Tommi S. Jaakkola

401 papers A* 96A 34B 3C 2Misc 2Journal 205Unranked 56

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Diamond Maps: Efficient Reward Alignment via Stochastic Flow Maps. CoRR Peter Holderrieth, Douglas Chen, Luca Eyring, Ishin Shah, Giri Anantharaman, Yutong He, Zeynep Akata, Tommi S. Jaakkola, Nicholas Matthew Boffi, Max Simchowitz
2026	J	`jnl`	FragmentFlow: Scalable Transition State Generation for Large Molecules. CoRR Ron Shprints, Peter Holderrieth, Juno Nam, Rafael Gómez-Bombarelli, Tommi S. Jaakkola
2026	J	`jnl`	Protein FID: improved evaluation of protein structure generative models. Bioinform. Felix Faltings, Hannes Stärk, Tommi S. Jaakkola, Regina Barzilay
2026	J	`jnl`	Rethinking Diffusion Models with Symmetries through Canonicalization with Applications to Molecular Graph Generation. CoRR Cai Zhou, Zijie Chen, Zian Li, Jike Wang, Kaiyi Jiang, Pan Li, Rose Yu, Muhan Zhang, Stephen Bates, Tommi S. Jaakkola
2026	J	`jnl`	Zatom-1: A Multimodal Flow Foundation Model for 3D Molecules and Materials. CoRR Alex Morehead, Miruna T. Cretu, Antonia Panescu, Rishabh Anand, Maurice Weiler, Tynan Perez, Samuel M. Blau, Steven Farrell, Wahid Bhimji, Anubhav Jain, Hrushikesh Sahasrabuddhe, Pietro Lio, Tommi S. Jaakkola, Rafael Gómez-Bombarelli, Rex Ying, N. Benjamin Erichson, Michael W. Mahoney
2025	J	`jnl`	AI-based Methods for Simulating, Sampling, and Predicting Protein Ensembles. CoRR Bowen Jing, Bonnie Berger, Tommi S. Jaakkola
2025	A*	`conf`	An Information Criterion for Controlled Disentanglement of Multimodal Data. ICLR Chenyu Wang, Sharut Gupta, Xinyi Zhang, Sana Tonekaboni, Stefanie Jegelka, Tommi S. Jaakkola, Caroline Uhler
2025	J	`jnl`	Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems. Found. Trends Mach. Learn. 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
2025	J	`jnl`	Coevolutionary Continuous Discrete Diffusion: Make Your Diffusion Language Model a Latent Reasoner. CoRR Cai Zhou, Chenxiao Yang, Yi Hu, Chenyu Wang, Chubin Zhang, Muhan Zhang, Lester Mackey, Tommi S. Jaakkola, Stephen Bates, Dinghuai Zhang
2025	A*	`conf`	Composing Unbalanced Flows for Flexible Docking and Relaxation. ICLR Gabriele Corso, Vignesh Ram Somnath, Noah Getz, Regina Barzilay, Tommi S. Jaakkola, Andreas Krause
2025	J	`jnl`	Continuously Tempered Diffusion Samplers. CoRR Ezra Erives, Bowen Jing, Peter Holderrieth, Tommi S. Jaakkola
2025	A*	`conf`	Data Distillation for extrapolative protein design through exact preference optimization. ICLR Mostafa Karimi, Sharmi Banerjee, Tommi S. Jaakkola, Bella Dubrov, Shang Shang, Ron Benson
2025	A*	`conf`	Fictitious Synthetic Data Can Improve LLM Factuality via Prerequisite Learning. ICLR Yujian Liu, Shiyu Chang, Tommi S. Jaakkola, Yang Zhang
2025	A*	`conf`	Fine-Tuning Discrete Diffusion Models via Reward Optimization with Applications to DNA and Protein Design. ICLR Chenyu Wang, Masatoshi Uehara, Yichun He, Amy Wang, Avantika Lal, Tommi S. Jaakkola, Sergey Levine, Aviv Regev, Hanchen Wang, Tommaso Biancalani
2025	J	`jnl`	GLASS Flows: Transition Sampling for Alignment of Flow and Diffusion Models. CoRR Peter Holderrieth, Uriel Singer, Tommi S. Jaakkola, Ricky T. Q. Chen, Yaron Lipman, Brian Karrer
2025	A*	`conf`	Generator Matching: Generative modeling with arbitrary Markov processes. ICLR Peter Holderrieth, Marton Havasi, Jason Yim, Neta Shaul, Itai Gat, Tommi S. Jaakkola, Brian Karrer, Ricky T. Q. Chen, Yaron Lipman
2025	J	`jnl`	HarnessLLM: Automatic Testing Harness Generation via Reinforcement Learning. CoRR Yujian Liu, Jiabao Ji, Yang Zhang, Wenbo Guo, Tommi S. Jaakkola, Shiyu Chang
2025	A*	`conf`	Identifying biological perturbation targets through causal differential networks. ICML Menghua Wu, Umesh Padia, Sean H. Murphy, Regina Barzilay, Tommi S. Jaakkola
2025	J	`jnl`	Implicit Augmentation from Distributional Symmetry in Turbulence Super-Resolution. CoRR Julia Balla, Jeremiah Bailey, Ali Backour, Elyssa F. Hofgard, Tommi S. Jaakkola, Tess E. Smidt, Ryley McConkey
2025	J	`jnl`	Inference-Time Scaling for Diffusion Models beyond Scaling Denoising Steps. CoRR 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	A*	`conf`	LEAPS: A discrete neural sampler via locally equivariant networks. ICML Peter Holderrieth, Michael Samuel Albergo, Tommi S. Jaakkola
2025	J	`jnl`	LEAPS: A discrete neural sampler via locally equivariant networks. CoRR Peter Holderrieth, Michael S. Albergo, Tommi S. Jaakkola
2025	J	`jnl`	Learning Diffusion Models with Flexible Representation Guidance. CoRR Chenyu Wang, Cai Zhou, Sharut Gupta, Zongyu Lin, Stefanie Jegelka, Stephen Bates, Tommi S. Jaakkola
2025	J	`jnl`	Next Semantic Scale Prediction via Hierarchical Diffusion Language Models. CoRR Cai Zhou, Chenyu Wang, Dinghuai Zhang, Shangyuan Tong, Yifei Wang, Stephen Bates, Tommi S. Jaakkola
2025	A*	`conf`	ProtComposer: Compositional Protein Structure Generation with 3D Ellipsoids. ICLR Hannes Stärk, Bowen Jing, Tomas Geffner, Jason Yim, Tommi S. Jaakkola, Arash Vahdat, Karsten Kreis
2025	J	`jnl`	ProxelGen: Generating Proteins as 3D Densities. CoRR Felix Faltings, Hannes Stärk, Regina Barzilay, Tommi S. Jaakkola
2025	J	`jnl`	SPG: Sandwiched Policy Gradient for Masked Diffusion Language Models. CoRR Chenyu Wang, Paria Rashidinejad, DiJia Su, Song Jiang, Sid Wang, Siyan Zhao, Cai Zhou, Shannon Zejiang Shen, Feiyu Chen, Tommi S. Jaakkola, Yuandong Tian, Bo Liu
