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Viewing Publication 1 - 87 from 87

2023

Compositional Foundation Models for Hierarchical PlanningAnurag Ajay, Seungwook Han, Yilun Du, Shuang Li, Abhi Gupta, Tommi S. Jaakkola, Joshua B. Tenenbaum, Leslie Pack Kaelbling, Akash Srivastava, Pulkit Agrawal. nips 2023: [doi]

Is Conditional Generative Modeling all you need for Decision Making?Anurag Ajay, Yilun Du, Abhi Gupta, Joshua B. Tenenbaum, Tommi S. Jaakkola, Pulkit Agrawal. iclr 2023: [doi]

DiffDock: Diffusion Steps, Twists, and Turns for Molecular DockingGabriele Corso, Hannes Stärk, Bowen Jing, Regina Barzilay, Tommi S. Jaakkola. iclr 2023: [doi]

Compositional Sculpting of Iterative Generative ProcessesTimur Garipov, Sebastiaan De Peuter, Ge Yang, Vikas K. Garg, Samuel Kaski, Tommi S. Jaakkola. nips 2023: [doi]

Efficiently Controlling Multiple Risks with Pareto TestingBracha Laufer-Goldshtein, Adam Fisch, Regina Barzilay, Tommi S. Jaakkola. iclr 2023: [doi]

Diffusion Probabilistic Modeling of Protein Backbones in 3D for the motif-scaffolding problemBrian L. Trippe, Jason Yim, Doug Tischer, David Baker, Tamara Broderick, Regina Barzilay, Tommi S. Jaakkola. iclr 2023: [doi]

Stable Target Field for Reduced Variance Score Estimation in Diffusion ModelsYilun Xu, Shangyuan Tong, Tommi S. Jaakkola. iclr 2023: [doi]

PFGM++: Unlocking the Potential of Physics-Inspired Generative ModelsYilun Xu, Ziming Liu, Yonglong Tian, Shangyuan Tong, Max Tegmark, Tommi S. Jaakkola. icml 2023: 38566-38591 [doi]

Restart Sampling for Improving Generative ProcessesYilun Xu, Mingyang Deng, Xiang Cheng, Yonglong Tian, Ziming Liu, Tommi S. Jaakkola. nips 2023: [doi]

SE(3) diffusion model with application to protein backbone generationJason Yim, Brian L. Trippe, Valentin De Bortoli, Emile Mathieu, Arnaud Doucet, Regina Barzilay, Tommi S. Jaakkola. icml 2023: 40001-40039 [doi]

Towards Coherent Image Inpainting Using Denoising Diffusion Implicit ModelsGuanhua Zhang, Jiabao Ji, Yang Zhang, Mo Yu, Tommi S. Jaakkola, Shiyu Chang. icml 2023: 41164-41193 [doi]

2022

Fundamental Limits and Tradeoffs in Invariant Representation LearningHan Zhao 0002, Chen Dan 0001, Bryon Aragam, Tommi S. Jaakkola, Geoffrey J. Gordon, Pradeep Ravikumar. jmlr, 23, 2022. [doi]

Calibrated Selective ClassificationAdam Fisch, Tommi S. Jaakkola, Regina Barzilay. tmlr, 2022, 2022. [doi]

Independent SE(3)-Equivariant Models for End-to-End Rigid Protein DockingOctavian-Eugen Ganea, Xinyuan Huang, Charlotte Bunne, Yatao Bian, Regina Barzilay, Tommi S. Jaakkola, Andreas Krause 0001. iclr 2022: [doi]

Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-designWengong Jin, Jeremy Wohlwend, Regina Barzilay, Tommi S. Jaakkola. iclr 2022: [doi]

Torsional Diffusion for Molecular Conformer GenerationBowen Jing, Gabriele Corso, Jeffrey Chang, Regina Barzilay, Tommi S. Jaakkola. nips 2022: [doi]

Adversarial Support AlignmentShangyuan Tong, Timur Garipov, Yang Zhang, Shiyu Chang, Tommi S. Jaakkola. iclr 2022: [doi]

Crystal Diffusion Variational Autoencoder for Periodic Material GenerationTian Xie, Xiang Fu, Octavian-Eugen Ganea, Regina Barzilay, Tommi S. Jaakkola. iclr 2022: [doi]

Controlling Directions Orthogonal to a ClassifierYilun Xu, Hao He, Tianxiao Shen, Tommi S. Jaakkola. iclr 2022: [doi]

Poisson Flow Generative ModelsYilun Xu, Ziming Liu, Max Tegmark, Tommi S. Jaakkola. nips 2022: [doi]

