Jaakkola, Tommi S.

91 publications

FnTML 2025 Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems Xuan Zhang, Limei Wang, Jacob Helwig, Youzhi Luo, Cong Fu, Yaochen Xie, Meng Liu, Yuchao Lin, Zhao Xu, Keqiang Yan, Keir Adams, Maurice Weiler, Xiner Li, Tianfan Fu, Yucheng Wang, Alex Strasser, Haiyang Yu, Yuqing Xie, Xiang Fu, Shenglong Xu, Yi Liu, Yuanqi Du, Alexandra Saxton, Hongyi Ling, Hannah Lawrence, Hannes Stärk, Shurui Gui, Carl Edwards, Nicholas Gao, Adriana Ladera, Tailin Wu, Elyssa F. Hofgard, Aria Mansouri Tehrani, Rui Wang, Ameya Daigavane, Montgomery Bohde, Jerry Kurtin, Qian Huang, Tuong Phung, Minkai Xu, Chaitanya K. Joshi, Simon V. Mathis, Kamyar Azizzadenesheli, Ada Fang, Alán Aspuru-Guzik, Erik J. Bekkers, Michael M. Bronstein, Marinka Zitnik, Anima Anandkumar, Stefano Ermon, Pietro Liò, Rose Yu, Stephan Günnemann, Jure Leskovec, Heng Ji, Jimeng Sun, Regina Barzilay, Tommi S. Jaakkola, Connor W. Coley, Xiaoning Qian, Xiaofeng Qian, Tess E. Smidt, Shuiwang Ji

ICLR 2024 Conformal Language Modeling Victor Quach, Adam Fisch, Tal Schuster, Adam Yala, Jae Ho Sohn, Tommi S. Jaakkola, Regina Barzilay

ICLR 2024 Deep Confident Steps to New Pockets: Strategies for Docking Generalization Gabriele Corso, Arthur Deng, Nicholas Polizzi, Regina Barzilay, Tommi S. Jaakkola

ICLR 2024 Equivariant Scalar Fields for Molecular Docking with Fast Fourier Transforms Bowen Jing, Tommi S. Jaakkola, Bonnie Berger

ICLR 2024 Improving Protein Optimization with Smoothed Fitness Landscapes Andrew Kirjner, Jason Yim, Raman Samusevich, Shahar Bracha, Tommi S. Jaakkola, Regina Barzilay, Ila R Fiete

ICLR 2024 MOFDiff: Coarse-Grained Diffusion for Metal-Organic Framework Design Xiang Fu, Tian Xie, Andrew Scott Rosen, Tommi S. Jaakkola, Jake Allen Smith

ICLR 2024 Particle Guidance: Non-I.I.D. Diverse Sampling with Diffusion Models Gabriele Corso, Yilun Xu, Valentin De Bortoli, Regina Barzilay, Tommi S. Jaakkola

ICLR 2024 Removing Biases from Molecular Representations via Information Maximization Chenyu Wang, Sharut Gupta, Caroline Uhler, Tommi S. Jaakkola

ICLRW 2023 DiffDock-PP: Rigid Protein-Protein Docking with Diffusion Models Mohamed Amine Ketata, Cedrik Laue, Ruslan Mammadov, Hannes Stark, Menghua Wu, Gabriele Corso, Céline Marquet, Regina Barzilay, Tommi S. Jaakkola

ICLR 2023 DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking Gabriele Corso, Hannes Stärk, Bowen Jing, Regina Barzilay, Tommi S. Jaakkola

ICLR 2023 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

ICLR 2023 Efficiently Controlling Multiple Risks with Pareto Testing Bracha Laufer-Goldshtein, Adam Fisch, Regina Barzilay, Tommi S. Jaakkola

ICLRW 2023 EigenFold: Generative Protein Structure Prediction with Diffusion Models Bowen Jing, Ezra Erives, Peter Pao-Huang, Gabriele Corso, Bonnie Berger, Tommi S. Jaakkola

