Tommi S. Jaakkola, Ph.D. Thomas Siebel Professor of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society MIT Computer Science and Artificial Intelligence Laboratory Stata Center, Bldg 32-G470 Cambridge, MA 02139 tommi at csail dot mit dot edu [home]   [papers]   [research]   [courses]   [people]

The list does not include all recent preprints from arXiv or bioRxiv. For a more complete list, see my Google scholar page

Papers in reverse chronological order

• Cai Zhou, Chenxiao Yang, Yi Hu, Chenyu Wang, Chubin Zhang, Muhan Zhang, Lester Mackey, Tommi Jaakkola, Stephen Bates, and Dinghuai Zhang.
  Coevolutionary Continuous Discrete Diffusion: Make Your Diffusion Language Model a Latent Reasoner.
  International Conference on Machine Learning (ICML), 2026.
  [link]
• Abhi Gupta, Polina Barabanshchikova, Vikas Garg, Samuel Kaski, and Tommi Jaakkola.
  Divide-and-Denoise: A Game-Theoretic Method for Fairly Composing Diffusion Models.
  International Conference on Machine Learning (ICML), 2026.
  [link]
• Peter Holderrieth, Douglas Chen, Luca Eyring, Ishin Shah, Giri Anantharaman, Yutong He, Zeynep Akata, Tommi Jaakkola, Nicholas Matthew Boffi, and Max Simchowitz.
  Diamond Maps: Efficient Reward Alignment via Stochastic Flow Maps.
  International Conference on Machine Learning (ICML), 2026.
  [link]
• Bowen Jing, Bonnie Berger, and Tommi Jaakkola.
  AI-based methods for simulating, sampling, and predicting protein ensembles.
  Current Opinion in Structural Biology, 98. 2026.
  [link]
• Shangyuan Tong, Nanye Ma, Saining Xie, and Tommi Jaakkola.
  Flow Map Distillation Without Data.
  The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026.
  [link]
• Peter Holderrieth, Uriel Singer, Tommi Jaakkola, Ricky T. Q. Chen, Yaron Lipman, and Brian Karrer.
  GLASS Flows: Efficient Inference for Reward Alignment of Flow and Diffusion Models.
  The 14th International Conference on Learning Representations (ICLR), 2026.
  [link]
• Chenyu Wang, Paria Rashidinejad, DiJia Su, Song Jiang, Sid Wang, Siyan Zhao, Cai Zhou, Shannon Zejiang Shen, Feiyu Chen, Tommi Jaakkola, Yuandong Tian, and Bo Liu.
  SPG: Sandwiched Policy Gradient for Masked Diffusion Language Models.
  The 14th International Conference on Learning Representations (ICLR), 2026.
  [link]
• Woody Ahern, Jason Yim, Doug Tischer, Saman Salike, Seth M. Woodbury, Donghyo Kim, Indrek Kalvet, Yakov Kipnis, Brian Coventry, Han Raut Altae-Tran, Magnus S. Bauer, Regina Barzilay, Tommi S. Jaakkola, Rohith Krishna, and David Baker.
  Atom-level enzyme active site scaffolding using RFdiffusion2.
  Nature Methods, 23, pp. 96–105. 2025.
  [link]
• Chenyu Wang, Cai Zhou, Sharut Gupta, Zongyu Lin, Stefanie Jegelka, Stephen Bates, and Tommi Jaakkola.
  Learning Diffusion Models with Flexible Representation Guidance.
  Neural Information Processing Systems (NeurIPS), 2025.
  [link]
• Cai Zhou, Chenyu Wang, Dinghuai Zhang, Shangyuan Tong, Yifei Wang, Stephen Bates, and Tommi Jaakkola.
  Next Semantic Scale Prediction via Hierarchical Diffusion Language Models.
  Neural Information Processing Systems (NeurIPS), 2025.
  [link]
• Ryotaro Okabe, Mouyang Cheng, Abhijatmedhi Chotrattanapituk, Manasi Mandal, Kiran Mak, Denisse Cordova Carrizales, Nguyen Tuan Hung, Xiang Fu, Bowen Han, Yao Wang, Weiwei Xie, Robert J. Cava, Tommi S. Jaakkola, Yongqiang Cheng, and Mingda Li.
  Structural constraint integration in a generative model for the discovery of quantum materials.
  Nature Materials, 2025.
  [link]
• Menghua Wu, Cai Zhou, Stephen Bates, and Tommi Jaakkola.
  Thought calibration: Efficient and confident test-time scaling.
  Empirical Methods in Natural Language Processing (EMNLP), 2025.
  [link]
• Peter Holderrieth, Michael S. Albergo, and Tommi Jaakkola.
  LEAPS: A discrete neural sampler via locally equivariant networks.
  International Conference on Machine Learning (ICML), 2025.
  [link]
• Menghua Wu, Umesh Padia, Sean H. Murphy, Regina Barzilay, and Tommi Jaakkola.
  Identifying biological perturbation targets through causal differential networks.
  International Conference on Machine Learning (ICML), 2025.
  [link]
• Jeet Mohapatra, Nima Dehmamy, Csaba Both, Subhro Das, and Tommi Jaakkola.
  Symmetry-Driven Discovery of Dynamical Variables in Molecular Simulations.
  International Conference on Machine Learning (ICML), 2025.
  [link]
• Nanye Ma, Shangyuan Tong, Haolin Jia, Hexiang Hu, Yu-Chuan Su, Mingda Zhang, Xuan Yang, Yandong Li, Tommi Jaakkola, Xuhui Jia, and Saining Xie.
  Inference-Time Scaling for Diffusion Models beyond Scaling Denoising Steps.
  In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR), 2025.
  [link]
• Peter Holderrieth, Marton Havasi, Jason Yim, Neta Shaul, Itai Gat, Tommi Jaakkola, Brian Karrer, Ricky T. Q. Chen, and Yaron Lipman.
  Generator Matching: Generative modeling with arbitrary Markov processes.
  The 13th International Conference on Learning Representations (ICLR), 2025.
  [link]
• Gabriel Corso, Vignesh Ram Somnath, Noah Getz, Regina Barzilay, Tommi Jaakkola, and Andreas Krause.
  Composing Unbalanced Flows for Flexible Docking and Relaxation.
  The 13th International Conference on Learning Representations (ICLR), 2025.
  [link]
• Hannes Stark, Bowen Jing, Tomas Geffner, Jason Yim, Tommi Jaakkola, Arash Vahdat, and Karsten Kreis.
  ProtComposer: Compositional Protein Structure Generation with 3D Ellipsoids.
  The 13th International Conference on Learning Representations (ICLR), 2025.
  [link]
• Chenyu Wang, Sharut Gupta, Xinyi Zhang, Sana Tonekaboni, Stefanie Jegelka, Tommi Jaakkola, and Caroline Uhler.
  An Information Criterion for Controlled Disentanglement of Multimodal Data.
  The 13th International Conference on Learning Representations (ICLR), 2025.
  [link]
• Mostafa Karimi, Sharmi Banerjee, Tommi Jaakkola, Bella Dubrov, Shang Shang, and Ron Benson.
  Data Distillation for extrapolative protein design through exact preference optimization.
  The 13th International Conference on Learning Representations (ICLR), 2025.
  [link]
• Chenyu Wang, Masatoshi Uehara, Yichun He, Amy Wang, Tommaso Biancalani, Avantika Lal, Tommi Jaakkola, Sergey Levine, Hanchen Wang, and Aviv Regev.
  Fine-Tuning Discrete Diffusion Models via Reward Optimization with Applications to DNA and Protein Design.
  The 13th International Conference on Learning Representations (ICLR), 2025.
  [link]
• Yujian Liu, Shiyu Chang, Tommi Jaakkola, and Yang Zhang.
  Fictitious Synthetic Data Can Improve LLM Factuality via Prerequisite Learning.
  The 13th International Conference on Learning Representations (ICLR), 2025.
  [link]
• Sulin Liu, Juno Nam, Andrew Campbell, Hannes Stark, Yilun Xu, Tommi Jaakkola, and Rafael Gomez-Bombarelli.
  Think while You Generate: Discrete Diffusion with Planned Denoising.
  The 13th International Conference on Learning Representations (ICLR), 2025.
  [link]
• Peter Holderrieth, Yilun Xu, and Tommi Jaakkola.
  Hamiltonian Score Matching and Generative Flows.
  Neural Information Processing Systems (NeurIPS), 2024.
  [link]
• Sharut Gupta, Chenyu Wang, Yifei Wang, Tommi Jaakkola, and Stefanie Jegelka.