2025	J	`jnl`	Sample, estimate, aggregate: A recipe for causal discovery foundation models. Trans. Mach. Learn. Res. Menghua Wu, Yujia Bao, Regina Barzilay, Tommi S. Jaakkola
2025	A*	`conf`	Scaling Inference Time Compute for Diffusion Models. CVPR 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	A*	`conf`	Think while You Generate: Discrete Diffusion with Planned Denoising. ICLR Sulin Liu, Juno Nam, Andrew Campbell, Hannes Stärk, Yilun Xu, Tommi S. Jaakkola, Rafael Gómez-Bombarelli
2025	A*	`conf`	Thought calibration: Efficient and confident test-time scaling. EMNLP Menghua Wu, Cai Zhou, Stephen Bates, Tommi S. Jaakkola
2025	J	`jnl`	Thought calibration: Efficient and confident test-time scaling. CoRR Menghua Wu, Cai Zhou, Stephen Bates, Tommi S. Jaakkola
2024	J	`jnl`	A Cosmic-Scale Benchmark for Symmetry-Preserving Data Processing. CoRR Julia Balla, Siddharth Mishra-Sharma, Carolina Cuesta-Lázaro, Tommi S. Jaakkola, Tess E. Smidt
2024	A*	`conf`	A Recipe for Charge Density Prediction. NeurIPS Xiang Fu, Andrew S. Rosen, Kyle Bystrom, Rui Wang, Albert Musaelian, Boris Kozinsky, Tess E. Smidt, Tommi S. Jaakkola
2024	J	`jnl`	A Recipe for Charge Density Prediction. CoRR Xiang Fu, Andrew S. Rosen, Kyle Bystrom, Rui Wang, Albert Musaelian, Boris Kozinsky, Tess E. Smidt, Tommi S. Jaakkola
2024	A*	`conf`	AlphaFold Meets Flow Matching for Generating Protein Ensembles. ICML Bowen Jing, Bonnie Berger, Tommi S. Jaakkola
2024	J	`jnl`	AlphaFold Meets Flow Matching for Generating Protein Ensembles. CoRR Bowen Jing, Bonnie Berger, Tommi S. Jaakkola
2024	J	`jnl`	An Information Criterion for Controlled Disentanglement of Multimodal Data. CoRR Chenyu Wang, Sharut Gupta, Xinyi Zhang, Sana Tonekaboni, Stefanie Jegelka, Tommi S. Jaakkola, Caroline Uhler
2024	A*	`conf`	Conformal Language Modeling. ICLR Victor Quach, Adam Fisch, Tal Schuster, Adam Yala, Jae Ho Sohn, Tommi S. Jaakkola, Regina Barzilay
2024	A*	`conf`	Correcting Diffusion Generation Through Resampling. CVPR Yujian Liu, Yang Zhang, Tommi S. Jaakkola, Shiyu Chang
2024	A*	`conf`	Deep Confident Steps to New Pockets: Strategies for Docking Generalization. ICLR Gabriele Corso, Arthur Deng, Nicholas Polizzi, Regina Barzilay, Tommi S. Jaakkola
2024	J	`jnl`	Deep Confident Steps to New Pockets: Strategies for Docking Generalization. CoRR Gabriele Corso, Arthur Deng, Benjamin Fry, Nicholas Polizzi, Regina Barzilay, Tommi S. Jaakkola
2024	A*	`conf`	Dirichlet Flow Matching with Applications to DNA Sequence Design. ICML Hannes Stärk, Bowen Jing, Chenyu Wang, Gabriele Corso, Bonnie Berger, Regina Barzilay, Tommi S. Jaakkola
2024	J	`jnl`	Dirichlet Flow Matching with Applications to DNA Sequence Design. CoRR Hannes Stärk, Bowen Jing, Chenyu Wang, Gabriele Corso, Bonnie Berger, Regina Barzilay, Tommi S. Jaakkola
2024	A*	`conf`	DisCo-Diff: Enhancing Continuous Diffusion Models with Discrete Latents. ICML Yilun Xu, Gabriele Corso, Tommi S. Jaakkola, Arash Vahdat, Karsten Kreis
2024	J	`jnl`	DisCo-Diff: Enhancing Continuous Diffusion Models with Discrete Latents. CoRR Yilun Xu, Gabriele Corso, Tommi S. Jaakkola, Arash Vahdat, Karsten Kreis
2024	A*	`conf`	Equivariant Scalar Fields for Molecular Docking with Fast Fourier Transforms. ICLR Bowen Jing, Tommi S. Jaakkola, Bonnie Berger
2024	J	`jnl`	Fictitious Synthetic Data Can Improve LLM Factuality via Prerequisite Learning. CoRR Yujian Liu, Shiyu Chang, Tommi S. Jaakkola, Yang Zhang
2024	J	`jnl`	Fine-Tuning Discrete Diffusion Models via Reward Optimization with Applications to DNA and Protein Design. CoRR Chenyu Wang, Masatoshi Uehara, Yichun He, Amy Wang, Tommaso Biancalani, Avantika Lal, Tommi S. Jaakkola, Sergey Levine, Hanchen Wang, Aviv Regev
2024	A*	`conf`	Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design. ICML Andrew Campbell, Jason Yim, Regina Barzilay, Tom Rainforth, Tommi S. Jaakkola
2024	J	`jnl`	Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design. CoRR Andrew Campbell, Jason Yim, Regina Barzilay, Tom Rainforth, Tommi S. Jaakkola
2024	A*	`conf`	Generative Modeling of Molecular Dynamics Trajectories. NeurIPS Bowen Jing, Hannes Stärk, Tommi S. Jaakkola, Bonnie Berger
2024	J	`jnl`	Generative Modeling of Molecular Dynamics Trajectories. CoRR Bowen Jing, Hannes Stärk, Tommi S. Jaakkola, Bonnie Berger
2024	J	`jnl`	Generator Matching: Generative modeling with arbitrary Markov processes. CoRR Peter Holderrieth, Marton Havasi, Jason Yim, Neta Shaul, Itai Gat, Tommi S. Jaakkola, Brian Karrer, Ricky T. Q. Chen, Yaron Lipman
2024	A*	`conf`	Hamiltonian Score Matching and Generative Flows. NeurIPS Peter Holderrieth, Yilun Xu, Tommi S. Jaakkola
2024	J	`jnl`	Hamiltonian Score Matching and Generative Flows. CoRR Peter Holderrieth, Yilun Xu, Tommi S. Jaakkola
2024	A*	`conf`	Harmonic Self-Conditioned Flow Matching for joint Multi-Ligand Docking and Binding Site Design. ICML Hannes Stärk, Bowen Jing, Regina Barzilay, Tommi S. Jaakkola
2024	J	`jnl`	Improved motif-scaffolding with SE(3) flow matching. CoRR Jason Yim, Andrew Campbell, Emile Mathieu, Andrew Y. K. Foong, Michael Gastegger, José Jiménez-Luna, Sarah Lewis, Victor Garcia Satorras, Bastiaan S. Veeling, Frank Noé, Regina Barzilay, Tommi S. Jaakkola
2024	J	`jnl`	Improved motif-scaffolding with SE(3) flow matching. Trans. Mach. Learn. Res. Jason Yim, Andrew Campbell, Emile Mathieu, Andrew Y. K. Foong, Michael Gastegger, José Jiménez-Luna, Sarah Lewis, Victor Garcia Satorras, Bastiaan S. Veeling, Frank Noé, Regina Barzilay, Tommi S. Jaakkola
