| 2025 | CVPR | Scaling Inference Time Compute for Diffusion Models. | Nanye Ma, Shangyuan Tong, Haolin Jia, Hexiang Hu, Yu-Chuan Su, Mingda Zhang, Xuan Yang, Yandong Li, Tommi S. Jaakkola, Xuhui Jia, Saining Xie |
| 2025 | EMNLP | Thought calibration: Efficient and confident test-time scaling. | Menghua Wu, Cai Zhou, Stephen Bates, Tommi S. Jaakkola |
| 2025 | ICLR | Composing Unbalanced Flows for Flexible Docking and Relaxation. | Gabriele Corso, Vignesh Ram Somnath, Noah Getz, Regina Barzilay, Tommi S. Jaakkola, Andreas Krause |
| 2025 | ICLR | Generator Matching: Generative modeling with arbitrary Markov processes. | Peter Holderrieth, Marton Havasi, Jason Yim, Neta Shaul, Itai Gat, Tommi S. Jaakkola, Brian Karrer, Ricky T. Q. Chen, Yaron Lipman |
| 2025 | ICLR | Data Distillation for extrapolative protein design through exact preference optimization. | Mostafa Karimi, Sharmi Banerjee, Tommi S. Jaakkola, Bella Dubrov, Shang Shang, Ron Benson |
| 2025 | ICLR | Fictitious Synthetic Data Can Improve LLM Factuality via Prerequisite Learning. | Yujian Liu, Shiyu Chang, Tommi S. Jaakkola, Yang Zhang |
| 2025 | ICLR | Think while You Generate: Discrete Diffusion with Planned Denoising. | Sulin Liu, Juno Nam, Andrew Campbell, Hannes Strk, Yilun Xu, Tommi S. Jaakkola, Rafael Gmez-Bombarelli |
| 2025 | ICLR | ProtComposer: Compositional Protein Structure Generation with 3D Ellipsoids. | Hannes Strk, Bowen Jing, Tomas Geffner, Jason Yim, Tommi S. Jaakkola, Arash Vahdat, Karsten Kreis |
| 2025 | ICLR | An Information Criterion for Controlled Disentanglement of Multimodal Data. | Chenyu Wang, Sharut Gupta, Xinyi Zhang, Sana Tonekaboni, Stefanie Jegelka, Tommi S. Jaakkola, Caroline Uhler |
| 2025 | ICLR | Fine-Tuning Discrete Diffusion Models via Reward Optimization with Applications to DNA and Protein Design. | Chenyu Wang, Masatoshi Uehara, Yichun He, Amy Wang, Avantika Lal, Tommi S. Jaakkola, Sergey Levine, Aviv Regev, Hanchen Wang, Tommaso Biancalani |
| 2025 | ICML | LEAPS: A discrete neural sampler via locally equivariant networks. | Peter Holderrieth, Michael Samuel Albergo, Tommi S. Jaakkola |
| 2025 | ICML | Symmetry-Driven Discovery of Dynamical Variables in Molecular Simulations. | Jeet Mohapatra, Nima Dehmamy, Csaba Both, Subhro Das, Tommi S. Jaakkola |
| 2025 | ICML | Identifying biological perturbation targets through causal differential networks. | Menghua Wu, Umesh Padia, Sean H. Murphy, Regina Barzilay, Tommi S. Jaakkola |
| 2024 | CVPR | Correcting Diffusion Generation Through Resampling. | Yujian Liu, Yang Zhang, Tommi S. Jaakkola, Shiyu Chang |
| 2024 | EMNLP | Revisiting Who's Harry Potter: Towards Targeted Unlearning from a Causal Intervention Perspective. | Yujian Liu, Yang Zhang, Tommi S. Jaakkola, Shiyu Chang |
| 2024 | ICLR | MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design. | Xiang Fu, Tian Xie, Andrew S. Rosen, Tommi S. Jaakkola, Jake Smith |
| 2024 | ICLR | Deep Confident Steps to New Pockets: Strategies for Docking Generalization. | Gabriele Corso, Arthur Deng, Nicholas Polizzi, Regina Barzilay, Tommi S. Jaakkola |
| 2024 | ICLR | Particle Guidance: non-I.I.D. Diverse Sampling with Diffusion Models. | Gabriele Corso, Yilun Xu, Valentin De Bortoli, Regina Barzilay, Tommi S. Jaakkola |