  Symmetries In-Context: Universal Self-Supervised Learning through Contextual World Models.
  Neural Information Processing Systems (NeurIPS), 2024.
  [link]
• Xiang Fu, Andrew Rosen, Kyle Bystrom, Rui Wang, Albert Musaelian, Boris Kozinsky, Tess Smidt, and Tommi Jaakkola.
  A Recipe for Charge Density Prediction.
  Neural Information Processing Systems (NeurIPS), 2024.
  [link]
• Nima Dehmamy, Csaba Both, Jeet Mohapatra, Subhro Das, and Tommi Jaakkola.
  Neural Network Reparametrization for Accelerated Optimization in Molecular Simulations.
  Neural Information Processing Systems (NeurIPS), 2024.
  [link]
• Bowen Jing, Hannes Stark, Tommi Jaakkola, and Bonnie Berger.
  Generative Modeling of Molecular Dynamics Trajectories.
  Neural Information Processing Systems (NeurIPS), 2024.
  [link]
• Bowen Jing, Bonnie Berger, and Tommi Jaakkola.
  AlphaFold Meets Flow Matching for Generating Protein Ensembles.
  International Conference on Machine Learning (ICML), 2024.
  [link]
• Andrew Campbell, Jason Yim, Regina Barzilay, Tom Rainforth, and Tommi Jaakkola.
  Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design.
  International Conference on Machine Learning (ICML), 2024.
  [link]
• Yilun Xu, Gabriel Corso, Tommi Jaakkola, Arash Vahdat, and Karsten Kreis.
  DisCo-Diff: Enhancing Continuous Diffusion Models with Discrete Latents.
  International Conference on Machine Learning (ICML), 2024.
  [link]
• Hannes Stark, Bowen Jing, Regina Barzilay, and Tommi Jaakkola.
  Harmonic Self-Conditioned Flow Matching for joint Multi-Ligand Docking and Binding Site Design.
  International Conference on Machine Learning (ICML), 2024.
  [link]
• Hannes Stark, Bowen Jing, Chenyu Wang, Gabriel Corso, Bonnie Berger, Regina Barzilay, and Tommi Jaakkola.
  Dirichlet Flow Matching with Applications to DNA Sequence Design.
  International Conference on Machine Learning (ICML), 2024.
  [link]
• Jason Yim, Hannes Stark, Gabriel Corso, Bowen Jing, Regina Barzilay, and Tommi Jaakkola.
  Diffusion models in protein structure and docking.
  WIREs Computational Molecular Science, 14(2), pp. e1711. 2024.
  [link]
• Ryotaro Okabe, Abhijatmedhi Chotrattanapituk, Artittaya Boonkird, Nina Andrejevic, Xiang Fu, Tommi S. Jaakkola, Qichen Song, Thanh Nguyen, Nathan Drucker, Sai Mu, Yao Wang, Bolin Liao, Yongqiang Cheng, and Mingda Li.
  Virtual node graph neural network for full phonon prediction.
  Nature Computational Science, 4(7). 2024.
  [link]
• Yujian Liu, Yang Zhang, Tommi Jaakkola, and Shiyu Chang.
  Correcting Diffusion Generation through Resampling.
  Computer Vision and Pattern Recognition (CVPR), 2024.
  [link]
• Gabriele Corso, Hannes Stark, Stefanie Jegelka, Tommi Jaakkola, and Regina Barzilay.
  Graph Neural Networks.
  Nature Reviews Methods Primers, 4(17). 2024.
  [link]
• Xiang Fu, Tian Xie, Andrew S. Rosen, Tommi Jaakkola, and Jake Smith.
  MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design.
  The 12th International Conference on Learning Representations (ICLR), 2024.
  [link]
• Gabriele Corso, Yilun Xu, Valentin De Bortoli, Regina Barzilay, and Tommi Jaakkola.
  Particle Guidance: non-I.I.D. Diverse Sampling with Diffusion Models.
  The 12th International Conference on Learning Representations (ICLR), 2024.
  [link]
• Gabriele Corso, Arthur Deng, Benjamin Fry, Nicholas Polizzi, Regina Barzilay, and Tommi Jaakkola.
  Deep Confident Steps to New Pockets: Strategies for Docking Generalization.
  The 12th International Conference on Learning Representations (ICLR), 2024.
  [link]
• Chenyu Wang, Sharut Gupta, Caroline Uhler, and Tommi Jaakkola.
  Removing Biases from Molecular Representations via Information Maximization.
  The 12th International Conference on Learning Representations (ICLR), 2024.
  [link]
• Bowen Jing, Tommi Jaakkola, and Bonnie Berger.
  Learning Scalar Fields for Molecular Docking with Fast Fourier Transforms.
  The 12th International Conference on Learning Representations (ICLR), 2024.
  [link]
• Andrew Kirjner, Jason Yim, Raman Samusevich, Shahar Bracha, Tommi Jaakkola, Regina Barzilay, and Ila Fiete.
  Improving protein optimization with smoothed fitness landscapes.
  The 12th International Conference on Learning Representations (ICLR), 2024.
  [link]
• Victor Quach, Adam Fisch, Tal Schuster, Adam Yala, Jae Ho Sohn, Tommi S. Jaakkola, and Regina Barzilay.
  Conformal Language Modeling.
  The 12th International Conference on Learning Representations (ICLR), 2024.
  [link]
• Brent A. Koscher, Richard B. Canty, Matthew A. McDonald, Kevin P. Greenman, Charles J. McGill, Camille L. Bilodeau, Wengong Jin, Haoyang Wu, Florence H. Vermeire, Brooke Jin, Travis Hart, Timothy Kulesza, Shih-Cheng Li, Tommi S. Jaakkola, Regina Barzilay, Rafael Gomez-Bombarelli, William H. Green, and and Klavs F. Jensen.
  Autonomous, multiproperty-driven molecular discovery: From predictions to measurements and back.
  Science, 382. 2023.
  [link]
• Timur Garipov, Sebastiaan De Peuter, Ge Yang, Vikas Garg, Samuel Kaski, and Tommi Jaakkola.
  Compositional Sculpting of Iterative Generative Processes.
  Neural Information Processing Systems (NeurIPS), 2023.
  [link]
• Yilun Xu, Mingyang Deng, Xiang Cheng, Yonglong Tian, Ziming Liu, and Tommi Jaakkola.
  Restart Sampling for Improving Generative Processes.
  Neural Information Processing Systems (NeurIPS), 2023.
  [link]
• Anurag Ajay, Seungwook Han, Yilun Du, Shuang Li, Abhi Gupta, Tommi Jaakkola, Josh Tenenbaum, Leslie Kaelbling, Akash Srivastava, and Pulkit Agrawal.
  Hierarchical Planning with Foundation Models.
  Neural Information Processing Systems (NeurIPS), 2023.
  [link]
• Xiang Fu, Tian Xie, Nathan J. Rebello, Bradley D. Olsen, and Tommi Jaakkola.
  Simulate Time-integrated Coarse-grained Molecular Dynamics with Multi-scale Graph Networks.
  Transactions on Machine Learning Research (TMLR), 2023.
  [link]
• Joseph L. Watson, David Juergens, Nathaniel R. Bennett, Brian L. Trippe, Jason Yim, Helen E. Eisenach, Woody Ahern, Andrew J. Borst, Robert J. Ragotte, Lukas F. Milles, Basile I. M. Wicky, Nikita Hanikel, Samuel J. Pellock, Alexis Courbet, William Sheffler, Jue Wang, Preetham Venkatesh, Isaac Sappington, Susana Vazquez Torres, Anna Lauko, Valentin De Bortoli, Emile Mathieu, Sergey Ovchinnikov, Regina Barzilay, Tommi S. Jaakkola, Frank DiMaio, Minkyung Baek, and David Baker.
  De novo design of protein structure and function with RFdiffusion.
  Nature, 620, pp. 1089–1100. 2023.
  [link]
• Gary Liu, Denise B. Catacutan, Khushi Rathod, Kyle Swanson, Wengong Jin, Jody C. Mohammed, Anush Chiappino-Pepe, Saad A. Syed, Meghan Fragis, Kenneth Rachwalski, Jakob Magolan, Michael G. Surette, Brian K. Coombes, Tommi Jaakkola, Regina Barzilay, James J. Collins, and Jonathan M. Stokes.
  Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumannii.
  Nature Chemical Biology, 2023.