2024	A*	`conf`	Improving protein optimization with smoothed fitness landscapes. ICLR Andrew Kirjner, Jason Yim, Raman Samusevich, Shahar Bracha, Tommi S. Jaakkola, Regina Barzilay, Ila R. Fiete
2024	A*	`conf`	In-Context Symmetries: Self-Supervised Learning through Contextual World Models. NeurIPS Sharut Gupta, Chenyu Wang, Yifei Wang, Tommi S. Jaakkola, Stefanie Jegelka
2024	J	`jnl`	In-Context Symmetries: Self-Supervised Learning through Contextual World Models. CoRR Sharut Gupta, Chenyu Wang, Yifei Wang, Tommi S. Jaakkola, Stefanie Jegelka
2024	A*	`conf`	MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design. ICLR Xiang Fu, Tian Xie, Andrew S. Rosen, Tommi S. Jaakkola, Jake Smith
2024	A*	`conf`	Neural Network Reparametrization for Accelerated Optimization in Molecular Simulations. NeurIPS Nima Dehmamy, Csaba Both, Jeet Mohapatra, Subhro Das, Tommi S. Jaakkola
2024	A*	`conf`	Particle Guidance: non-I.I.D. Diverse Sampling with Diffusion Models. ICLR Gabriele Corso, Yilun Xu, Valentin De Bortoli, Regina Barzilay, Tommi S. Jaakkola
2024	J	`jnl`	Predicting perturbation targets with causal differential networks. CoRR Menghua Wu, Umesh Padia, Sean H. Murphy, Regina Barzilay, Tommi S. Jaakkola
2024	A*	`conf`	Removing Biases from Molecular Representations via Information Maximization. ICLR Chenyu Wang, Sharut Gupta, Caroline Uhler, Tommi S. Jaakkola
2024	A*	`conf`	Revisiting Who's Harry Potter: Towards Targeted Unlearning from a Causal Intervention Perspective. EMNLP Yujian Liu, Yang Zhang, Tommi S. Jaakkola, Shiyu Chang
2024	J	`jnl`	Revisiting Who's Harry Potter: Towards Targeted Unlearning from a Causal Intervention Perspective. CoRR Yujian Liu, Yang Zhang, Tommi S. Jaakkola, Shiyu Chang
2024	J	`jnl`	Risk-Controlling Model Selection via Guided Bayesian Optimization. Trans. Mach. Learn. Res. Bracha Laufer-Goldshtein, Adam Fisch, Regina Barzilay, Tommi S. Jaakkola
2024	J	`jnl`	Sample, estimate, aggregate: A recipe for causal discovery foundation models. CoRR Menghua Wu, Yujia Bao, Regina Barzilay, Tommi S. Jaakkola
2024	J	`jnl`	Structural Constraint Integration in Generative Model for Discovery of Quantum Material Candidates. CoRR Ryotaro Okabe, Mouyang Cheng, Abhijatmedhi Chotrattanapituk, Nguyen Tuan Hung, Xiang Fu, Bowen Han, Yao Wang, Weiwei Xie, Robert J. Cava, Tommi S. Jaakkola, Yongqiang Cheng, Mingda Li
2024	J	`jnl`	Think While You Generate: Discrete Diffusion with Planned Denoising. CoRR Sulin Liu, Juno Nam, Andrew Campbell, Hannes Stärk, Yilun Xu, Tommi S. Jaakkola, Rafael Gómez-Bombarelli
2024	J	`jnl`	Verlet Flows: Exact-Likelihood Integrators for Flow-Based Generative Models. CoRR Ezra Erives, Bowen Jing, Tommi S. Jaakkola
2024	J	`jnl`	Virtual node graph neural network for full phonon prediction. Nat. Comput. Sci. Ryotaro Okabe, Abhijatmedhi Chotrattanapituk, Artittaya Boonkird, Nina Andrejevic, Xiang Fu, Tommi S. Jaakkola, Qichen Song, Thanh Nguyen, Nathan C. Drucker, Sai Mu, Yao Wang, Bolin Liao, Yongqiang Cheng, Mingda Li
2023	J	`jnl`	Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems. CoRR 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, Haiyang Yu, Yuqing Xie, Xiang Fu, Alex Strasser, 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
2023	A*	`conf`	Compositional Foundation Models for Hierarchical Planning. NeurIPS Anurag Ajay, Seungwook Han, Yilun Du, Shuang Li, Abhi Gupta, Tommi S. Jaakkola, Joshua B. Tenenbaum, Leslie Pack Kaelbling, Akash Srivastava, Pulkit Agrawal
2023	J	`jnl`	Compositional Foundation Models for Hierarchical Planning. CoRR Anurag Ajay, Seungwook Han, Yilun Du, Shuang Li, Abhi Gupta, Tommi S. Jaakkola, Josh Tenenbaum, Leslie Pack Kaelbling, Akash Srivastava, Pulkit Agrawal
2023	A*	`conf`	Compositional Sculpting of Iterative Generative Processes. NeurIPS Timur Garipov, Sebastiaan De Peuter, Ge Yang, Vikas Garg, Samuel Kaski, Tommi S. Jaakkola
2023	J	`jnl`	Compositional Sculpting of Iterative Generative Processes. CoRR Timur Garipov, Sebastiaan De Peuter, Ge Yang, Vikas Garg, Samuel Kaski, Tommi S. Jaakkola
2023	J	`jnl`	Conformal Language Modeling. CoRR Victor Quach, Adam Fisch, Tal Schuster, Adam Yala, Jae Ho Sohn, Tommi S. Jaakkola, Regina Barzilay
2023	J	`jnl`	Correcting Diffusion Generation through Resampling. CoRR Yujian Liu, Yang Zhang, Tommi S. Jaakkola, Shiyu Chang
2023	J	`jnl`	DiffDock-PP: Rigid Protein-Protein Docking with Diffusion Models. CoRR Mohamed Amine Ketata, Cedrik Laue, Ruslan Mammadov, Hannes Stärk, Menghua Wu, Gabriele Corso, Céline Marquet, Regina Barzilay, Tommi S. Jaakkola
2023	A*	`conf`	DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking. ICLR Gabriele Corso, Hannes Stärk, Bowen Jing, Regina Barzilay, Tommi S. Jaakkola
2023	A*	`conf`	Diffusion Probabilistic Modeling of Protein Backbones in 3D for the motif-scaffolding problem. ICLR Brian L. Trippe, Jason Yim, Doug Tischer, David Baker, Tamara Broderick, Regina Barzilay, Tommi S. Jaakkola
2023	A*	`conf`	Efficiently Controlling Multiple Risks with Pareto Testing. ICLR Bracha Laufer-Goldshtein, Adam Fisch, Regina Barzilay, Tommi S. Jaakkola
2023	J	`jnl`	EigenFold: Generative Protein Structure Prediction with Diffusion Models. CoRR Bowen Jing, Ezra Erives, Peter Pao-Huang, Gabriele Corso, Bonnie Berger, Tommi S. Jaakkola
2023	J	`jnl`	Equivariant Scalar Fields for Molecular Docking with Fast Fourier Transforms. CoRR Bowen Jing, Tommi S. Jaakkola, Bonnie Berger
2023	J	`jnl`	Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations. Trans. Mach. Learn. Res. Xiang Fu, Zhenghao Wu, Wujie Wang, Tian Xie, Sinan Keten, Rafael Gómez-Bombarelli, Tommi S. Jaakkola
