination Markov Networks »
Jun Zhu · Eric Xing · Bo Zhang -
2008 Poster: Learning the Semantic Correlation: An Alternative Way to Gain from Unlabeled Text »
Yi Zhang · Jeff Schneider · Artur Dubrawski -
2007 Workshop: Statistical Network Models »
Kevin Murphy · Lise Getoor · Eric Xing · Raphael Gottardo -
2007 Poster: HM-BiTAM: Bilingual Topic Exploration, Word Alignment, and Translation »
Bing Zhao · Eric Xing -
2006 Poster: A Hidden Markov Dirichlet Process Model for Genetic Recombination in Open Ancestral Space »
KyungAh Sohn · Eric Xing -
2006 Talk: A Hidden Markov Dirichlet Process Model for Genetic Recombination in Open Ancestral Space »
KyungAh Sohn · Eric Xing