  [link] [pdf]
• Yilun Xu, Ziming Liu, Yonglong Tian, Shangyuan Tong, Max Tegmark, and Tommi Jaakkola.
  PFGM++: Unlocking the Potential of Physics-Inspired Generative Models.
  International Conference on Machine Learning (ICML), 2023.
  [link]
• Jason Yim, Brian L. Trippe, Valentin De Bortoli, Emile Mathieu, Arnaud Doucet, Regina Barzilay, and Tommi Jaakkola.
  SE(3) diffusion model with application to protein backbone generation.
  International Conference on Machine Learning (ICML), 2023.
  [link]
• Guanhua Zhang, Jiabao Ji, Yang Zhang, Mo Yu, Tommi Jaakkola, and Shiyu Chang.
  Towards Coherent Image Inpainting Using Denoising Diffusion Implicit Models.
  International Conference on Machine Learning (ICML), 2023.
  [link]
• Xiang Fu, Zhenghao Wu, Wujie Wang, Tian Xie, Sinan Keten, Rafael Gomez-Bombarelli, and Tommi Jaakkola.
  Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations.
  Transactions on Machine Learning Research (TMLR), 2023.
  [link]
• Mohamed Amine Ketata, Cedrik Laue, Ruslan Mammadov, Hannes Stark, Menghua Wu, Gabriele Corso, Celine Marquet, Regina Barzilay, and Tommi S. Jaakkola.
  DiffDock-PP: Rigid Protein-Protein Docking with Diffusion Models.
  Machine Learning for Drug Discovery (ICLR workshop), 2023.
  [link]
• Bowen Jing, Ezra Erives, Peter Pao-Huang, Gabriele Corso, Bonnie Berger, and Tommi Jaakkola.
  EigenFold: Generative Protein Structure Prediction with Diffusion Models.
  Machine Learning for Drug Discovery Workshop (ICLR workshop), 2023.
  [link]
• Brian L. Trippe, Jason Yim, Doug Tischer, David Baker, Tamara Broderick, Regina Barzilay, and Tommi Jaakkola.
  Diffusion Probabilistic Modeling of Protein Backbones in 3D for the motif-scaffolding problem.
  The 11th International Conference on Learning Representations (ICLR), 2023.
  [link]
• Gabriele Corso, Hannes Stärk, Bowen Jing, Regina Barzilay, and Tommi Jaakkola.
  DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking.
  The 11th International Conference on Learning Representations (ICLR), 2023.
  [link]
• Yilun Xu, Shangyuan Tong, and Tommi Jaakkola.
  Stable Target Field for Reduced Variance Score Estimation.
  The 11th International Conference on Learning Representations (ICLR), 2023.
  [link]
• Anurag Ajay, Yilun Du, Abhi Gupta, Joshua Tenenbaum, Tommi Jaakkola, and Pulkit Agrawal.
  Is Conditional Generative Modeling all you need for Decision Making?.
  The 11th International Conference on Learning Representations (ICLR), 2023.
  [link]
• Bracha Laufer-Goldshtein, Adam Fisch, Regina Barzilay, and Tommi Jaakkola.
  Efficiently Controlling Multiple Risks with Pareto Testing.
  The 11th International Conference on Learning Representations (ICLR), 2023.
  [link]
• Han Zhao, Chen Dan, Bryon Aragam, Tommi S. Jaakkola, Geoffrey J. Gordon, and Pradeep Ravikumar.
  Fundamental Limits and Tradeoffs in Invariant Representation Learning.
  Journal of Machine Learning Research, 23(340), pp. 1–49. 2022.
  [link]
• Adam Fisch, Tommi Jaakkola, and Regina Barzilay.
  Calibrated Selective Classification.
  Transactions on Machine Learning Research, 2022.
  [link]
• Bowen Jing, Gabriele Corso, Jeffrey Chang, Regina Barzilay, and Tommi Jaakkola.
  Torsional Diffusion for Molecular Conformer Generation.
  Neural Information Processing Systems (NeurIPS), 2022.
  [link]
• Yilun Xu, Ziming Liu, Max Tegmark, and Tommi Jaakkola.
  Poisson Flow Generative Models.
  Neural Information Processing Systems (NeurIPS), 2022.
  [link]
• Felix Wong, Aarti Krishnan, Erica J Zheng, Hannes Stark, Abigail L Manson, Ashlee M Earl, Tommi Jaakkola, and James J Collins.
  Benchmarking AlphaFold-enabled molecular docking predictions for antibiotic discovery in Molecular Systems Biology.
  Molecular Systems Biology, 18(9). 2022.
  [link]
• Bowen Jing, Gabriele Corso, Renato Berlinghieri, and Tommi Jaakkola.
  Subspace Diffusion Generative Models.
  European Conference on Computer Vision (ECCV), 2022.
  [link]
• Hannes Stark, Octavian-Eugen Ganea, Lagnajit Pattanaik, Regina Barzilay, and Tommi Jaakkola.
  EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction.
  International Conference on Machine Learning (ICML), 2022.
  [link]
• Wengong Jin, Regina Barzilay, and Tommi Jaakkola.
  Antibody-Antigen Interface Design via Hierarchical Structure Refinement.
  International Conference on Machine Learning (ICML), 2022.
  [link]
• Adam Fisch, Tal Schuster, Tommi Jaakkola, and Regina Barzilay.
  Conformal Prediction Sets with Limited False Positives.
  International Conference on Machine Learning (ICML), 2022.
  [link]
• Camille Bilodeau, Wengong Jin, Tommi Jaakkola, Regina Barzilay, and Klavs F. Jensen.
  Generative models for molecular discovery: Recent advances and challenges.
  WIREs Computational Molecular Science, 2022.
  [link]
• Tian Xie, Xiang Fu, Octavian Ganea, Regina Barzilay, and Tommi Jaakkola.
  Crystal Diffusion Variational Autoencoder for Periodic Material Generation.
  The Tenth International Conference on Learning Representations (ICLR), 2022.
  [pdf]
• Wengong Jin, Jeremy Wohlwend, Regina Barzilay, and Tommi Jaakkola.
  Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design.
  The Tenth International Conference on Learning Representations (ICLR), 2022.
  [pdf]
• Yilun Xu, Hao He, Tianxiao Shen, and Tommi Jaakkola.
  Controlling Directions Orthogonal to a Classifier.
  The Tenth International Conference on Learning Representations (ICLR), 2022.
  [pdf]
• Shangyuan Tong, Timur Garipov, Yang Zhang, Shiyu Chang, and Tommi Jaakkola.
  Adversarial support alignment.
  The Tenth International Conference on Learning Representations (ICLR), 2022.
  [pdf]
• Octavian-Eugen Ganea, Xinyuan Huang, Charlotte Bunne, Yatao Bian, Regina Barzilay, Tommi Jaakkola, and Andreas Krause.
  Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking.
  The Tenth International Conference on Learning Representations (ICLR), 2022.
  [pdf]
• Octavian-Eugen Ganea, Lagnajit Pattanaik, Connor W. Coley, Regina Barzilay, Klavs F. Jensen, William H. Green, and Tommi Jaakkola.
  GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles.
  Neural Information Processing Systems (NeurIPS), 2021.
  [link]
• Mo Yu, Yang Zhang, Shiyu Chang, and Tommi S. Jaakkola.
  Understanding Interlocking Dynamics of Cooperative Rationalization.
  Neural Information Processing Systems (NeurIPS), 2021.
  [link]
• Wengong Jin, Jonathan M. Stokes, Richard T. Eastman, Zina Itkin, Alexey V. Zakharov, James J. Collins, Tommi S. Jaakkola, and Regina Barzilay.
  Deep learning identifies synergistic drug combinations for treating COVID-19.
  Proceedings of the National Academy of Sciences of the USA (PNAS), 118(39). 2021.
  [link]
• Tal Schuster, Adam Fisch, Tommi Jaakkola, and Regina Barzilay.
  Consistent Accelerated Inference via Confident Adaptive Transformers.
  Empirical Methods in Natural Language Processing (EMNLP), 2021.
  [link]
• Xiang Fu, Ge Yang, Pulkit Agrawal, and Tommi Jaakkola.
  Learning Task Informed Abstractions.
  International Conference on Machine Learning (ICML), 2021.
  [link]
• Adam Fisch, Tal Schuster, Tommi Jaakkola, and Regina Barzilay.
  Few-Shot Conformal Prediction with Auxiliary Tasks.
  International Conference on Machine Learning (ICML), 2021.
  [link]
• Peiyuan Liao, Han Zhao, Keyulu Xu, Tommi Jaakkola, Geoffrey Gordon, Stefanie Jegelka, and Ruslan Salakhutdinov.