2023	J	`jnl`	GenPhys: From Physical Processes to Generative Models. CoRR Ziming Liu, Di Luo, Yilun Xu, Tommi S. Jaakkola, Max Tegmark
2023	J	`jnl`	Harmonic Self-Conditioned Flow Matching for Multi-Ligand Docking and Binding Site Design. CoRR Hannes Stärk, Bowen Jing, Regina Barzilay, Tommi S. Jaakkola
2023	A*	`conf`	Is Conditional Generative Modeling all you need for Decision Making? ICLR Anurag Ajay, Yilun Du, Abhi Gupta, Joshua B. Tenenbaum, Tommi S. Jaakkola, Pulkit Agrawal
2023	J	`jnl`	Learning Interatomic Potentials at Multiple Scales. CoRR Xiang Fu, Albert Musaelian, Anders Johansson, Tommi S. Jaakkola, Boris Kozinsky
2023	J	`jnl`	MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design. CoRR Xiang Fu, Tian Xie, Andrew S. Rosen, Tommi S. Jaakkola, Jake Smith
2023	J	`jnl`	Optimizing protein fitness using Gibbs sampling with Graph-based Smoothing. CoRR Andrew Kirjner, Jason Yim, Raman Samusevich, Tommi S. Jaakkola, Regina Barzilay, Ila Fiete
2023	J	`jnl`	PEOPL: Characterizing Privately Encoded Open Datasets with Public Labels. CoRR Homa Esfahanizadeh, Adam Yala, Rafael G. L. D'Oliveira, Andrea J. D. Jaba, Victor Quach, Ken R. Duffy, Tommi S. Jaakkola, Vinod Vaikuntanathan, Manya Ghobadi, Regina Barzilay, Muriel Médard
2023	A*	`conf`	PFGM++: Unlocking the Potential of Physics-Inspired Generative Models. ICML Yilun Xu, Ziming Liu, Yonglong Tian, Shangyuan Tong, Max Tegmark, Tommi S. Jaakkola
2023	J	`jnl`	PFGM++: Unlocking the Potential of Physics-Inspired Generative Models. CoRR Yilun Xu, Ziming Liu, Yonglong Tian, Shangyuan Tong, Max Tegmark, Tommi S. Jaakkola
2023	J	`jnl`	Particle Guidance: non-I.I.D. Diverse Sampling with Diffusion Models. CoRR Gabriele Corso, Yilun Xu, Valentin De Bortoli, Regina Barzilay, Tommi S. Jaakkola
2023	J	`jnl`	Removing Biases from Molecular Representations via Information Maximization. CoRR Chenyu Wang, Sharut Gupta, Caroline Uhler, Tommi S. Jaakkola
2023	A*	`conf`	Restart Sampling for Improving Generative Processes. NeurIPS Yilun Xu, Mingyang Deng, Xiang Cheng, Yonglong Tian, Ziming Liu, Tommi S. Jaakkola
2023	J	`jnl`	Restart Sampling for Improving Generative Processes. CoRR Yilun Xu, Mingyang Deng, Xiang Cheng, Yonglong Tian, Ziming Liu, Tommi S. Jaakkola
2023	J	`jnl`	Risk-Controlling Model Selection via Guided Bayesian Optimization. CoRR Bracha Laufer-Goldshtein, Adam Fisch, Regina Barzilay, Tommi S. Jaakkola
2023	A*	`conf`	SE(3) diffusion model with application to protein backbone generation. ICML Jason Yim, Brian L. Trippe, Valentin De Bortoli, Emile Mathieu, Arnaud Doucet, Regina Barzilay, Tommi S. Jaakkola
2023	J	`jnl`	SE(3) diffusion model with application to protein backbone generation. CoRR Jason Yim, Brian L. Trippe, Valentin De Bortoli, Emile Mathieu, Arnaud Doucet, Regina Barzilay, Tommi S. Jaakkola
2023	J	`jnl`	Simulate Time-integrated Coarse-grained Molecular Dynamics with Multi-scale Graph Networks. Trans. Mach. Learn. Res. Xiang Fu, Tian Xie, Nathan J. Rebello, Bradley D. Olsen, Tommi S. Jaakkola
2023	A*	`conf`	Stable Target Field for Reduced Variance Score Estimation in Diffusion Models. ICLR Yilun Xu, Shangyuan Tong, Tommi S. Jaakkola
2023	J	`jnl`	Stable Target Field for Reduced Variance Score Estimation in Diffusion Models. CoRR Yilun Xu, Shangyuan Tong, Tommi S. Jaakkola
2023	A*	`conf`	Towards Coherent Image Inpainting Using Denoising Diffusion Implicit Models. ICML Guanhua Zhang, Jiabao Ji, Yang Zhang, Mo Yu, Tommi S. Jaakkola, Shiyu Chang
2023	J	`jnl`	Towards Coherent Image Inpainting Using Denoising Diffusion Implicit Models. CoRR Guanhua Zhang, Jiabao Ji, Yang Zhang, Mo Yu, Tommi S. Jaakkola, Shiyu Chang
2022	A*	`conf`	Adversarial Support Alignment. ICLR Shangyuan Tong, Timur Garipov, Yang Zhang, Shiyu Chang, Tommi S. Jaakkola
2022	J	`jnl`	Adversarial Support Alignment. CoRR Shangyuan Tong, Timur Garipov, Yang Zhang, Shiyu Chang, Tommi S. Jaakkola
2022	J	`jnl`	Antibody-Antigen Docking and Design via Hierarchical Equivariant Refinement. CoRR Wengong Jin, Regina Barzilay, Tommi S. Jaakkola
2022	A*	`conf`	Antibody-Antigen Docking and Design via Hierarchical Structure Refinement. ICML Wengong Jin, Regina Barzilay, Tommi S. Jaakkola
2022	J	`jnl`	Calibrated Selective Classification. CoRR Adam Fisch, Tommi S. Jaakkola, Regina Barzilay
2022	J	`jnl`	Calibrated Selective Classification. Trans. Mach. Learn. Res. Adam Fisch, Tommi S. Jaakkola, Regina Barzilay
2022	A*	`conf`	Conformal Prediction Sets with Limited False Positives. ICML Adam Fisch, Tal Schuster, Tommi S. Jaakkola, Regina Barzilay
2022	J	`jnl`	Conformal Prediction Sets with Limited False Positives. CoRR Adam Fisch, Tal Schuster, Tommi S. Jaakkola, Regina Barzilay
2022	A*	`conf`	Controlling Directions Orthogonal to a Classifier. ICLR Yilun Xu, Hao He, Tianxiao Shen, Tommi S. Jaakkola
2022	J	`jnl`	Controlling Directions Orthogonal to a Classifier. CoRR Yilun Xu, Hao He, Tianxiao Shen, Tommi S. Jaakkola
2022	A*	`conf`	Crystal Diffusion Variational Autoencoder for Periodic Material Generation. ICLR Tian Xie, Xiang Fu, Octavian-Eugen Ganea, Regina Barzilay, Tommi S. Jaakkola
2022	J	`jnl`	DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking. CoRR Gabriele Corso, Hannes Stärk, Bowen Jing, Regina Barzilay, Tommi S. Jaakkola
2022	J	`jnl`	Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem. CoRR Brian L. Trippe, Jason Yim, Doug Tischer, David Baker, Tamara Broderick, Regina Barzilay, Tommi S. Jaakkola