ICLR 2023 Is Conditional Generative Modeling All You Need for Decision Making? Anurag Ajay, Yilun Du, Abhi Gupta, Joshua B. Tenenbaum, Tommi S. Jaakkola, Pulkit Agrawal

ICMLW 2023 Optimizing Protein Fitness Using Gibbs Sampling with Graph-Based Smoothing Andrew Kirjner, Jason Yim, Raman Samusevich, Tommi S. Jaakkola, Regina Barzilay, Ila R Fiete

TMLR 2023 Simulate Time-Integrated Coarse-Grained Molecular Dynamics with Multi-Scale Graph Networks Xiang Fu, Tian Xie, Nathan J. Rebello, Bradley Olsen, Tommi S. Jaakkola

ICLR 2023 Stable Target Field for Reduced Variance Score Estimation in Diffusion Models Yilun Xu, Shangyuan Tong, Tommi S. Jaakkola

ICLR 2022 Adversarial Support Alignment Shangyuan Tong, Timur Garipov, Yang Zhang, Shiyu Chang, Tommi S. Jaakkola

TMLR 2022 Calibrated Selective Classification Adam Fisch, Tommi S. Jaakkola, Regina Barzilay

ICLR 2022 Controlling Directions Orthogonal to a Classifier Yilun Xu, Hao He, Tianxiao Shen, Tommi S. Jaakkola

ICLR 2022 Crystal Diffusion Variational Autoencoder for Periodic Material Generation Tian Xie, Xiang Fu, Octavian-Eugen Ganea, Regina Barzilay, Tommi S. Jaakkola

NeurIPSW 2022 DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking Gabriele Corso, Hannes Stärk, Bowen Jing, Regina Barzilay, Tommi S. Jaakkola

ICLRW 2022 EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction Hannes Stärk, Octavian-Eugen Ganea, Lagnajit Pattanaik, Regina Barzilay, Tommi S. Jaakkola

NeurIPSW 2022 Forces Are Not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations Xiang Fu, Zhenghao Wu, Wujie Wang, Tian Xie, Sinan Keten, Rafael Gomez-Bombarelli, Tommi S. Jaakkola

JMLR 2022 Fundamental Limits and Tradeoffs in Invariant Representation Learning Han Zhao, Chen Dan, Bryon Aragam, Tommi S. Jaakkola, Geoffrey J. Gordon, Pradeep Ravikumar

ICLR 2022 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

NeurIPSW 2022 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 2022 Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-Design Wengong Jin, Jeremy Wohlwend, Regina Barzilay, Tommi S. Jaakkola

ICLRW 2022 Simulate Time-Integrated Coarse-Grained Molecular Dynamics with Geometric Machine Learning Xiang Fu, Tian Xie, Nathan J. Rebello, Bradley Olsen, Tommi S. Jaakkola

ICLRW 2022 Torsional Diffusion for Molecular Conformer Generation Bowen Jing, Gabriele Corso, Regina Barzilay, Tommi S. Jaakkola

ICLR 2021 Efficient Conformal Prediction via Cascaded Inference with Expanded Admission Adam Fisch, Tal Schuster, Tommi S. Jaakkola, Regina Barzilay

NeurIPSW 2021 Fragment-Based Sequential Translation for Molecular Optimization Benson Chen, Xiang Fu, Regina Barzilay, Tommi S. Jaakkola

ICLR 2020 Locally Constant Networks Guang-He Lee, Tommi S. Jaakkola

AAAI 2019 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

AISTATS 2019 Towards Optimal Transport with Global Invariances David Alvarez-Melis, Stefanie Jegelka, Tommi S. Jaakkola

ICLR 2019 Towards Robust, Locally Linear Deep Networks Guang-He Lee, David Alvarez-Melis, Tommi S. Jaakkola