  Information Obfuscation of Graph Neural Networks.
  International Conference on Machine Learning (ICML), 2021.
  [link]
• Karren Yang, Samuel Goldman, Wengong Jin, Alex Lu, Regina Barzilay, Tommi Jaakkola, and Caroline Uhler.
  Improved conditional flow models for molecule to image synthesis.
  Computer Vision and Pattern Recognition (CVPR), 2021.
  [link]
• Adam Fisch, Tal Schuster, Tommi Jaakkola, and Regina Barzilay.
  Efficient Conformal Prediction via Cascaded Inference with Expanded Admission.
  The Ninth International Conference on Learning Representations (ICLR), 2021.
  [link]
• Wengong Jin, Regina Barzilay, and Tommi Jaakkola.
  Discovering Synergistic Drug Combinations for COVID with Biological Bottleneck Models.
  NeurIPS Machine Learning for Molecules Workshop, 2020.
  [link]
• Tianxiao Shen, Victor Quach, Regina Barzilay, and Tommi Jaakkola.
  Blank Language Models.
  Empirical Methods in Natural Language Processing (EMNLP), 2020.
  [link]
• Vikas K. Garg and Tommi Jaakkola.
  Predicting deliberative outcomes.
  International Conference on Machine Learning (ICML), 2020.
  [pdf]
• Shiyu Chang, Yang Zhang, Mo Yu, and Tommi Jaakkola.
  Invariant Rationalization.
  International Conference on Machine Learning (ICML), 2020.
  [link]
• Tianxiao Shen, Jonas Mueller, Regina Barzilay, and Tommi Jaakkola.
  Educating Text Autoencoders: Latent Representation Guidance via Denoising.
  International Conference on Machine Learning (ICML), 2020.
  [link]
• Wengong Jin, Regina Barzilay, and Tommi Jaakkola.
  Hierarchical Generation of Molecular Graphs using Structural Motifs.
  International Conference on Machine Learning (ICML), 2020.
  [pdf]
• Wengong Jin, Regina Barzilay, and Tommi Jaakkola.
  Multi-Objective Molecule Generation using Interpretable Substructures.
  International Conference on Machine Learning (ICML), 2020.
  [pdf]
• Vikas K. Garg, Stefanie Jegelka, and Tommi Jaakkola.
  Generalization and Representational Limits of Graph Neural Networks.
  International Conference on Machine Learning (ICML), 2020.
  [pdf]
• Kevin Yang, Kyle Swanson, Wengong Jin, Regina Barzilay, and Tommi Jaakkola.
  Improving Molecular Design by Stochastic Iterative Target Augmentation.
  International Conference on Machine Learning (ICML), 2020.
  [link]
• Jonathan M. Stokes, Kevin Yang, Kyle Swanson, Wengong Jin, Andres Cubillos-Ruiz, Nina M. Donghia, Craig R. MacNair, Shawn French, Lindsey A. Carfrae, Zohar Bloom-Ackerman, Victoria M. Tran, Anush Chiappino-Pepe, Ahmed H. Badran, Ian W. Andrews, Emma J. Chory, George M. Church, Eric D. Brown, Tommi S. Jaakkola, Regina Barzilay, and James J. Collins.
  A Deep Learning Approach to Antibiotic Discovery.
  Cell, 180(4). 2020.
  [pdf]
• David Alvarez Melis, Youssef Mroueh, and Tommi S. Jaakkola.
  Unsupervised Hierarchy Matching with Optimal Transport over Hyperbolic spaces.
  Artificial Intelligence and Statistics (AISTATS), 2020.
  [link]
• Chen-Yu Hsu, Abbas Zeitoun, Guang-He Lee, Dina Katabi, and Tommi Jaakkola.
  Self-Supervised Learning of Appliance Usage.
  International Conference on Learning Representations (ICLR), 2020.
  [pdf]
• Guang-He Lee and Tommi Jaakkola.
  Oblique Decision Trees from Derivatives of ReLU Networks.
  International Conference on Learning Representations (ICLR), 2020.
  [pdf]
• Shiyu Chang, Yang Zhang, Mo Yu, and Tommi Jaakkola.
  A Game Theoretic Approach to Class-wise Selective Rationalization.
  Neural Information Processing Systems (NeurIPS), 2019.
  [pdf]
• Vikas K. Garg and Tommi Jaakkola.
  Solving graph compression via optimal transport.
  Neural Information Processing Systems (NeurIPS), 2019.
  [pdf]
• Guang-He Lee, Yang Yuan, Shiyu Chang, and Tommi S. Jaakkola.
  Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifiers.
  Neural Information Processing Systems (NeurIPS), 2019.
  [pdf]
• John Ingraham, Vikas K. Garg, Regina Barzilay, and Tommi Jaakkola.
  Generative Models for Graph-Based Protein Design.
  Neural Information Processing Systems (NeurIPS), 2019.
  [pdf]
• Guy Loberbom, Andreea Gane, Tommi Jaakkola, and Tamir Hazan.
  Direct Optimization through argmax for Discrete Variational Auto-Encoder.
  Neural Information Processing Systems (NeurIPS), 2019.
  [pdf]
• David Alvarez Melis, Youssef Mroueh, and Tommi Jaakkola.
  Unsupervised Hierarchy Matching with Optimal Transport over Hyperbolic spaces.
  Optimal Transport and Machine Learning (NeurIPS OTML workshop), 2019.
  [link]
• Mo Yu, Shiyu Chang, Yang Zhang, and Tommi Jaakkola.
  Rethinking Cooperative Rationalization: Introspective Extraction and Complement Control.
  Empirical Methods in Natural Language Processing (EMNLP), 2019.
  [pdf]
• Kevin Yang, Kyle Swanson, Wengong Jin, Connor Coley, Philipp Eiden, Hua Gao, Angel Guzman-Perez, Timothy Hopper, Brian Kelley, Miriam Mathea, Andrew Palmer, Volker Settels, Tommi Jaakkola, Klavs Jensen, and Regina Barzilay.
  Analyzing Learned Molecular Representations for Property Prediction.
  Journal of Chemical Information and Modeling, 2019.
  [link]
• Benson Chen, Regina Barzilay, and Tommi Jaakkola.
  Path-Augmented Graph Transformer Network.
  Learning and Reasoning with Graph-Structured Representations (ICML workshop), 2019.
  [link]
• Guang-He Lee, Wengong Jin, David Alvarez Melis, and Tommi S. Jaakkola.
  Functional Transparency for Structured Data: a Game-Theoretic Approach.
  International Conference on Machine Learning (ICML), 2019.
  [link]
• Tamir Hazan, Francesco Orabona, Anand D. Sarwate, Subhransu Maji, and Tommi S. Jaakkola.
  High Dimensional Inference with Random Maximum A-Posteriori Perturbations.
  IEEE Transactions on Information Theory, 65(10). 2019.
  [link]
• John Ingraham, Vikas K. Garg, Regina Barzilay, and Tommi Jaakkola.
  Generative Models for Graph-Based Protein Design.
  Deep Generative Models for Highly Structured Data (ICLR workshop), 2019.
  [pdf]
• Connor W. Coley, Wengong Jin, Luke Rogers, Timothy F. Jamison, Tommi S. Jaakkola, William H. Green, Regina Barzilay, and Klavs F. Jensen.
  A Graph-Convolutional Neural Network Model for the Prediction of Chemical Reactivity.
  Chemical Science, 10(2), pp. 370–377. 2019.
  [link]
• Guang-He Lee, David Alvarez Melis, and Tommi S. Jaakkola.
  Towards Robust, Locally Linear Deep Networks.
  International Conference on Learning Representations (ICLR), 2019.
  [pdf]
• Wengong Jin, Kevin Yang, Regina Barzilay, and Tommi Jaakkola.
  Learning Multimodal Graph-to-Graph Translation for Molecule Optimization.
  International Conference on Learning Representations (ICLR), 2019.
  [link]
• P. Malalur and T. Jaakkola.
  Alignment Based Matching Networks for One-Shot Classification and Open-Set Recognition.
  arXiv, 2019.
  [link]
• David Alvarez Melis, Stefanie Jegelka, and Tommi S. Jaakkola.
  Towards Optimal Transport with Global Invariances.
  Artificial Intelligence and Statistics (AISTATS), 2019.
  [pdf]
• Karthik Narasimhan, Regina Barzilay, and Tommi Jaakkola.
  Grounding Language for Transfer in Deep Reinforcement Learning.