2022	J	`jnl`	Efficiently Controlling Multiple Risks with Pareto Testing. CoRR Bracha Laufer-Goldshtein, Adam Fisch, Regina Barzilay, Tommi S. Jaakkola
2022	A*	`conf`	EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction. ICML Hannes Stärk, Octavian Ganea, Lagnajit Pattanaik, Regina Barzilay, Tommi S. Jaakkola
2022	J	`jnl`	EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction. CoRR Hannes Stärk, Octavian-Eugen Ganea, Lagnajit Pattanaik, Regina Barzilay, Tommi S. Jaakkola
2022	J	`jnl`	Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations. CoRR Xiang Fu, Zhenghao Wu, Wujie Wang, Tian Xie, Sinan Keten, Rafael Gómez-Bombarelli, Tommi S. Jaakkola
2022	J	`jnl`	Fundamental Limits and Tradeoffs in Invariant Representation Learning. J. Mach. Learn. Res. Han Zhao, Chen Dan, Bryon Aragam, Tommi S. Jaakkola, Geoffrey J. Gordon, Pradeep Ravikumar
2022	A*	`conf`	Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking. ICLR Octavian-Eugen Ganea, Xinyuan Huang, Charlotte Bunne, Yatao Bian, Regina Barzilay, Tommi S. Jaakkola, Andreas Krause
2022	J	`jnl`	Is Conditional Generative Modeling all you need for Decision-Making? CoRR Anurag Ajay, Yilun Du, Abhi Gupta, Joshua B. Tenenbaum, Tommi S. Jaakkola, Pulkit Agrawal
2022	A*	`conf`	Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design. ICLR Wengong Jin, Jeremy Wohlwend, Regina Barzilay, Tommi S. Jaakkola
2022	A*	`conf`	Poisson Flow Generative Models. NeurIPS Yilun Xu, Ziming Liu, Max Tegmark, Tommi S. Jaakkola
2022	J	`jnl`	Poisson Flow Generative Models. CoRR Yilun Xu, Ziming Liu, Max Tegmark, Tommi S. Jaakkola
2022	J	`jnl`	Simulate Time-integrated Coarse-grained Molecular Dynamics with Geometric Machine Learning. CoRR Xiang Fu, Tian Xie, Nathan J. Rebello, Bradley D. Olsen, Tommi S. Jaakkola
2022	—	`conf`	Subspace Diffusion Generative Models. ECCV (23) Bowen Jing, Gabriele Corso, Renato Berlinghieri, Tommi S. Jaakkola
2022	J	`jnl`	Subspace Diffusion Generative Models. CoRR Bowen Jing, Gabriele Corso, Renato Berlinghieri, Tommi S. Jaakkola
2022	J	`jnl`	Syfer: Neural Obfuscation for Private Data Release. CoRR Adam Yala, Victor Quach, Homa Esfahanizadeh, Rafael G. L. D'Oliveira, Ken R. Duffy, Muriel Médard, Tommi S. Jaakkola, Regina Barzilay
2022	A*	`conf`	Torsional Diffusion for Molecular Conformer Generation. NeurIPS Bowen Jing, Gabriele Corso, Jeffrey Chang, Regina Barzilay, Tommi S. Jaakkola
2022	J	`jnl`	Torsional Diffusion for Molecular Conformer Generation. CoRR Bowen Jing, Gabriele Corso, Jeffrey Chang, Regina Barzilay, Tommi S. Jaakkola
2021	—	`conf`	Consistent Accelerated Inference via Confident Adaptive Transformers. EMNLP (1) Tal Schuster, Adam Fisch, Tommi S. Jaakkola, Regina Barzilay
2021	J	`jnl`	Consistent Accelerated Inference via Confident Adaptive Transformers. CoRR Tal Schuster, Adam Fisch, Tommi S. Jaakkola, Regina Barzilay
2021	J	`jnl`	Crystal Diffusion Variational Autoencoder for Periodic Material Generation. CoRR Tian Xie, Xiang Fu, Octavian-Eugen Ganea, Regina Barzilay, Tommi S. Jaakkola
2021	J	`jnl`	Deep learning identifies synergistic drug combinations for treating COVID-19. Proc. Natl. Acad. Sci. USA Wengong Jin, Jonathan M. Stokes, Richard T. Eastman, Zina Itkin, Alexey V. Zakharov, James J. Collins, Tommi S. Jaakkola, Regina Barzilay
2021	A*	`conf`	Efficient Conformal Prediction via Cascaded Inference with Expanded Admission. ICLR Adam Fisch, Tal Schuster, Tommi S. Jaakkola, Regina Barzilay
2021	A*	`conf`	Few-Shot Conformal Prediction with Auxiliary Tasks. ICML Adam Fisch, Tal Schuster, Tommi S. Jaakkola, Regina Barzilay
2021	J	`jnl`	Few-shot Conformal Prediction with Auxiliary Tasks. CoRR Adam Fisch, Tal Schuster, Tommi S. Jaakkola, Regina Barzilay
2021	J	`jnl`	Fragment-based Sequential Translation for Molecular Optimization. CoRR Benson Chen, Xiang Fu, Regina Barzilay, Tommi S. Jaakkola
2021	A*	`conf`	GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles. NeurIPS Octavian Ganea, Lagnajit Pattanaik, Connor W. Coley, Regina Barzilay, Klavs F. Jensen, William H. Green Jr., Tommi S. Jaakkola
2021	J	`jnl`	GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles. CoRR Octavian-Eugen Ganea, Lagnajit Pattanaik, Connor W. Coley, Regina Barzilay, Klavs F. Jensen, William H. Green Jr., Tommi S. Jaakkola
2021	J	`jnl`	Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking. CoRR Octavian-Eugen Ganea, Xinyuan Huang, Charlotte Bunne, Yatao Bian, Regina Barzilay, Tommi S. Jaakkola, Andreas Krause
2021	A*	`conf`	Information Obfuscation of Graph Neural Networks. ICML Peiyuan Liao, Han Zhao, Keyulu Xu, Tommi S. Jaakkola, Geoffrey J. Gordon, Stefanie Jegelka, Ruslan Salakhutdinov
2021	J	`jnl`	Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design. CoRR Wengong Jin, Jeremy Wohlwend, Regina Barzilay, Tommi S. Jaakkola
2021	J	`jnl`	Learning Representations that Support Robust Transfer of Predictors. CoRR Yilun Xu, Tommi S. Jaakkola
2021	A*	`conf`	Learning Task Informed Abstractions. ICML Xiang Fu, Ge Yang, Pulkit Agrawal, Tommi S. Jaakkola
2021	J	`jnl`	Learning Task Informed Abstractions. CoRR Xiang Fu, Ge Yang, Pulkit Agrawal, Tommi S. Jaakkola
2021	A*	`conf`	Mol2Image: Improved Conditional Flow Models for Molecule to Image Synthesis. CVPR Karren D. Yang, Samuel Goldman, Wengong Jin, Alex X. Lu, Regina Barzilay, Tommi S. Jaakkola, Caroline Uhler