| 2024 | ICLR | Equivariant Scalar Fields for Molecular Docking with Fast Fourier Transforms. | Bowen Jing, Tommi S. Jaakkola, Bonnie Berger |
| 2024 | ICLR | Improving protein optimization with smoothed fitness landscapes. | Andrew Kirjner, Jason Yim, Raman Samusevich, Shahar Bracha, Tommi S. Jaakkola, Regina Barzilay, Ila R. Fiete |
| 2024 | ICLR | Conformal Language Modeling. | Victor Quach, Adam Fisch, Tal Schuster, Adam Yala, Jae Ho Sohn, Tommi S. Jaakkola, Regina Barzilay |
| 2024 | ICLR | Removing Biases from Molecular Representations via Information Maximization. | Chenyu Wang, Sharut Gupta, Caroline Uhler, Tommi S. Jaakkola |
| 2024 | ICML | Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design. | Andrew Campbell, Jason Yim, Regina Barzilay, Tom Rainforth, Tommi S. Jaakkola |
| 2024 | ICML | AlphaFold Meets Flow Matching for Generating Protein Ensembles. | Bowen Jing, Bonnie Berger, Tommi S. Jaakkola |
| 2024 | ICML | Harmonic Self-Conditioned Flow Matching for joint Multi-Ligand Docking and Binding Site Design. | Hannes Strk, Bowen Jing, Regina Barzilay, Tommi S. Jaakkola |
| 2024 | ICML | Dirichlet Flow Matching with Applications to DNA Sequence Design. | Hannes Strk, Bowen Jing, Chenyu Wang, Gabriele Corso, Bonnie Berger, Regina Barzilay, Tommi S. Jaakkola |
| 2024 | ICML | DisCo-Diff: Enhancing Continuous Diffusion Models with Discrete Latents. | Yilun Xu, Gabriele Corso, Tommi S. Jaakkola, Arash Vahdat, Karsten Kreis |
| 2023 | ICLR | Is Conditional Generative Modeling all you need for Decision Making? | Anurag Ajay, Yilun Du, Abhi Gupta, Joshua B. Tenenbaum, Tommi S. Jaakkola, Pulkit Agrawal |
| 2023 | ICLR | DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking. | Gabriele Corso, Hannes Strk, Bowen Jing, Regina Barzilay, Tommi S. Jaakkola |
| 2023 | ICLR | Efficiently Controlling Multiple Risks with Pareto Testing. | Bracha Laufer-Goldshtein, Adam Fisch, Regina Barzilay, Tommi S. Jaakkola |
| 2023 | ICLR | Diffusion Probabilistic Modeling of Protein Backbones in 3D for the motif-scaffolding problem. | Brian L. Trippe, Jason Yim, Doug Tischer, David Baker, Tamara Broderick, Regina Barzilay, Tommi S. Jaakkola |
| 2023 | ICLR | Stable Target Field for Reduced Variance Score Estimation in Diffusion Models. | Yilun Xu, Shangyuan Tong, Tommi S. Jaakkola |
| 2023 | ICML | PFGM++: Unlocking the Potential of Physics-Inspired Generative Models. | Yilun Xu, Ziming Liu, Yonglong Tian, Shangyuan Tong, Max Tegmark, Tommi S. Jaakkola |
| 2023 | ICML | SE(3) diffusion model with application to protein backbone generation. | Jason Yim, Brian L. Trippe, Valentin De Bortoli, Emile Mathieu, Arnaud Doucet, Regina Barzilay, Tommi S. Jaakkola |
| 2023 | ICML | Towards Coherent Image Inpainting Using Denoising Diffusion Implicit Models. | Guanhua Zhang, Jiabao Ji, Yang Zhang, Mo Yu, Tommi S. Jaakkola, Shiyu Chang |
| 2022 | ECCV | Subspace Diffusion Generative Models. | Bowen Jing, Gabriele Corso, Renato Berlinghieri, Tommi S. Jaakkola |
| 2022 | ICLR | Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking. | Octavian-Eugen Ganea, Xinyuan Huang, Charlotte Bunne, Yatao Bian, Regina Barzilay, Tommi S. Jaakkola, Andreas Krause |