  Journal of Artificial Intelligence Research, 63, pp. 849-874. 2018.
  [pdf]
• Hao Wang, Chengzhi Mao, Hao He, Mingmin Zhao, Tommi S. Jaakkola, and Dina Katabi.
  Bidirectional Inference Networks with Application to Health Profiling.
  AAAI Conference on Artificial Intelligence (AAAI), 2018.
  [link]
• David Alvarez Melis and Tommi S. Jaakkola.
  Towards Robust Interpretability with Self-Explaining Neural Networks.
  Advances in Neural Information Processing Systems (NeurIPS), 2018.
  [pdf]
• David Alvarez Melis and Tommi S. Jaakkola.
  Gromov-Wasserstein Alignment of Word Embedding Spaces.
  Empirical Methods in Natural Language Processing (EMNLP), 2018.
  [pdf]
• Wengong Jin, Regina Barzilay, and Tommi Jaakkola.
  Junction Tree Variational Autoencoder for Molecular Graph Generation.
  International Conference on Machine Learning (ICML), 2018.
  [link]
• Guang-He Lee, David Alvarez Melis, and Tommi S. Jaakkola.
  Game theoretic interpretability for temporal modeling.
  Fairness, Accountability, and Transparency in Machine Learning (ICML workshop), 2018.
  [link]
• David Alvarez Melis and Tommi S. Jaakkola.
  On the Robustness of Interpretability Methods.
  Human Interpretability in Machine Learning (ICML workshop), 2018.
  [link]
• David Alvarez Melis, Tommi S. Jaakkola, and Stefanie Jegelka.
  Structured Optimal Transport.
  Artificial Intelligence and Statistics (AISTATS), 2018.
  [pdf]
• Luke B. Hewitt, Maxwell I. Nye, Andreea Gane, Tommi Jaakkola, and Joshua B. Tenenbaum.
  The Variational Homoencoder: Learning to learn high capacity generative models from few examples.
  Uncertainty in Artificial Intelligence (UAI), 2018.
  [link]
• Vikas K. Garg and Tommi Jaakkola.
  Local Aggregative Games.
  Advances in Neural Information Processing Systems (NIPS), 2017.
  [pdf]
• Wengong Jin, Connor W. Coley, Regina Barzilay, and Tommi Jaakkola.
  Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network.
  Advances in Neural Information Processing Systems (NIPS), 2017.
  [link]
• Tianxiao Shen, Tao Lei, Regina Barzilay, and Tommi Jaakkola.
  Style Transfer from Non-Parallel Text by Cross-Alignment.
  Advances in Neural Information Processing Systems (NIPS), 2017.
  [link]
• Yuan Zhang, Regina Barzilay, and Tommi Jaakkola.
  Aspect-augmented Adversarial Networks for Domain Adaptation.
  Transactions of the Association for Computational Linguistics (TACL), 2017.
  [pdf]
• David Alvarez Melis and Tommi S. Jaakkola.
  A causal framework for explaining the predictions of black-box sequence-to-sequence models.
  Empirical Methods in Natural Language Processing (EMNLP), 2017.
  [pdf]
• Tao Lei, Wengong Jin, Regina Barzilay, and Tommi Jaakkola.
  Deriving Neural Architectures from Sequence and Graph Kernels.
  International Conference on Machine Learning (ICML), 2017.
  [pdf]
• Jonas Mueller, David Gifford, and Tommi Jaakkola.
  Sequence to Better Sequence: Continuous Revision of Combinatorial Structures.
  International Conference on Machine Learning (ICML), 2017.
  [pdf]
• Mingmin Zhao, Shichao Yue, Dina Katabi, Tommi S. Jaakkola, and Matt T. Bianchi.
  Learning Sleep Stages from Radio Signals: A Conditional Adversarial Architecture.
  International Conference on Machine Learning (ICML), 2017.
  [pdf]
• Jonas Mueller, Tommi Jaakkola, and David Gifford.
  Modeling Persistent Trends in Distributions.
  Journal of the American Statistical Association, 2017.
  [pdf]
• Connor W. Coley, Regina Barzilay, Tommi S. Jaakkola, William H. Green, and Klavs F. Jensen.
  Prediction of Organic Reaction Outcomes Using Machine Learning.
  ACS Central Science, 2017.
  [pdf]
• Connor W. Coley, Regina Barzilay, William H. Green, Tommi S. Jaakkola, and Klavs F. Jensen.
  Convolutional embedding of attributed molecular graphs for physical property prediction.
  Journal of Chemical Information and Modeling, 57(8), pp. 1757-1772. 2017.
  [link]
• David Alvarez-Melis and Tommi S. Jaakkola.
  Tree Structured Decoding with Doubly Recurrent Neural Networks.
  International Conference on Learning Representations (ICLR), 2017.
  [pdf]
• Jonas Mueller, David Reshef, George Du, and Tommi Jaakkola.
  Learning Optimal Interventions.
  Artificial Intelligence and Statistics (AISTATS), 2017.
  [pdf]
• Vikas K. Garg and Tommi Jaakkola.
  Learning Tree Structured Potential Games.
  Advances in Neural Information Processing Systems (NIPS), 2016.
  [pdf]
• Tao Lei, Regina Barzilay, and Tommi Jaakkola.
  Rationalizing Neural Predictions.
  Empirical Methods in Natural Language Processing (EMNLP), 2016.
  [pdf]
• Youyang Gu, Regina Barzilay, and Tommi S. Jaakkola.
  Food Adulteration Detection using Neural Networks.
  Empirical Methods in Natural Language Processing (EMNLP), 2016.
• Jean Honorio and Tommi Jaakkola.
  Structured Prediction: From Gaussian Perturbations to Linear-Time Principled Algorithms.
  Uncertainty in Artificial Intelligence (UAI), 2016.
  [pdf]
• Tatsunori B. Hashimoto, David Alvarez Melis, and Tommi S. Jaakkola.
  Word embeddings as metric recovery in semantic spaces.
  Transactions of the Association for Computational Linguistics (TACL), 4. 2016.
  [pdf]
• Tatsunori B. Hashimoto, Tommi S. Jaakkola, and David K. Gifford.
  Learning population-level diffusions with generative RNNs.
  International Conference on Machine Learning (ICML), 2016.
  [pdf]
• Yuan Zhang, David Gaddy, Regina Barzilay, and Tommi Jaakkola.
  Ten Pairs to Tag – Multilingual POS Tagging via Coarse Mapping between Embeddings.
  The 15th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL), 2016.
  [pdf]
• Tao Lei, Hrishikesh Joshi, Regina Barzilay, Tommi Jaakkola, Katerina Tymoshenko, Alessandro Moschitti, and Lluis Marquez.
  Semi-supervised Question Retrieval with Gated Convolutions.
  The 15th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL), 2016.
  [pdf]
• Vikas K. Garg, Cynthia Rudin, and Tommi Jaakkola.
  CRAFT: ClusteR-specific Assorted Feature selecTion.
  Artificial Intelligence and Statistics (AISTATS), 2016.
  [pdf]
• Tatsunori B. Hashimoto, David Alvarez-Melis, and Tommi S. Jaakkola.
  Word, graph and manifold embedding from Markov processes.
  arXiv:1509.05808, 2015.
  [link]
• Tatsunori B. Hashimoto, Yi Sun, and Tommi S. Jaakkola.
  From random walks to distances on unweighted graphs.
  Advances in Neural Information Processing Systems (NIPS), 2015.
  [pdf]
• Jonas Mueller and Tommi Jaakkola.
  Principal Differences Analysis: Interpretable Characterization of Differences between Distributions.
  Advances in Neural Information Processing Systems (NIPS), 2015.
  [pdf]
• Tao Lei, Regina Barzilay, and Tommi Jaakkola.
  Molding CNNs for Text: Non-linear, Non-consecutive Convolutions.
  Empirical Methods in Natural Language Processing (EMNLP), 2015.
  [pdf] [link]
• Karthik Narasimhan, Regina Barzilay, and Tommi Jaakkola.
  An Unsupervised Method for Uncovering Morphological Chains.
  Transactions of the Association for Computational Linguistics, 3, pp. 157–167. 2015.
  [pdf] [link]
• Tatsunori B. Hashimoto, Yi Sun, and Tommi S. Jaakkola.
  Metric recovery from directed unweighted graphs.
  Artificial Intelligence and Statistics (AISTATS), 2015.
  [pdf]
• Yu Xin and Tommi Jaakkola.
  Controlling privacy in recommender systems.