2021

Few-Shot Conformal Prediction with Auxiliary TasksAdam Fisch, Tal Schuster, Tommi S. Jaakkola, Regina Barzilay. icml 2021: 3329-3339 [doi]

Efficient Conformal Prediction via Cascaded Inference with Expanded AdmissionAdam Fisch, Tal Schuster, Tommi S. Jaakkola, Regina Barzilay. iclr 2021: [doi]

Learning Task Informed AbstractionsXiang Fu, Ge Yang, Pulkit Agrawal, Tommi S. Jaakkola. icml 2021: 3480-3491 [doi]

GeoMol: Torsional Geometric Generation of Molecular 3D Conformer EnsemblesOctavian Ganea, Lagnajit Pattanaik, Connor W. Coley, Regina Barzilay, Klavs F. Jensen, William H. Green Jr., Tommi S. Jaakkola. nips 2021: 13757-13769 [doi]

Deep learning identifies synergistic drug combinations for treating COVID-19Wengong Jin, Jonathan M. Stokes, Richard T. Eastman, Zina Itkin, Alexey V. Zakharov, James J. Collins, Tommi S. Jaakkola, Regina Barzilay. pnas, 118(39), 2021. [doi]

Information Obfuscation of Graph Neural NetworksPeiyuan Liao, Han Zhao 0002, Keyulu Xu, Tommi S. Jaakkola, Geoffrey J. Gordon, Stefanie Jegelka, Ruslan Salakhutdinov. icml 2021: 6600-6610 [doi]

Consistent Accelerated Inference via Confident Adaptive TransformersTal Schuster, Adam Fisch, Tommi S. Jaakkola, Regina Barzilay. emnlp 2021: 4962-4979 [doi]

Mol2Image: Improved Conditional Flow Models for Molecule to Image SynthesisKarren D. Yang, Samuel Goldman, Wengong Jin, Alex X. Lu, Regina Barzilay, Tommi S. Jaakkola, Caroline Uhler. cvpr 2021: 6688-6698 [doi]

Understanding Interlocking Dynamics of Cooperative RationalizationMo Yu, Yang Zhang 0001, Shiyu Chang, Tommi S. Jaakkola. nips 2021: 12822-12835 [doi]

2020

Self-Supervised Learning of Appliance UsageChen-Yu Hsu 0001, Abbas Zeitoun, Guang-He Lee, Dina Katabi, Tommi S. Jaakkola. iclr 2020: [doi]

Unsupervised Hierarchy Matching with Optimal Transport over Hyperbolic SpacesDavid Alvarez-Melis, Youssef Mroueh, Tommi S. Jaakkola. aistats 2020: 1606-1617 [doi]

Invariant RationalizationShiyu Chang, Yang Zhang, Mo Yu, Tommi S. Jaakkola. icml 2020: 1448-1458 [doi]

Predicting deliberative outcomesVikas K. Garg, Tommi S. Jaakkola. icml 2020: 3408-3418 [doi]

Hierarchical Generation of Molecular Graphs using Structural MotifsWengong Jin, Regina Barzilay, Tommi S. Jaakkola. icml 2020: 4839-4848 [doi]

Oblique Decision Trees from Derivatives of ReLU NetworksGuang-He Lee, Tommi S. Jaakkola. iclr 2020: [doi]

Blank Language ModelsTianxiao Shen, Victor Quach, Regina Barzilay, Tommi S. Jaakkola. emnlp 2020: 5186-5198 [doi]

Improving Molecular Design by Stochastic Iterative Target AugmentationKevin Yang, Wengong Jin, Kyle Swanson, Regina Barzilay, Tommi S. Jaakkola. icml 2020: 10716-10726 [doi]

2019

Towards Optimal Transport with Global InvariancesDavid Alvarez-Melis, Stefanie Jegelka, Tommi S. Jaakkola. aistats 2019: 1870-1879 [doi]

A Game Theoretic Approach to Class-wise Selective RationalizationShiyu Chang, Yang Zhang, Mo Yu, Tommi S. Jaakkola. nips 2019: 10055-10065 [doi]

Solving graph compression via optimal transportVikas K. Garg, Tommi S. Jaakkola. nips 2019: 8012-8023 [doi]

High Dimensional Inference With Random Maximum A-Posteriori PerturbationsTamir Hazan, Francesco Orabona, Anand D. Sarwate, Subhransu Maji, Tommi S. Jaakkola. TIT, 65(10):6539-6560, 2019. [doi]