| 2022 | ICLR | Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design. | Wengong Jin, Jeremy Wohlwend, Regina Barzilay, Tommi S. Jaakkola |
| 2022 | ICLR | Adversarial Support Alignment. | Shangyuan Tong, Timur Garipov, Yang Zhang, Shiyu Chang, Tommi S. Jaakkola |
| 2022 | ICLR | Crystal Diffusion Variational Autoencoder for Periodic Material Generation. | Tian Xie, Xiang Fu, Octavian-Eugen Ganea, Regina Barzilay, Tommi S. Jaakkola |
| 2022 | ICLR | Controlling Directions Orthogonal to a Classifier. | Yilun Xu, Hao He, Tianxiao Shen, Tommi S. Jaakkola |
| 2022 | ICML | Conformal Prediction Sets with Limited False Positives. | Adam Fisch, Tal Schuster, Tommi S. Jaakkola, Regina Barzilay |
| 2022 | ICML | Antibody-Antigen Docking and Design via Hierarchical Structure Refinement. | Wengong Jin, Regina Barzilay, Tommi S. Jaakkola |
| 2022 | ICML | EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction. | Hannes Strk, Octavian Ganea, Lagnajit Pattanaik, Regina Barzilay, Tommi S. Jaakkola |
| 2021 | CVPR | Mol2Image: Improved Conditional Flow Models for Molecule to Image Synthesis. | Karren D. Yang, Samuel Goldman, Wengong Jin, Alex X. Lu, Regina Barzilay, Tommi S. Jaakkola, Caroline Uhler |
| 2021 | EMNLP | Consistent Accelerated Inference via Confident Adaptive Transformers. | Tal Schuster, Adam Fisch, Tommi S. Jaakkola, Regina Barzilay |
| 2021 | ICLR | Efficient Conformal Prediction via Cascaded Inference with Expanded Admission. | Adam Fisch, Tal Schuster, Tommi S. Jaakkola, Regina Barzilay |
| 2021 | ICML | Few-Shot Conformal Prediction with Auxiliary Tasks. | Adam Fisch, Tal Schuster, Tommi S. Jaakkola, Regina Barzilay |
| 2021 | ICML | Learning Task Informed Abstractions. | Xiang Fu, Ge Yang, Pulkit Agrawal, Tommi S. Jaakkola |
| 2021 | ICML | Information Obfuscation of Graph Neural Networks. | Peiyuan Liao, Han Zhao, Keyulu Xu, Tommi S. Jaakkola, Geoffrey J. Gordon, Stefanie Jegelka, Ruslan Salakhutdinov |
| 2020 | AISTATS | Unsupervised Hierarchy Matching with Optimal Transport over Hyperbolic Spaces. | David Alvarez-Melis, Youssef Mroueh, Tommi S. Jaakkola |
| 2020 | EMNLP | Blank Language Models. | Tianxiao Shen, Victor Quach, Regina Barzilay, Tommi S. Jaakkola |
| 2020 | ICLR | Self-Supervised Learning of Appliance Usage. | Chen-Yu Hsu, Abbas Zeitoun, Guang-He Lee, Dina Katabi, Tommi S. Jaakkola |
| 2020 | ICLR | Oblique Decision Trees from Derivatives of ReLU Networks. | Guang-He Lee, Tommi S. Jaakkola |
| 2020 | ICML | Invariant Rationalization. | Shiyu Chang, Yang Zhang, Mo Yu, Tommi S. Jaakkola |
| 2020 | ICML | Predicting deliberative outcomes. | Vikas K. Garg, Tommi S. Jaakkola |
| 2020 | ICML | Generalization and Representational Limits of Graph Neural Networks. | Vikas K. Garg, Stefanie Jegelka, Tommi S. Jaakkola |
| 2020 | ICML | Hierarchical Generation of Molecular Graphs using Structural Motifs. | Wengong Jin, Regina Barzilay, Tommi S. Jaakkola |
| 2020 | ICML | Multi-Objective Molecule Generation using Interpretable Substructures. | Wengong Jin, Regina Barzilay, Tommi S. Jaakkola |
| 2020 | ICML | Educating Text Autoencoders: Latent Representation Guidance via Denoising. | Tianxiao Shen, Jonas Mueller, Regina Barzilay, Tommi S. Jaakkola |