  Advances in Neural Information Processing Systems (NIPS), 2014.
  [pdf]
• Yuan Zhang, Tao Lei, Regina Barzilay, and Tommi Jaakkola.
  Greed is Good if Randomized: New Inference for Dependency Parsing.
  Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014.
  [pdf]
• Tao Lei, Yu Xin, Yuan Zhang, Regina Barzilay, and Tommi Jaakkola.
  Low-Rank Tensors for Scoring Dependency Structures.
  Association for Computational Linguistics (ACL), 2014.
  [pdf]
• Yuan Zhang, Tao Lei, Regina Barzilay, Tommi Jaakkola, and Amir Globerson.
  Steps to Excellence: Simple Inference with Refined Scoring of Dependency Trees.
  Association for Computational Linguistics (ACL), 2014.
  [pdf]
• Jean Honorio and Tommi Jaakkola.
  A Unified Framework for Consistency of Regularized Loss Minimizers.
  Proceedings of the 31th International Conference on Machine Learning (ICML), 2014.
  [pdf]
• Andreea Gane, Tamir Hazan, and Tommi Jaakkola.
  Learning with Maximum A-Posteriori Perturbation Models.
  Artificial Intelligence and Statistics (AISTATS), 2014.
  [pdf]
• Subhransu Maji, Tamir Hazan, and Tommi Jaakkola.
  Active Boundary Annotation using Random MAP Perturbations.
  Artificial Intelligence and Statistics (AISTATS), 2014.
  [pdf]
• Jean Honorio and Tommi Jaakkola.
  Tight Bounds for the Expected Risk of Linear Classifiers and PAC-Bayes Finite-Sample Guarantees.
  Artificial Intelligence and Statistics (AISTATS), 2014.
  [pdf]
• Francesco Orabona, Tamir Hazan, Anand D. Sarwate, and Tommi Jaakkola.
  On Measure Concentration of Random Maximum A-Posteriori Perturbations.
  Proceedings of the 31th International Conference on Machine Learning (ICML), 2014.
• Ofer Meshi, Tommi Jaakkola, and Amir Globerson.
  Smoothed Coordinate Descent for MAP Inference.
  Advanced Structured Prediction, 2014.
  [pdf]
• Richard I Sherwood, Tatsunori Hashimoto, Charles W O’Donnell, Sophia Lewis, Amira A Barkal, John Peter van Hoff, Vivek Karun, Tommi Jaakkola, and David K Gifford.
  Discovery of directional and nondirectional pioneer transcription factors by modeling Dnase profile magnitude and shape.
  Nature Biotechnology, 32(2), pp. 171–178. 2014.
  [pdf]
• Tamir Hazan, Subhransu Maji, Joseph Keshet, and Tommi Jaakkola.
  Learning Efficient Random Maximum A-Posteriori Predictors with Non-Decomposable Loss Functions.
  Advances of Neural Information Processing Systems (NIPS), 2013.
  [pdf]
• Tamir Hazan, Subhransu Maji, and Tommi Jaakkola.
  On Sampling from the Gibbs distribution with Random Maximum A Posteriori Perturbations.
  Advances of Neural Information Processing Systems (NIPS), 2013.
  [pdf]
• Jean Honorio and Tommi Jaakkola.
  Two-Sided Exponential Concentration Bounds for Bayes Error Rate and Shannon Entropy.
  Proceedings of the 30th International Conference on Machine Learning (ICML), 2013.
  [pdf]
• Jean Honorio and Tommi Jaakkola.
  Inverse Covariance Estimation for High-Dimensional Data in Linear Time and Space: Spectral Methods for Riccati and Sparse Models.
  Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence (UAI), 2013.
  [pdf]
• Tamir Hazan and Tommi Jaakkola.
  On the Partition Function and Random Maximum A-Posteriori Perturbations.
  Proceedings of the 29th International Conference on Machine Learning (ICML), 2012.
  [pdf]
• Ofer Meshi, Tommi Jaakkola, and Amir Globerson.
  Convergence Rate Analysis of MAP Coordinate Minimization Algorithms.
  Advances in Neural Information Processing Systems (NIPS), 2012.
• Tatsunori Hashimoto, Tommi Jaakkola, Richard Sherwood, Esteban O Mazzoni, Hynek Wichterle, and David Gifford.
  Lineage based identification of cellular states and expression programs.
  Proceedings of the 20th Annual International Conference on Intelligent Systems for Molecular Biology (ISMB), 2012.
  [pdf]
• J. Zico Kolter and Tommi Jaakkola.
  Approximate Inference in Additive Factorial HMMs with Application to Energy Disaggregation.
  Proceedings of the 15th International Conference on Artificial Intelligence and Statistics (AISTATS), 22, pp. 1472-1482. 2012.
  [pdf]
• Yu Xin and Tommi Jaakkola.
  Primal-Dual methods for sparse constrained matrix completion.
  Proceedings of the 15th International Conference on Artificial Intelligence and Statistics (AISTATS), 22, pp. 1323-1331. 2012.
  [pdf]
• David Sontag, Amir Globerson, and Tommi Jaakkola.
  Introduction to dual decomposition for inference.
  S. Sra, S. Nowozin, and S. Wright, Eds., Optimization for Machine Learning, 2010.
  [pdf]
• Yuchun Guo, Georgios Papachristoudis, Robert C. Altshuler, Georg K. Gerber, Tommi S. Jaakkola, David K. Gifford, and Shaun Mahony.
  Discovering homotypic binding events at high spatial resolution.
  Bioinformatics, 2010.
  [link]
• David Sontag, Ofer Meshi, Tommi Jaakkola, and Amir Globerson.
  More data means less inference: A pseudo-max approach to structured learning.
  Advances in Neural Information Processing Systems (NIPS), 2010.
  [pdf]
• Alexander M. Rush, David Sontag, Michael Collins, and Tommi Jaakkola.
  On Dual Decomposition and Linear Programming Relaxations for Natural Language Processing.
  Conference on Empirical Methods in Natural Language Processing (EMNLP), 2010.
  [pdf]
• Terry Koo, Alexander M. Rush, Michael Collins, Tommi Jaakkola, and David Sontag.
  Dual Decomposition for Parsing with Non-Projective Head Automata.
  Conference on Empirical Methods in Natural Language Processing (EMNLP), 2010.
  [pdf]
• Einat Minkov, Ben Charrow, Jonathan Ledlie, Seth Teller, and Tommi Jaakkola.
  Collaborative Future Event Recommendation.
  International Conference on Information and Knowledge Management, 2010.
  [pdf]
• Ofer Meshi, David Sontag, Tommi Jaakkola, and Amir Globerson.
  Learning Efficiently with Approximate Inference via Dual Losses.
  Proceedings of the 27th International Conference on Machine Learning (ICML), 2010.
  [pdf]
• Tommi Jaakkola, David Sontag, Amir Globerson, and Marina Meila.
  Learning Bayesian Network Structure using LP Relaxations.
  Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS), 2010.
  [pdf] [slides]
• David Sontag and Tommi Jaakkola.
  Tree Block Coordinate Descent for MAP in Graphical Models.
  Proceedings of the 12th International Conference on Artificial Intelligence and Statistics (AISTATS), 2009.
  [pdf]
• David Sontag, Amir Globerson, and Tommi Jaakkola.
  Clusters and Coarse Partitions in LP Relaxations.
  Advances in Neural Information Processing Systems (NIPS), 2008.
  [pdf]
• David Sontag, Talya Meltzer, Amir Globerson, Tommi Jaakkola, and Yair Weiss.
  Tightening LP Relaxations for MAP using Message Passing.
  Proceedings of the 24rd Conference on Uncertainty in Artificial Intelligence (AISTATS), 2008.
  [pdf]
• David Sontag and Tommi Jaakkola.
  New Outer Bounds on the Marginal Polytope.
  Advances in Neural Information Processing Systems (NIPS), 2007.
  [pdf]
• Amir Globerson and Tommi Jaakkola.
  Fixing Max-Product: Convergent Message Passing Algorithms for MAP LP-Relaxations.
  Advances in Neural Information Processing Systems (NIPS), 2007.
  [pdf]
• Georg K. Gerber, Robin D. Dowell, Tommi S. Jaakkola, and David K. Gifford.
  Automated discovery of functional generality of human gene expression programs.
  PloS Computational biology, 2007.
  [link]
• Amir Globerson and Tommi Jaakkola.
  Convergent Propagation Algorithms via Oriented Trees.
  Proceedings of the 23rd Conference on Uncertainty in Artificial Intelligence (UAI), 2007.