JAIR 2018 Grounding Language for Transfer in Deep Reinforcement Learning Karthik Narasimhan, Regina Barzilay, Tommi S. Jaakkola

AISTATS 2018 Structured Optimal Transport David Alvarez-Melis, Tommi S. Jaakkola, Stefanie Jegelka

UAI 2018 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

AISTATS 2017 Learning Optimal Interventions Jonas Mueller, David Reshef, George Du, Tommi S. Jaakkola

ICML 2017 Learning Sleep Stages from Radio Signals: A Conditional Adversarial Architecture Mingmin Zhao, Shichao Yue, Dina Katabi, Tommi S. Jaakkola, Matt T. Bianchi

ICLR 2017 Tree-Structured Decoding with Doubly-Recurrent Neural Networks David Alvarez-Melis, Tommi S. Jaakkola

AISTATS 2016 CRAFT: ClusteR-Specific Assorted Feature selecTion Vikas K. Garg, Cynthia Rudin, Tommi S. Jaakkola

UAI 2016 Structured Prediction: From Gaussian Perturbations to Linear-Time Principled Algorithms Jean Honorio, Tommi S. Jaakkola

AISTATS 2015 Metric Recovery from Directed Unweighted Graphs Tatsunori B. Hashimoto, Yi Sun, Tommi S. Jaakkola

AISTATS 2014 Active Boundary Annotation Using Random MAP Perturbations Subhransu Maji, Tamir Hazan, Tommi S. Jaakkola

AISTATS 2014 Learning with Maximum A-Posteriori Perturbation Models Andreea Gane, Tamir Hazan, Tommi S. Jaakkola

AISTATS 2014 Tight Bounds for the Expected Risk of Linear Classifiers and PAC-Bayes Finite-Sample Guarantees Jean Honorio, Tommi S. Jaakkola

UAI 2013 Inverse Covariance Estimation for High-Dimensional Data in Linear Time and Space: Spectral Methods for Riccati and Sparse Models Jean Honorio, Tommi S. Jaakkola

NeurIPS 2012 Convergence Rate Analysis of MAP Coordinate Minimization Algorithms Ofer Meshi, Amir Globerson, Tommi S. Jaakkola

ICML 2012 On the Partition Function and Random Maximum A-Posteriori Perturbations Tamir Hazan, Tommi S. Jaakkola

ICML 2010 Learning Efficiently with Approximate Inference via Dual Losses Ofer Meshi, David A. Sontag, Tommi S. Jaakkola, Amir Globerson

NeurIPS 2010 More Data Means Less Inference: A Pseudo-Max Approach to Structured Learning David Sontag, Ofer Meshi, Amir Globerson, Tommi S. Jaakkola

NeurIPS 2008 Clusters and Coarse Partitions in LP Relaxations David Sontag, Amir Globerson, Tommi S. Jaakkola

UAI 2008 Tightening LP Relaxations for MAP Using Message Passing David A. Sontag, Talya Meltzer, Amir Globerson, Tommi S. Jaakkola, Yair Weiss

UAI 2007 Convergent Propagation Algorithms via Oriented Trees Amir Globerson, Tommi S. Jaakkola

NeurIPS 2007 Fixing Max-Product: Convergent Message Passing Algorithms for MAP LP-Relaxations Amir Globerson, Tommi S. Jaakkola

NeurIPS 2007 New Outer Bounds on the Marginal Polytope David Sontag, Tommi S. Jaakkola

AISTATS 2007 Predictive Discretization During Model Selection Harald Steck, Tommi S. Jaakkola

NeurIPS 2006 Approximate Inference Using Planar Graph Decomposition Amir Globerson, Tommi S. Jaakkola

NeurIPS 2006 Game Theoretic Algorithms for Protein-DNA Binding Luis Pérez-breva, Luis E. Ortiz, Chen-hsiang Yeang, Tommi S. Jaakkola

NeurIPS 2006 Parameter Expanded Variational Bayesian Methods Tommi S. Jaakkola, Yuan Qi