| 2020 | ICML | Improving Molecular Design by Stochastic Iterative Target Augmentation. | Kevin Yang, Wengong Jin, Kyle Swanson, Regina Barzilay, Tommi S. Jaakkola |
| 2019 | AAAI | Bidirectional Inference Networks: A Class of Deep Bayesian Networks for Health Profiling. | Hao Wang, Chengzhi Mao, Hao He, Mingmin Zhao, Tommi S. Jaakkola, Dina Katabi |
| 2019 | AISTATS | Towards Optimal Transport with Global Invariances. | David Alvarez-Melis, Stefanie Jegelka, Tommi S. Jaakkola |
| 2019 | EMNLP | Rethinking Cooperative Rationalization: Introspective Extraction and Complement Control. | Mo Yu, Shiyu Chang, Yang Zhang, Tommi S. Jaakkola |
| 2019 | ICLR | Generative Models for Graph-Based Protein Design. | John Ingraham, Vikas K. Garg, Regina Barzilay, Tommi S. Jaakkola |
| 2019 | ICLR | Learning Multimodal Graph-to-Graph Translation for Molecule Optimization. | Wengong Jin, Kevin Yang, Regina Barzilay, Tommi S. Jaakkola |
| 2019 | ICLR | Towards Robust, Locally Linear Deep Networks. | Guang-He Lee, David Alvarez-Melis, Tommi S. Jaakkola |
| 2019 | ICML | Functional Transparency for Structured Data: a Game-Theoretic Approach. | Guang-He Lee, Wengong Jin, David Alvarez-Melis, Tommi S. Jaakkola |
| 2018 | AISTATS | Structured Optimal Transport. | David Alvarez-Melis, Tommi S. Jaakkola, Stefanie Jegelka |
| 2018 | EMNLP | Gromov-Wasserstein Alignment of Word Embedding Spaces. | David Alvarez-Melis, Tommi S. Jaakkola |
| 2018 | ICML | Junction Tree Variational Autoencoder for Molecular Graph Generation. | Wengong Jin, Regina Barzilay, Tommi S. Jaakkola |
| 2018 | UAI | The Variational Homoencoder: Learning to learn high capacity generative models from few examples. | Luke B. Hewitt, Maxwell I. Nye, Andreea Gane, Tommi S. Jaakkola, Joshua B. Tenenbaum |
| 2017 | AISTATS | Learning Optimal Interventions. | Jonas Mueller, David Reshef, George Du, Tommi S. Jaakkola |
| 2017 | EMNLP | A causal framework for explaining the predictions of black-box sequence-to-sequence models. | David Alvarez-Melis, Tommi S. Jaakkola |
| 2017 | ICLR | Tree-structured decoding with doubly-recurrent neural networks. | David Alvarez-Melis, Tommi S. Jaakkola |
| 2017 | ICML | Deriving Neural Architectures from Sequence and Graph Kernels. | Tao Lei, Wengong Jin, Regina Barzilay, Tommi S. Jaakkola |
| 2017 | ICML | Sequence to Better Sequence: Continuous Revision of Combinatorial Structures. | Jonas Mueller, David K. Gifford, Tommi S. Jaakkola |
| 2017 | ICML | Learning Sleep Stages from Radio Signals: A Conditional Adversarial Architecture. | Mingmin Zhao, Shichao Yue, Dina Katabi, Tommi S. Jaakkola, Matt T. Bianchi |
| 2016 | AISTATS | CRAFT: ClusteR-specific Assorted Feature selecTion. | Vikas K. Garg, Cynthia Rudin, Tommi S. Jaakkola |
| 2016 | EMNLP | Learning to refine text based recommendations. | Youyang Gu, Tao Lei, Regina Barzilay, Tommi S. Jaakkola |
| 2016 | EMNLP | Rationalizing Neural Predictions. | Tao Lei, Regina Barzilay, Tommi S. Jaakkola |
| 2016 | ICML | Learning Population-Level Diffusions with Generative RNNs. | Tatsunori B. Hashimoto, David K. Gifford, Tommi S. Jaakkola |