Generative Models for Graph-Based Protein DesignJohn Ingraham, Vikas K. Garg, Regina Barzilay, Tommi S. Jaakkola. nips 2019: 15794-15805 [doi]

Generative Models for Graph-Based Protein DesignJohn Ingraham, Vikas K. Garg, Regina Barzilay, Tommi S. Jaakkola. iclr 2019: [doi]

Learning Multimodal Graph-to-Graph Translation for Molecule OptimizationWengong Jin, Kevin Yang, Regina Barzilay, Tommi S. Jaakkola. iclr 2019: [doi]

Functional Transparency for Structured Data: a Game-Theoretic ApproachGuang-He Lee, Wengong Jin, David Alvarez-Melis, Tommi S. Jaakkola. icml 2019: 3723-3733 [doi]

Towards Robust, Locally Linear Deep NetworksGuang-He Lee, David Alvarez-Melis, Tommi S. Jaakkola. iclr 2019: [doi]

Tight Certificates of Adversarial Robustness for Randomly Smoothed ClassifiersGuang-He Lee, Yang Yuan, Shiyu Chang, Tommi S. Jaakkola. nips 2019: 4911-4922 [doi]

Direct Optimization through arg max for Discrete Variational Auto-EncoderGuy Lorberbom, Tommi S. Jaakkola, Andreea Gane, Tamir Hazan. nips 2019: 6200-6211 [doi]

Bidirectional Inference Networks: A Class of Deep Bayesian Networks for Health ProfilingHao Wang, Chengzhi Mao, Hao He, Mingmin Zhao, Tommi S. Jaakkola, Dina Katabi. AAAI 2019: 766-773 [doi]

Correction to Analyzing Learned Molecular Representations for Property PredictionKevin Yang, Kyle Swanson, Wengong Jin, Connor W. Coley, Philipp Eiden, Hua Gao, Angel Guzman-Perez, Timothy Hopper, Brian Kelley, Miriam Mathea, Andrew Palmer, Volker Settels, Tommi S. Jaakkola, Klavs F. Jensen, Regina Barzilay. jcisd, 59(12):5304-5305, 2019. [doi]

Analyzing Learned Molecular Representations for Property PredictionKevin Yang, Kyle Swanson, Wengong Jin, Connor W. Coley, Philipp Eiden, Hua Gao, Angel Guzman-Perez, Timothy Hopper, Brian Kelley, Miriam Mathea, Andrew Palmer, Volker Settels, Tommi S. Jaakkola, Klavs F. Jensen, Regina Barzilay. jcisd, 59(8):3370-3388, 2019. [doi]

Rethinking Cooperative Rationalization: Introspective Extraction and Complement ControlMo Yu, Shiyu Chang, Yang Zhang, Tommi S. Jaakkola. emnlp 2019: 4092-4101 [doi]

2018

Towards Robust Interpretability with Self-Explaining Neural NetworksDavid Alvarez-Melis, Tommi S. Jaakkola. nips 2018: 7786-7795 [doi]

Structured Optimal TransportDavid Alvarez-Melis, Tommi S. Jaakkola, Stefanie Jegelka. aistats 2018: 1771-1780 [doi]

Gromov-Wasserstein Alignment of Word Embedding SpacesDavid Alvarez-Melis, Tommi S. Jaakkola. emnlp 2018: 1881-1890 [doi]

The Variational Homoencoder: Learning to learn high capacity generative models from few examplesLuke B. Hewitt, Maxwell I. Nye, Andreea Gane, Tommi S. Jaakkola, Joshua B. Tenenbaum. uai 2018: 988-997 [doi]

Junction Tree Variational Autoencoder for Molecular Graph GenerationWengong Jin, Regina Barzilay, Tommi S. Jaakkola. icml 2018: 2328-2337 [doi]

Grounding Language for Transfer in Deep Reinforcement LearningKarthik Narasimhan, Regina Barzilay, Tommi S. Jaakkola. jair, 63:849-874, 2018. [doi]

2017

Aspect-augmented Adversarial Networks for Domain AdaptationYuan Zhang 0001, Regina Barzilay, Tommi S. Jaakkola. tacl, 5:515-528, 2017. [doi]

A causal framework for explaining the predictions of black-box sequence-to-sequence modelsDavid Alvarez-Melis, Tommi S. Jaakkola. emnlp 2017: 412-421 [doi]

Tree-structured decoding with doubly-recurrent neural networksDavid Alvarez-Melis, Tommi S. Jaakkola. iclr 2017: [doi]