  [pdf]
• David Sontag and Tommi Jaakkola.
  On Iteratively Constraining the Marginal Polytope for Approximate Inference and MAP.
  2007.
  [pdf]
• Amir Globerson and Tommi Jaakkola.
  Approximate inference using conditional entropy decompositions.
  Proceedings of the 11th International Conference on Artificial Intelligence and Statistics (AISTATS), 2007.
  [pdf]
• Harald Steck and Tommi S. Jaakkola.
  Predictive Discretization during Model Selection.
  Proceedings of the 11th International Conference on Artificial Intelligence and Statistics (AISTATS), 2007.
  [pdf]
• Amir Globerson and Tommi Jaakkola.
  Approximate inference using planar graph decomposition.
  Advances in Neural Information Processing Systems (NIPS), 2006.
  [pdf]
• Luis Perez-Breva, Luis E. Ortiz, Chen-Hsiang Yeang, and Tommi Jaakkola.
  Game theoretic algorithms for protein-DNA binding.
  Advances in Neural Information Processing Systems (NIPS), 2006.
  [pdf]
• Yuan Qi and Tommi S. Jaakkola.
  Parameter Expanded Variational Bayesian Methods.
  Advances in Neural Information Processing Systems (NIPS), 2006.
  [pdf]
• Yuan Qi, Alex Rolfe, Kenzie D MacIsaac, Georg K Gerber, Dmitry Pokholok, Julia Zeitlinger, Timothy Danford, Robin D Dowell, Ernest Fraenkel, Tommi S Jaakkola, Richard A Young, and David K Gifford.
  High-resolution computational models of genome binding events.
  Nature Biotechnology, 24, pp. 963-970. 2006.
  [pdf] [errata]
• Yuan Qi, Patrycja E. Missiuro, Ashish Kapoor, Craig P. Hunter, Tommi S. Jaakkola, David K. Gifford, and Hui Ge.
  Semi-supervised analysis of gene expression profiles for lineage-specific development in the Caenorhabditis elegans embryo.
  Bioinformatics, 22(14), pp. 417-423. 2006.
  [pdf]
• Luis Perez-Breva, Luis E. Ortiz, Chen-Hsiang Yeang, and Tommi Jaakkola.
  DNA Binding and Games.
  2006.
  [pdf]
• Adrian Corduneanu and Tommi Jaakkola.
  Data dependent regularization.
  Semi-supervised learning, 2006.
  [pdf]
• Martin J. Wainwright, Tommi S. Jaakkola, and Alan S. Willsky.
  MAP estimation via agreement on trees: Message-passing and linear-programming approaches.
  IEEE Transactions on Information Theory, 51(11), pp. 3697–3717. 2005.
  [pdf]
• J. Rennie and T. Jaakkola.
  Using term informativeness for named entity detection.
  Proceedings of the 28th Annual Conference on Research and Development in Information Retrieval (SIGIR), 2005.
  [pdf]
• C-H. Yeang and T. Jaakkola.
  Modeling the combinatorial functions of multiple transcription factors.
  The Ninth Annual International Conference on Research in Computational Molecular Biology (RECOMB), 2005.
  [pdf]
• C-H. Yeang, H. Mak, S. McCuine, C. Workman, T. Jaakkola, and T. Ideker.
  Validation and refinement of gene-regulatory pathways on a network of physical interactions.
  Genome Biology, 6(7), pp. R62. 2005.
  [pdf] [link]
• M. Wainwright, T. Jaakkola, and A. Willsky.
  A new class of upper bounds on the log partition function.
  IEEE Transactions on Information Theory, 51, pp. 2313-2335. 2005.
  [pdf]
• R. Rosales and T. Jaakkola.
  Focused inference.
  Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics (AISTATS), 2005.
  [pdf]
• N. Srebro, N. Alon, and T. Jaakkola.
  Generalization Error Bounds for Collaborative Prediction with Low-Rank Matrices.
  Advances in Neural Information Processing Systems (NIPS), 2004.
  [pdf]
• N. Srebro, J. Rennie, and T. Jaakkola.
  Maximum Margin Matrix Factorization.
  Advances in Neural Information Processing Systems (NIPS), 2004.
  [pdf]
• A. Corduneanu and T. Jaakkola.
  Distributed Information Regularization on Graphs.
  Advances in Neural Information Processing Systems (NIPS), 2004.
  [pdf]
• M. Wainwright, T. Jaakkola, and A. Willsky.
  Tree consistency and bounds on the performance of the max-product algorithm and its generalizations.
  Statistics and Computing, 14(2), pp. 143–166. 2004.
  [pdf]
• C-H. Yeang, T. Ideker, and T. Jaakkola.
  Physical network models.
  Journal of Computational Biology, 11(2-3), pp. 243-263. 2004.
  [pdf]
• N. Srebro and T. Jaakkola.
  Linear Dependent Dimensionality Reduction.
  Advances in Neural Information Processing Systems (NIPS), 2003.
  [pdf]
• C. Monteleoni and T. Jaakkola.
  Online Learning of Non-stationary Sequences.
  Advances in Neural Information Processing Systems (NIPS), 2003.
  [ps.gz]
• H. Steck and T. Jaakkola.
  Bias-Corrected Bootstrap and Model Uncertainty.
  Advances in Neural Information Processing Systems (NIPS), 2003.
  [pdf]
• A. Corduneanu and T. Jaakkola.
  On Information Regularization.
  Proceedings of the Nineteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI), 2003.
  [ps.gz]
• Z. Bar-Joseph, G. Gerber, I. Simon, D. Gifford, and T. Jaakkola.
  Comparing continuous representations of time series expression profiles to identify differentially expressed genes.
  Proceedings of the National Academy of Sciences, 100(18), pp. 10146-10151. 2003.
  [pdf] [link]
• Z. Bar-Joseph, G. Gerber, T. Lee, N. Rinaldi, J. Yoo, F. Robert, B. Gordon, E. Fraenkel, T. Jaakkola, R. Young, and D. Gifford.
  Computational Discovery of Gene Modules and Regulatory Networks.
  Nature Biotechnology, 21(11), pp. 1337-1342. 2003.
  [pdf] [link]
• H. Steck and T. Jaakkola.
  Semi-predictive Discretization during Model Selection.
  AI Memo AIM-2003-002, 2003.
  [pdf]
• Z. Bar-Joseph, G. Gerber, D. Gifford, T. Jaakkola, and I. Simon.
  Continuous Representations of Time Series Gene Expression Data.
  Journal of Computational Biology, 10(3-4), pp. 241-256. 2003.
  [pdf]
• N. Srebro and T. Jaakkola.
  Weighted Low-Rank Approximations.
  Proceedings of the Twentieth International Conference on Machine Learning (ICML), 2003.
  [pdf]
• C-H. Yeang and T. Jaakkola.
  Time series analysis of gene expression and location data.
  Proceedings of the Third IEEE Symposium on Bioinformatics and Bioengineering, pp. 305-312. 2003.
  [pdf]
• C-H. Yeang and T. Jaakkola.
  Physical network models and multi-source data integration.
  The Seventh Annual International Conference on Research in Computational Molecular Biology (RECOMB), 2003.
  [pdf]
• N. Srebro and T. Jaakkola.
  Generalized Low-Rank Approximations.
  AI Memo AIM-2003-001, 2003.
  [pdf]
• H. Steck and T. Jaakkola.
  On the Dirichlet Prior and Bayesian Regularization.
  Advances in Neural Information processing systems (NIPS), 2002.
  [ps.gz]
• M. Wainwright, T. Jaakkola, and A. Willsky.
  Tree-based parameterization framework for analysis of belief propagation and related algorithms.
  IEEE Transactions on information theory, 2002.
• M. J. Wainwright, T. Jaakkola, and A. S. Willsky.
  Exact MAP estimates by (hyper)tree agreement.
  Advances in Neural Information processing systems (NIPS), 2002.
  [ps.gz]
• M. Szummer and T. Jaakkola.
  Information Regularization with Partially Labeled Data.
  Advances in Neural Information processing systems (NIPS), 2002.
  [pdf]
• Ziv Bar-Joseph, Erik D. Demaine, David K. Gifford, Angele M. Hamel, Tommi S. Jaakkola, and Nathan Srebro.
  K-ary Clustering with Optimal Leaf Ordering for Gene Expression Data.
  Bioinformatics, 2002.
  [pdf]
• A. Corduneanu and T. Jaakkola.
  Continuation Methods for Mixing Heterogeneous Sources.