| 2016 | NAACL | Semi-supervised Question Retrieval with Gated Convolutions. | Tao Lei, Hrishikesh Joshi, Regina Barzilay, Tommi S. Jaakkola, Kateryna Tymoshenko, Alessandro Moschitti, Llus Mrquez |
| 2016 | NAACL | Ten Pairs to Tag - Multilingual POS Tagging via Coarse Mapping between Embeddings. | Yuan Zhang, David Gaddy, Regina Barzilay, Tommi S. Jaakkola |
| 2016 | UAI | Structured Prediction: From Gaussian Perturbations to Linear-Time Principled Algorithms. | Jean Honorio, Tommi S. Jaakkola |
| 2015 | AISTATS | Metric recovery from directed unweighted graphs. | Tatsunori B. Hashimoto, Yi Sun, Tommi S. Jaakkola |
| 2015 | EMNLP | Molding CNNs for text: non-linear, non-consecutive convolutions. | Tao Lei, Regina Barzilay, Tommi S. Jaakkola |
| 2014 | ACL | Low-Rank Tensors for Scoring Dependency Structures. | Tao Lei, Yu Xin, Yuan Zhang, Regina Barzilay, Tommi S. Jaakkola |
| 2014 | ACL | Steps to Excellence: Simple Inference with Refined Scoring of Dependency Trees. | Yuan Zhang, Tao Lei, Regina Barzilay, Tommi S. Jaakkola, Amir Globerson |
| 2014 | AISTATS | Learning with Maximum A-Posteriori Perturbation Models. | Andreea Gane, Tamir Hazan, Tommi S. Jaakkola |
| 2014 | AISTATS | Tight Bounds for the Expected Risk of Linear Classifiers and PAC-Bayes Finite-Sample Guarantees. | Jean Honorio, Tommi S. Jaakkola |
| 2014 | AISTATS | Active Boundary Annotation using Random MAP Perturbations. | Subhransu Maji, Tamir Hazan, Tommi S. Jaakkola |
| 2014 | EMNLP | Greed is Good if Randomized: New Inference for Dependency Parsing. | Yuan Zhang, Tao Lei, Regina Barzilay, Tommi S. Jaakkola |
| 2014 | ICML | A Unified Framework for Consistency of Regularized Loss Minimizers. | Jean Honorio, Tommi S. Jaakkola |
| 2014 | ICML | On Measure Concentration of Random Maximum A-Posteriori Perturbations. | Francesco Orabona, Tamir Hazan, Anand D. Sarwate, Tommi S. Jaakkola |
| 2013 | ICML | Two-Sided Exponential Concentration Bounds for Bayes Error Rate and Shannon Entropy. | Jean Honorio, Tommi S. Jaakkola |
| 2013 | UAI | Inverse Covariance Estimation for High-Dimensional Data in Linear Time and Space: Spectral Methods for Riccati and Sparse Models. | Jean Honorio, Tommi S. Jaakkola |
| 2012 | ICML | On the Partition Function and Random Maximum A-Posteriori Perturbations. | Tamir Hazan, Tommi S. Jaakkola |
| 2010 | CIKM | Collaborative future event recommendation. | Einat Minkov, Ben Charrow, Jonathan Ledlie, Seth J. Teller, Tommi S. Jaakkola |
| 2010 | EMNLP | Dual Decomposition for Parsing with Non-Projective Head Automata. | Terry Koo, Alexander M. Rush, Michael Collins, Tommi S. Jaakkola, David A. Sontag |
| 2010 | EMNLP | On Dual Decomposition and Linear Programming Relaxations for Natural Language Processing. | Alexander M. Rush, David A. Sontag, Michael Collins, Tommi S. Jaakkola |
| 2010 | ICML | Learning Efficiently with Approximate Inference via Dual Losses. | Ofer Meshi, David A. Sontag, Tommi S. Jaakkola, Amir Globerson |
| 2008 | UAI | Tightening LP Relaxations for MAP using Message Passing. | David A. Sontag, Talya Meltzer, Amir Globerson, Tommi S. Jaakkola, Yair Weiss |
| 2007 | UAI | Convergent Propagation Algorithms via Oriented Trees. | Amir Globerson, Tommi S. Jaakkola |