Convolutional Embedding of Attributed Molecular Graphs for Physical Property PredictionConnor W. Coley, Regina Barzilay, William H. Green Jr., Tommi S. Jaakkola, Klavs F. Jensen. jcisd, 57(8):1757-1772, August 2017. [doi]

Local Aggregative GamesVikas K. Garg, Tommi S. Jaakkola. nips 2017: 5347-5357 [doi]

Predicting Organic Reaction Outcomes with Weisfeiler-Lehman NetworkWengong Jin, Connor W. Coley, Regina Barzilay, Tommi S. Jaakkola. nips 2017: 2604-2613 [doi]

Deriving Neural Architectures from Sequence and Graph KernelsTao Lei, Wengong Jin, Regina Barzilay, Tommi S. Jaakkola. icml 2017: 2024-2033 [doi]

Learning Optimal InterventionsJonas Mueller, David Reshef, George Du, Tommi S. Jaakkola. aistats 2017: 1039-1047 [doi]

Sequence to Better Sequence: Continuous Revision of Combinatorial StructuresJonas Mueller, David K. Gifford, Tommi S. Jaakkola. icml 2017: 2536-2544 [doi]

Style Transfer from Non-Parallel Text by Cross-AlignmentTianxiao Shen, Tao Lei, Regina Barzilay, Tommi S. Jaakkola. nips 2017: 6833-6844 [doi]

Learning Sleep Stages from Radio Signals: A Conditional Adversarial ArchitectureMingmin Zhao, Shichao Yue, Dina Katabi, Tommi S. Jaakkola, Matt T. Bianchi. icml 2017: 4100-4109 [doi]

2016

Learning Tree Structured Potential GamesVikas K. Garg, Tommi S. Jaakkola. nips 2016: 1552-1560 [doi]

CRAFT: ClusteR-specific Assorted Feature selecTionVikas K. Garg, Cynthia Rudin, Tommi S. Jaakkola. aistats 2016: 305-313 [doi]

Learning to refine text based recommendationsYouyang Gu, Tao Lei, Regina Barzilay, Tommi S. Jaakkola. emnlp 2016: 2103-2108 [doi]

Learning Population-Level Diffusions with Generative RNNsTatsunori B. Hashimoto, David K. Gifford, Tommi S. Jaakkola. icml 2016: 2417-2426 [doi]

Word Embeddings as Metric Recovery in Semantic SpacesTatsunori B. Hashimoto, David Alvarez-Melis, Tommi S. Jaakkola. tacl, 4:273-286, 2016. [doi]

Structured Prediction: From Gaussian Perturbations to Linear-Time Principled AlgorithmsJean Honorio, Tommi S. Jaakkola. uai 2016: [doi]

Rationalizing Neural PredictionsTao Lei, Regina Barzilay, Tommi S. Jaakkola. emnlp 2016: 107-117 [doi]

Semi-supervised Question Retrieval with Gated ConvolutionsTao Lei, Hrishikesh Joshi, Regina Barzilay, Tommi S. Jaakkola, Kateryna Tymoshenko, Alessandro Moschitti, Lluís Màrquez. naacl 2016: 1279-1289 [doi]

Ten Pairs to Tag - Multilingual POS Tagging via Coarse Mapping between EmbeddingsYuan Zhang, David Gaddy, Regina Barzilay, Tommi S. Jaakkola. naacl 2016: 1307-1317 [doi]

2015

2006

Data-Dependent RegularizationAdrian Corduneanu, Tommi S. Jaakkola. In Olivier Chapelle, Bernhard Schölkopf, Alexander Zien, editors, Semi-Supervised Learning. pages 169-190, The MIT Press, 2006. [doi]

2000

Bayesian parameter estimation via variational methodsTommi S. Jaakkola, Michael I. Jordan. sac, 10(1):25-37, 2000. [doi]

1999

Probabilistic kernel regression modelsTommi S. Jaakkola, David Haussler. aistats 1999: [doi]

1998

Improving the Mean Field Approximation Via the Use of Mixture DistributionsTommi S. Jaakkola, Michael I. Jordan. In Michael I. Jordan, editor, Learning in Graphical Models. Volume 89 of NATO ASI Series, pages 163-173, Springer Netherlands, 1998. [doi]

An Introduction to Variational Methods for Graphical ModelsMichael I. Jordan, Zoubin Ghahramani, Tommi S. Jaakkola, Lawrence K. Saul. In Michael I. Jordan, editor, Learning in Graphical Models. Volume 89 of NATO ASI Series, pages 105-161, Springer Netherlands, 1998. [doi]

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