  Proceedings of the Eighteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI), 2002.
  [ps.gz]
• H. Steck and T. Jaakkola.
  Unsupervised Active Learning in Large Domains.
  Proceedings of the Eighteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI), 2002.
  [ps.gz]
• M. J. Wainwright, T. Jaakkola, and A. S. Willsky.
  A new class of upper bounds on the log partition function.
  Proceedings of the Eighteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI), 2002.
  [ps.gz]
• Z. Bar-Joseph, G. Gerber, D. Gifford, and T. Jaakkola.
  A New Approach to Analyzing Gene Expression Time Series Data.
  The Sixth Annual International Conference on Research in Computational Molecular Biology (RECOMB), 2002.
  [pdf]
• A. Corduneanu and T. Jaakkola.
  Stable mixing of complete and incomplete information.
  2001.
  [pdf]
• M. Wainwright, T. Jaakkola, and A. Willsky.
  Tree-based reparameterization for approximate estimation on loopy graphs.
  Advances in Neural Information processing systems (NIPS), 2001.
  [pdf]
• M. J. Wainwright, T. Jaakkola, and A. S. Willsky.
  Tree-based reparameterization framework for approximate estimation in graphs with cycles.
  2001.
  [ps.gz]
• M. Szummer and T. Jaakkola.
  Partially labeled classification with Markov Random Walks.
  Advances in Neural Information processing systems (NIPS), 2001.
  [ps]
• T. Jaakkola and H. Siegelmann.
  Active information retrieval.
  Advances in Neural Information processing systems (NIPS), pp. 777-784. 2001.
  [ps.gz]
• A. Hartemink, D. Gifford, T. Jaakkola, and R. Young.
  Combining location and expression data for principled Discovery of genetic regulatory network models.
  Pacific Symposium on Biocomputing, 7. 2002.
  [pdf]
• Z. Bar-Joseph, D. Gifford, and T. Jaakkola.
  Fast Optimal Leaf Ordering for Hierarchical Clustering.
  Proceedings of the Ninth International Conference on Intelligent Systems for Molecular Biology (ISMB), 2001.
  [pdf]
• A. Hartemink, D. Gifford, T. Jaakkola, and R. Young.
  Maximum likelihood estimation of optimal scaling factors for expression array normalization.
  Microarrays: Optical Technologies and Informatics, Proceedings of SPIE, 4266. 2001.
  [ps]
• A. Hartemink, D. Gifford, T. Jaakkola, and R. Young.
  Using Graphical Models and Genomic Expression Data to Statistically Validate Models of Genetic Regulatory Networks.
  Pacific Symposium on Biocomputing, 6, pp. 422-433. 2001.
  [pdf]
• T. Jaakkola.
  Tutorial on variational approximation methods.
  Advanced mean field methods: theory and practice, 2000.
  [ps]
• T. Jaakkola and M. Jordan.
  Bayesian parameter estimation via variational methods.
  Statistics and Computing, 10, pp. 25–37. 2000.
  [ps]
• T. Jaakkola, M. Diekhans, and D. Haussler.
  A discriminative framework for detecting remote protein homologies.
  Journal of Computational Biology, 7(1,2), pp. 95–114. 2000.
  [ps]
• B. Frey, R. Patrascu, T. Jaakkola, and J. Moran.
  Sequentially fitting inclusive trees for inference in Noisy-OR networks.
  Advances in Neural Information Processing Systems (NIPS), 2000.
  [ps]
• M. Szummer and T. Jaakkola.
  Kernel expansions with unlabeled examples.
  Advances in Neural Information Processing Systems (NIPS), 2000.
  [ps]
• M. Meila and T. Jaakkola.
  Tractable Bayesian Learning of Tree Belief Networks.
  Proceedings of the Sixteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI), 2000.
  [ps]
• T. Jebara and T. Jaakkola.
  Feature selection and dualities in maximum entropy discrimination.
  Proceedings of the Sixteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI), 2000.
  [ps]
• S. Singh, T. Jaakkola, M. Littman, and C. Szepesvari.
  Convergence Results for Single-Step On-Policy Reinforcement-Learning Algorithms.
  Machine Learning, 38(3), pp. 287. 2000.
  [ps]
• T. Jaakkola, M. Meila, and T. Jebara.
  Maximum entropy discrimination.
  Advances in Neural Information Processing Systems (NIPS), 1999.
  [ps]
• T. Jaakkola, M. Meila, and T. Jebara.
  Maximum entropy discrimination.
  1999.
  [ps]
• T. Jaakkola, M. Diekhans, and D. Haussler.
  Using the Fisher kernel method to detect remote protein homologies.
  Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology (ISMB), 1999.
  [ps]
• T. Jaakkola and M. Jordan.
  Variational probabilistic inference and the QMR-DT database.
  Journal of Artificial Intelligence Research, 10, pp. 291–322. 1999.
  [ps] [pdf]
• M. Jordan, Z. Ghahramani, T. Jaakkola, and L. Saul.
  An Introduction to Variational Methods for Graphical Models.
  Machine Learning, 37(2), pp. 183. 1999.
  [ps]
• T. Jaakkola and D. Haussler.
  Probabilistic kernel regression models.
  Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics (AISTATS), 1999.
  [ps]
• T. Jaakkola and D. Haussler.
  Exploiting generative models in discriminative classifiers.
  Advances in Neural Information Processing Systems (NIPS), 1998.
  [ps]
• C. Bishop, N. Lawrence, T. Jaakkola, and M. Jordan.
  Approximating posterior distributions in Belief networks using mixtures.
  Advances in Neural Information Processing Systems (NIPS), 1997.
  [ps]
• T. Jaakkola and M. Jordan.
  A variational approach to Bayesian logistic regression models and their extensions.
  Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics (AISTATS), 1997.
  [ps]
• T. Jaakkola.
  Variational methods for inference and estimation in graphical models.
  1997.
  [ps]
• T. Jaakkola and M. Jordan.
  Improving the mean field approximation via the use of mixture distributions.
  Proceedings of the NATO ASI on Learning in Graphical Models, 1997.
  [ps]
• L. Saul, T. Jaakkola, and M. Jordan.
  Mean field theory for sigmoid belief networks.
  Journal of Artificial Intelligence Research, 4, pp. 61–76. 1996.
  [ps] [pdf]
• T. Jaakkola and M. Jordan.
  Recursive algorithms for approximating probabilities in graphical models.
  Advances in Neural Information Processing Systems (NIPS), 1996.
  [ps]
• T. Jaakkola and M. Jordan.
  Computing Upper and Lower Bounds on Likelihoods in Intractable Networks.
  Proceedings of the Twelfth Annual Conference on Uncertainty in Artificial Intelligence (UAI), pp. 340–348. 1996.
  [ps]
• T. Jaakkola, L. Saul, and M. Jordan.
  Fast learning by bounding likelihoods in sigmoid type belief networks.
  Advances in Neural Information Processing Systems (NIPS), 1995.
  [ps]
• S. Singh, T. Jaakkola, and M. Jordan.
  Reinforcement learning with soft state aggregation.
  Advances in Neural Information Processing Systems (NIPS), 1994.
  [ps]
• T. Jaakkola, S. Singh, and M. Jordan.
  Reinforcement Learning Algorithm for Partially Observable Markov Decision Problems.
  Advances in Neural Information Processing Systems (NIPS), 1994.
  [ps]
• S. Singh, T. Jaakkola, and M. Jordan.
  Learning without state estimation in partially observable environments.
  Proceedings of the Eleventh Machine Learning Conference (ICML), 1994.
  [ps]
• T. Jaakkola, M. Jordan, and S. Singh.
  On the Convergence of Stochastic Iterative Dynamic Programming Algorithms.
  Neural Computation, 6(6), pp. 1185–1201. 1994.
  [ps]
• T. Jaakkola, M. Jordan, and S. Singh.
  Convergence of stochastic iterative Dynamic Programming algorithms.
  Advances in Neural Information Processing Systems (NIPS), 1993.
  [ps]
• A. Friberg, T. Jaakkola, and J. Tuovinen.
  Electromagnetic gaussian beam beyond the paraxial regime.
  IEEE Transactions of Antennas and Propagation, 1992.
• A. Vasara, M. Taghizadeh, J. Turunen, Westerholmand E. Noponen J., H. Ichikawa, J. Miller, T. Jaakkola, and S. Kuisma.
  Binary surface-relief gratings for array illumination in digital optics.
  Applied Optics, 31(17), pp. 3320-3336. 1992.