2021	J	`jnl`	NeuraCrypt: Hiding Private Health Data via Random Neural Networks for Public Training. CoRR Adam Yala, Homa Esfahanizadeh, Rafael G. L. D'Oliveira, Ken R. Duffy, Manya Ghobadi, Tommi S. Jaakkola, Vinod Vaikuntanathan, Regina Barzilay, Muriel Médard
2021	A*	`conf`	Understanding Interlocking Dynamics of Cooperative Rationalization. NeurIPS Mo Yu, Yang Zhang, Shiyu Chang, Tommi S. Jaakkola
2021	J	`jnl`	Understanding Interlocking Dynamics of Cooperative Rationalization. CoRR Mo Yu, Yang Zhang, Shiyu Chang, Tommi S. Jaakkola
2020	J	`jnl`	Adaptive Invariance for Molecule Property Prediction. CoRR Wengong Jin, Regina Barzilay, Tommi S. Jaakkola
2020	—	`conf`	Blank Language Models. EMNLP (1) Tianxiao Shen, Victor Quach, Regina Barzilay, Tommi S. Jaakkola
2020	J	`jnl`	Blank Language Models. CoRR Tianxiao Shen, Victor Quach, Regina Barzilay, Tommi S. Jaakkola
2020	J	`jnl`	Composing Molecules with Multiple Property Constraints. CoRR Wengong Jin, Regina Barzilay, Tommi S. Jaakkola
2020	J	`jnl`	Discovering Synergistic Drug Combinations for COVID with Biological Bottleneck Models. CoRR Wengong Jin, Regina Barzilay, Tommi S. Jaakkola
2020	J	`jnl`	Domain Extrapolation via Regret Minimization. CoRR Wengong Jin, Regina Barzilay, Tommi S. Jaakkola
2020	A*	`conf`	Educating Text Autoencoders: Latent Representation Guidance via Denoising. ICML Tianxiao Shen, Jonas Mueller, Regina Barzilay, Tommi S. Jaakkola
2020	J	`jnl`	Fundamental Limits and Tradeoffs in Invariant Representation Learning. CoRR Han Zhao, Chen Dan, Bryon Aragam, Tommi S. Jaakkola, Geoffrey J. Gordon, Pradeep Ravikumar
2020	A*	`conf`	Generalization and Representational Limits of Graph Neural Networks. ICML Vikas K. Garg, Stefanie Jegelka, Tommi S. Jaakkola
2020	J	`jnl`	Generalization and Representational Limits of Graph Neural Networks. CoRR Vikas K. Garg, Stefanie Jegelka, Tommi S. Jaakkola
2020	J	`jnl`	Graph Adversarial Networks: Protecting Information against Adversarial Attacks. CoRR Peiyuan Liao, Han Zhao, Keyulu Xu, Tommi S. Jaakkola, Geoffrey J. Gordon, Stefanie Jegelka, Ruslan Salakhutdinov
2020	A*	`conf`	Hierarchical Generation of Molecular Graphs using Structural Motifs. ICML Wengong Jin, Regina Barzilay, Tommi S. Jaakkola
2020	J	`jnl`	Hierarchical Generation of Molecular Graphs using Structural Motifs. CoRR Wengong Jin, Regina Barzilay, Tommi S. Jaakkola
2020	J	`jnl`	Improved Conditional Flow Models for Molecule to Image Synthesis. CoRR Karren D. Yang, Samuel Goldman, Wengong Jin, Alex Lu, Regina Barzilay, Tommi S. Jaakkola, Caroline Uhler
2020	A*	`conf`	Improving Molecular Design by Stochastic Iterative Target Augmentation. ICML Kevin Yang, Wengong Jin, Kyle Swanson, Regina Barzilay, Tommi S. Jaakkola
2020	J	`jnl`	Improving Molecular Design by Stochastic Iterative Target Augmentation. CoRR Kevin Yang, Wengong Jin, Kyle Swanson, Regina Barzilay, Tommi S. Jaakkola
2020	A*	`conf`	Invariant Rationalization. ICML Shiyu Chang, Yang Zhang, Mo Yu, Tommi S. Jaakkola
2020	J	`jnl`	Invariant Rationalization. CoRR Shiyu Chang, Yang Zhang, Mo Yu, Tommi S. Jaakkola
2020	A*	`conf`	Multi-Objective Molecule Generation using Interpretable Substructures. ICML Wengong Jin, Regina Barzilay, Tommi S. Jaakkola
2020	A*	`conf`	Oblique Decision Trees from Derivatives of ReLU Networks. ICLR Guang-He Lee, Tommi S. Jaakkola
2020	J	`jnl`	Optimal Transport Graph Neural Networks. CoRR Gary Bécigneul, Octavian-Eugen Ganea, Benson Chen, Regina Barzilay, Tommi S. Jaakkola
2020	A*	`conf`	Predicting deliberative outcomes. ICML Vikas K. Garg, Tommi S. Jaakkola
2020	J	`jnl`	Relaxed Conformal Prediction Cascades for Efficient Inference Over Many Labels. CoRR Adam Fisch, Tal Schuster, Tommi S. Jaakkola, Regina Barzilay
2020	A*	`conf`	Self-Supervised Learning of Appliance Usage. ICLR Chen-Yu Hsu, Abbas Zeitoun, Guang-He Lee, Dina Katabi, Tommi S. Jaakkola
2020	J	`jnl`	The Benefits of Pairwise Discriminators for Adversarial Training. CoRR Shangyuan Tong, Timur Garipov, Tommi S. Jaakkola
2020	A	`conf`	Unsupervised Hierarchy Matching with Optimal Transport over Hyperbolic Spaces. AISTATS David Alvarez-Melis, Youssef Mroueh, Tommi S. Jaakkola
2019	A*	`conf`	A Game Theoretic Approach to Class-wise Selective Rationalization. NeurIPS Shiyu Chang, Yang Zhang, Mo Yu, Tommi S. Jaakkola
2019	J	`jnl`	A Game Theoretic Approach to Class-wise Selective Rationalization. CoRR Shiyu Chang, Yang Zhang, Mo Yu, Tommi S. Jaakkola
2019	J	`jnl`	A Stratified Approach to Robustness for Randomly Smoothed Classifiers. CoRR Guang-He Lee, Yang Yuan, Shiyu Chang, Tommi S. Jaakkola
2019	J	`jnl`	Alignment Based Matching Networks for One-Shot Classification and Open-Set Recognition. CoRR Paresh Malalur, Tommi S. Jaakkola
2019	J	`jnl`	Analyzing Learned Molecular Representations for Property Prediction. J. Chem. Inf. Model. Kevin 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
2019	J	`jnl`	Are Learned Molecular Representations Ready For Prime Time? CoRR Kevin 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
2019	A*	`conf`	Bidirectional Inference Networks: A Class of Deep Bayesian Networks for Health Profiling. AAAI Hao Wang, Chengzhi Mao, Hao He, Mingmin Zhao, Tommi S. Jaakkola, Dina Katabi
2019	J	`jnl`	Bidirectional Inference Networks: A Class of Deep Bayesian Networks for Health Profiling. CoRR Hao Wang, Chengzhi Mao, Hao He, Mingmin Zhao, Tommi S. Jaakkola, Dina Katabi
2019	J	`jnl`	Correction to Analyzing Learned Molecular Representations for Property Prediction. J. Chem. Inf. Model. Kevin 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