AISTATS 2005 Focused Inference Romer Rosales, Tommi S. Jaakkola

NeurIPS 2004 Distributed Information Regularization on Graphs Adrian Corduneanu, Tommi S. Jaakkola

NeurIPS 2004 Generalization Error Bounds for Collaborative Prediction with Low-Rank Matrices Nathan Srebro, Noga Alon, Tommi S. Jaakkola

NeurIPS 2004 Maximum-Margin Matrix Factorization Nathan Srebro, Jason Rennie, Tommi S. Jaakkola

NeurIPS 2003 Bias-Corrected Bootstrap and Model Uncertainty Harald Steck, Tommi S. Jaakkola

NeurIPS 2003 Linear Dependent Dimensionality Reduction Nathan Srebro, Tommi S. Jaakkola

UAI 2003 On Information Regularization Adrian Corduneanu, Tommi S. Jaakkola

NeurIPS 2003 Online Learning of Non-Stationary Sequences Claire Monteleoni, Tommi S. Jaakkola

AISTATS 2003 Tree-Reweighted Belief Propagation Algorithms and Approximate ML Estimation by Pseudo-Moment Matching Martin J. Wainwright, Tommi S. Jaakkola, Alan S. Willsky

ICML 2003 Weighted Low-Rank Approximations Nathan Srebro, Tommi S. Jaakkola

UAI 2002 A New Class of Upper Bounds on the Log Partition Function Martin J. Wainwright, Tommi S. Jaakkola, Alan S. Willsky

UAI 2002 Continuation Methods for Mixing Heterogenous Sources Adrian Corduneanu, Tommi S. Jaakkola

NeurIPS 2002 Exact MAP Estimates by (Hyper)tree Agreement Martin J. Wainwright, Tommi S. Jaakkola, Alan S. Willsky

NeurIPS 2002 Information Regularization with Partially Labeled Data Martin Szummer, Tommi S. Jaakkola

NeurIPS 2002 On the Dirichlet Prior and Bayesian Regularization Harald Steck, Tommi S. Jaakkola

UAI 2002 Unsupervised Active Learning in Large Domains Harald Steck, Tommi S. Jaakkola

MLJ 2000 Convergence Results for Single-Step On-Policy Reinforcement-Learning Algorithms Satinder Singh, Tommi S. Jaakkola, Michael L. Littman, Csaba Szepesvári

UAI 2000 Feature Selection and Dualities in Maximum Entropy Discrimination Tony Jebara, Tommi S. Jaakkola

UAI 2000 Tractable Bayesian Learning of Tree Belief Networks Marina Meila, Tommi S. Jaakkola

MLJ 1999 An Introduction to Variational Methods for Graphical Models Michael I. Jordan, Zoubin Ghahramani, Tommi S. Jaakkola, Lawrence K. Saul

AISTATS 1999 Probabilistic Kernel Regression Models Tommi S. Jaakkola, David Haussler

JAIR 1999 Variational Probabilistic Inference and the QMR-DT Network Tommi S. Jaakkola, Michael I. Jordan

AISTATS 1997 A Variational Approach to Bayesian Logistic Regression Models and Their Extensions Tommi S. Jaakkola, Michael I. Jordan

UAI 1996 Computing Upper and Lower Bounds on Likelihoods in Intractable Networks Tommi S. Jaakkola, Michael I. Jordan

JAIR 1996 Mean Field Theory for Sigmoid Belief Networks Lawrence K. Saul, Tommi S. Jaakkola, Michael I. Jordan

ICML 1994 Learning Without State-Estimation in Partially Observable Markovian Decision Processes Satinder P. Singh, Tommi S. Jaakkola, Michael I. Jordan

NeCo 1994 On the Convergence of Stochastic Iterative Dynamic Programming Algorithms Tommi S. Jaakkola, Michael I. Jordan, Satinder P. Singh