| 2006 | ISMB | Semi-supervised analysis of gene expression profiles for lineage-specific development in the | Yuan (Alan) Qi, Patrycja E. Missiuro, Ashish Kapoor, Craig P. Hunter, Tommi S. Jaakkola, David K. Gifford, Hui Ge |
| 2005 | AISTATS | Focused Inference. | Rmer Rosales, Tommi S. Jaakkola |
| 2005 | RECOMB | Modeling the Combinatorial Functions of Multiple Transcription Factors. | Chen-Hsiang Yeang, Tommi S. Jaakkola |
| 2005 | SIGIR | Using term informativeness for named entity detection. | Jason D. M. Rennie, Tommi S. Jaakkola |
| 2003 | AISTATS | Tree-reweighted belief propagation algorithms and approximate ML estimation by pseudo-moment matching. | Martin J. Wainwright, Tommi S. Jaakkola, Alan S. Willsky |
| 2003 | BIBE | Time Series Analysis of Gene Expression and Location Data. | Chen-Hsiang Yeang, Tommi S. Jaakkola |
| 2003 | ICML | Weighted Low-Rank Approximations. | Nathan Srebro, Tommi S. Jaakkola |
| 2003 | RECOMB | Physical network models and multi-source data integration. | Chen-Hsiang Yeang, Tommi S. Jaakkola |
| 2003 | UAI | On Information Regularization. | Adrian Corduneanu, Tommi S. Jaakkola |
| 2002 | PSB | Combining Location and Expression Data for Principled Discovery of Genetic Regulatory Network Models. | Alexander J. Hartemink, David K. Gifford, Tommi S. Jaakkola, Richard A. Young |
| 2002 | RECOMB | A new approach to analyzing gene expression time series data. | Ziv Bar-Joseph, Georg K. Gerber, David K. Gifford, Tommi S. Jaakkola, Itamar Simon |
| 2002 | UAI | Continuation Methods for Mixing Heterogenous Sources. | Adrian Corduneanu, Tommi S. Jaakkola |
| 2002 | UAI | Unsupervised Active Learning in Large Domains. | Harald Steck, Tommi S. Jaakkola |
| 2002 | UAI | A New Class of upper Bounds on the Log Partition Function. | Martin J. Wainwright, Tommi S. Jaakkola, Alan S. Willsky |
| 2002 | WABI | K-ary Clustering with Optimal Leaf Ordering for Gene Expression Data. | Ziv Bar-Joseph, Erik D. Demaine, David K. Gifford, Angle M. Hamel, Tommi S. Jaakkola, Nathan Srebro |
| 2001 | ISMB | Fast optimal leaf ordering for hierarchical clustering. | Ziv Bar-Joseph, David K. Gifford, Tommi S. Jaakkola |
| 2001 | PSB | Using Graphical Models and Genomic Expression Data to Statistically Validate Models of Genetic Regulatory Networks. | Alexander J. Hartemink, David K. Gifford, Tommi S. Jaakkola, Richard A. Young |
| 2000 | UAI | Feature Selection and Dualities in Maximum Entropy Discrimination. | Tony Jebara, Tommi S. Jaakkola |
| 2000 | UAI | Tractable Bayesian Learning of Tree Belief Networks. | Marina Meila, Tommi S. Jaakkola |
| 1999 | AISTATS | Probabilistic kernel regression models. | Tommi S. Jaakkola, David Haussler |
| 1999 | ISMB | Using the Fisher Kernel Method to Detect Remote Protein Homologies. | Tommi S. Jaakkola, Mark Diekhans, David Haussler |
| 1997 | AISTATS | A Variational Approach to Bayesian Logistic Regression Models and their Extensions. | Tommi S. Jaakkola, Michael I. Jordan |
| 1996 | UAI | Computing upper and lower bounds on likelihoods in intractable networks. | Tommi S. Jaakkola, Michael I. Jordan |
| 1994 | ICML | Learning Without State-Estimation in Partially Observable Markovian Decision Processes. | Satinder P. Singh, Tommi S. Jaakkola, Michael I. Jordan |