2019	A*	`conf`	Direct Optimization through arg max for Discrete Variational Auto-Encoder. NeurIPS Guy Lorberbom, Tommi S. Jaakkola, Andreea Gane, Tamir Hazan
2019	A*	`conf`	Functional Transparency for Structured Data: a Game-Theoretic Approach. ICML Guang-He Lee, Wengong Jin, David Alvarez-Melis, Tommi S. Jaakkola
2019	J	`jnl`	Functional Transparency for Structured Data: a Game-Theoretic Approach. CoRR Guang-He Lee, Wengong Jin, David Alvarez-Melis, Tommi S. Jaakkola
2019	—	`conf`	Generative Models for Graph-Based Protein Design. DGS@ICLR John Ingraham, Vikas K. Garg, Regina Barzilay, Tommi S. Jaakkola
2019	A*	`conf`	Generative Models for Graph-Based Protein Design. NeurIPS John Ingraham, Vikas K. Garg, Regina Barzilay, Tommi S. Jaakkola
2019	J	`jnl`	High Dimensional Inference With Random Maximum A-Posteriori Perturbations. IEEE Trans. Inf. Theory Tamir Hazan, Francesco Orabona, Anand D. Sarwate, Subhransu Maji, Tommi S. Jaakkola
2019	J	`jnl`	Latent Space Secrets of Denoising Text-Autoencoders. CoRR Tianxiao Shen, Jonas Mueller, Regina Barzilay, Tommi S. Jaakkola
2019	—	`conf`	Learning Multimodal Graph-to-Graph Translation for Molecule Optimization. ICLR (Poster) Wengong Jin, Kevin Yang, Regina Barzilay, Tommi S. Jaakkola
2019	J	`jnl`	Learning to Make Generalizable and Diverse Predictions for Retrosynthesis. CoRR Benson Chen, Tianxiao Shen, Tommi S. Jaakkola, Regina Barzilay
2019	J	`jnl`	Locally Constant Networks. CoRR Guang-He Lee, Tommi S. Jaakkola
2019	J	`jnl`	Multi-resolution Autoregressive Graph-to-Graph Translation for Molecules. CoRR Wengong Jin, Regina Barzilay, Tommi S. Jaakkola
2019	J	`jnl`	Path-Augmented Graph Transformer Network. CoRR Benson Chen, Regina Barzilay, Tommi S. Jaakkola
2019	—	`conf`	Rethinking Cooperative Rationalization: Introspective Extraction and Complement Control. EMNLP/IJCNLP (1) Mo Yu, Shiyu Chang, Yang Zhang, Tommi S. Jaakkola
2019	J	`jnl`	Rethinking Cooperative Rationalization: Introspective Extraction and Complement Control. CoRR Mo Yu, Shiyu Chang, Yang Zhang, Tommi S. Jaakkola
2019	A*	`conf`	Solving graph compression via optimal transport. NeurIPS Vikas K. Garg, Tommi S. Jaakkola
2019	J	`jnl`	Solving graph compression via optimal transport. CoRR Vikas K. Garg, Tommi S. Jaakkola
2019	J	`jnl`	Strategic Prediction with Latent Aggregative Games. CoRR Vikas K. Garg, Tommi S. Jaakkola
2019	A*	`conf`	Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifiers. NeurIPS Guang-He Lee, Yang Yuan, Shiyu Chang, Tommi S. Jaakkola
2019	A	`conf`	Towards Optimal Transport with Global Invariances. AISTATS David Alvarez-Melis, Stefanie Jegelka, Tommi S. Jaakkola
2019	—	`conf`	Towards Robust, Locally Linear Deep Networks. ICLR (Poster) Guang-He Lee, David Alvarez-Melis, Tommi S. Jaakkola
2019	J	`jnl`	Towards Robust, Locally Linear Deep Networks. CoRR Guang-He Lee, David Alvarez-Melis, Tommi S. Jaakkola
2019	J	`jnl`	Unsupervised Hierarchy Matching with Optimal Transport over Hyperbolic Spaces. CoRR David Alvarez-Melis, Youssef Mroueh, Tommi S. Jaakkola
2018	J	`jnl`	Direct Optimization through arg max for Discrete Variational Auto-Encoder. CoRR Guy Lorberbom, Andreea Gane, Tommi S. Jaakkola, Tamir Hazan
2018	J	`jnl`	Game-Theoretic Interpretability for Temporal Modeling. CoRR Guang-He Lee, David Alvarez-Melis, Tommi S. Jaakkola
2018	A*	`conf`	Gromov-Wasserstein Alignment of Word Embedding Spaces. EMNLP David Alvarez-Melis, Tommi S. Jaakkola
2018	J	`jnl`	Gromov-Wasserstein Alignment of Word Embedding Spaces. CoRR David Alvarez-Melis, Tommi S. Jaakkola
2018	J	`jnl`	Grounding Language for Transfer in Deep Reinforcement Learning. J. Artif. Intell. Res. Karthik Narasimhan, Regina Barzilay, Tommi S. Jaakkola
2018	A*	`conf`	Junction Tree Variational Autoencoder for Molecular Graph Generation. ICML Wengong Jin, Regina Barzilay, Tommi S. Jaakkola
2018	J	`jnl`	Junction Tree Variational Autoencoder for Molecular Graph Generation. CoRR Wengong Jin, Regina Barzilay, Tommi S. Jaakkola
2018	J	`jnl`	Learning Multimodal Graph-to-Graph Translation for Molecular Optimization. CoRR Wengong Jin, Kevin Yang, Regina Barzilay, Tommi S. Jaakkola
2018	J	`jnl`	On the Robustness of Interpretability Methods. CoRR David Alvarez-Melis, Tommi S. Jaakkola
2018	A	`conf`	Structured Optimal Transport. AISTATS David Alvarez-Melis, Tommi S. Jaakkola, Stefanie Jegelka
2018	A	`conf`	The Variational Homoencoder: Learning to learn high capacity generative models from few examples. UAI Luke B. Hewitt, Maxwell I. Nye, Andreea Gane, Tommi S. Jaakkola, Joshua B. Tenenbaum
2018	J	`jnl`	The Variational Homoencoder: Learning to learn high capacity generative models from few examples. CoRR Luke B. Hewitt, Maxwell I. Nye, Andreea Gane, Tommi S. Jaakkola, Joshua B. Tenenbaum
2018	J	`jnl`	Towards Optimal Transport with Global Invariances. CoRR David Alvarez-Melis, Stefanie Jegelka, Tommi S. Jaakkola
2018	A*	`conf`	Towards Robust Interpretability with Self-Explaining Neural Networks. NeurIPS David Alvarez-Melis, Tommi S. Jaakkola
2018	J	`jnl`	Towards Robust Interpretability with Self-Explaining Neural Networks. CoRR David Alvarez-Melis, Tommi S. Jaakkola
2017	A*	`conf`	A causal framework for explaining the predictions of black-box sequence-to-sequence models. EMNLP David Alvarez-Melis, Tommi S. Jaakkola
2017	J	`jnl`	A causal framework for explaining the predictions of black-box sequence-to-sequence models. CoRR David Alvarez-Melis, Tommi S. Jaakkola
2017	J	`jnl`	Aspect-augmented Adversarial Networks for Domain Adaptation. CoRR Yuan Zhang, Regina Barzilay, Tommi S. Jaakkola
2017	J	`jnl`	Aspect-augmented Adversarial Networks for Domain Adaptation. Trans. Assoc. Comput. Linguistics Yuan Zhang, Regina Barzilay, Tommi S. Jaakkola
2017	J	`jnl`	Convolutional Embedding of Attributed Molecular Graphs for Physical Property Prediction. J. Chem. Inf. Model. Connor W. Coley, Regina Barzilay, William H. Green Jr., Tommi S. Jaakkola, Klavs F. Jensen
2017	J	`jnl`	Deep Transfer in Reinforcement Learning by Language Grounding. CoRR Karthik Narasimhan, Regina Barzilay, Tommi S. Jaakkola
2017	A*	`conf`	Deriving Neural Architectures from Sequence and Graph Kernels. ICML Tao Lei, Wengong Jin, Regina Barzilay, Tommi S. Jaakkola
2017	J	`jnl`	Deriving Neural Architectures from Sequence and Graph Kernels. CoRR Tao Lei, Wengong Jin, Regina Barzilay, Tommi S. Jaakkola
2017	A	`conf`	Learning Optimal Interventions. AISTATS Jonas Mueller, David Reshef, George Du, Tommi S. Jaakkola
2017	A*	`conf`	Learning Sleep Stages from Radio Signals: A Conditional Adversarial Architecture. ICML Mingmin Zhao, Shichao Yue, Dina Katabi, Tommi S. Jaakkola, Matt T. Bianchi
2017	—	`conf`	Local Aggregative Games. NIPS Vikas K. Garg, Tommi S. Jaakkola
2017	—	`conf`	Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network. NIPS Wengong Jin, Connor W. Coley, Regina Barzilay, Tommi S. Jaakkola
2017	J	`jnl`	Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network. CoRR Wengong Jin, Connor W. Coley, Regina Barzilay, Tommi S. Jaakkola
2017	A*	`conf`	Sequence to Better Sequence: Continuous Revision of Combinatorial Structures. ICML Jonas Mueller, David K. Gifford, Tommi S. Jaakkola
2017	J	`jnl`	Structured Optimal Transport. CoRR David Alvarez-Melis, Tommi S. Jaakkola, Stefanie Jegelka
2017	—	`conf`	Style Transfer from Non-Parallel Text by Cross-Alignment. NIPS Tianxiao Shen, Tao Lei, Regina Barzilay, Tommi S. Jaakkola
2017	J	`jnl`	Style Transfer from Non-Parallel Text by Cross-Alignment. CoRR Tianxiao Shen, Tao Lei, Regina Barzilay, Tommi S. Jaakkola
2017	—	`conf`	Tree-structured decoding with doubly-recurrent neural networks. ICLR (Poster) David Alvarez-Melis, Tommi S. Jaakkola
2016	A	`conf`	CRAFT: ClusteR-specific Assorted Feature selecTion. AISTATS Vikas K. Garg, Cynthia Rudin, Tommi S. Jaakkola
2016	J	`jnl`	High Dimensional Inference with Random Maximum A-Posteriori Perturbations. CoRR Tamir Hazan, Francesco Orabona, Anand D. Sarwate, Subhransu Maji, Tommi S. Jaakkola
2016	J	`jnl`	Learning Optimal Interventions. CoRR Jonas Mueller, David Reshef, George Du, Tommi S. Jaakkola
2016	A*	`conf`	Learning Population-Level Diffusions with Generative RNNs. ICML Tatsunori B. Hashimoto, David K. Gifford, Tommi S. Jaakkola
2016	—	`conf`	Learning Tree Structured Potential Games. NIPS Vikas K. Garg, Tommi S. Jaakkola
2016	A*	`conf`	Learning to refine text based recommendations. EMNLP Youyang Gu, Tao Lei, Regina Barzilay, Tommi S. Jaakkola
2016	A*	`conf`	Rationalizing Neural Predictions. EMNLP Tao Lei, Regina Barzilay, Tommi S. Jaakkola
2016	J	`jnl`	Rationalizing Neural Predictions. CoRR Tao Lei, Regina Barzilay, Tommi S. Jaakkola
2016	—	`conf`	Semi-supervised Question Retrieval with Gated Convolutions. HLT-NAACL Tao Lei, Hrishikesh Joshi, Regina Barzilay, Tommi S. Jaakkola, Kateryna Tymoshenko, Alessandro Moschitti, Lluís Màrquez
2016	A	`conf`	Structured Prediction: From Gaussian Perturbations to Linear-Time Principled Algorithms. UAI Jean Honorio, Tommi S. Jaakkola
2016	—	`conf`	Ten Pairs to Tag - Multilingual POS Tagging via Coarse Mapping between Embeddings. HLT-NAACL Yuan Zhang, David Gaddy, Regina Barzilay, Tommi S. Jaakkola
2016	J	`jnl`	Word Embeddings as Metric Recovery in Semantic Spaces. Trans. Assoc. Comput. Linguistics Tatsunori B. Hashimoto, David Alvarez-Melis, Tommi S. Jaakkola
2015	J	`jnl`	An Unsupervised Method for Uncovering Morphological Chains. CoRR Karthik Narasimhan, Regina Barzilay, Tommi S. Jaakkola
2015	J	`jnl`	An Unsupervised Method for Uncovering Morphological Chains. Trans. Assoc. Comput. Linguistics Karthik Narasimhan, Regina Barzilay, Tommi S. Jaakkola
2015	J	`jnl`	CRAFT: ClusteR-specific Assorted Feature selecTion. CoRR Vikas K. Garg, Cynthia Rudin, Tommi S. Jaakkola
2015	J	`jnl`	Denoising Bodies to Titles: Retrieving Similar Questions with Recurrent Convolutional Models. CoRR Tao Lei, Hrishikesh Joshi, Regina Barzilay, Tommi S. Jaakkola, Kateryna Tymoshenko, Alessandro Moschitti, Lluís Màrquez i Villodre
2015	—	`conf`	From random walks to distances on unweighted graphs. NIPS Tatsunori B. Hashimoto, Yi Sun, Tommi S. Jaakkola
2015	J	`jnl`	From random walks to distances on unweighted graphs. CoRR Tatsunori B. Hashimoto, Yi Sun, Tommi S. Jaakkola
2015	A	`conf`	Metric recovery from directed unweighted graphs. AISTATS Tatsunori B. Hashimoto, Yi Sun, Tommi S. Jaakkola
2015	A*	`conf`	Molding CNNs for text: non-linear, non-consecutive convolutions. EMNLP Tao Lei, Regina Barzilay, Tommi S. Jaakkola
2015	J	`jnl`	Molding CNNs for text: non-linear, non-consecutive convolutions. CoRR Tao Lei, Regina Barzilay, Tommi S. Jaakkola
2015	—	`conf`	Principal Differences Analysis: Interpretable Characterization of Differences between Distributions. NIPS Jonas Mueller, Tommi S. Jaakkola
2015	J	`jnl`	Principal Differences Analysis: Interpretable Characterization of Differences between Distributions. CoRR Jonas Mueller, Tommi S. Jaakkola
2015	J	`jnl`	Statistical Learning under Nonstationary Mixing Processes. CoRR Steve Hanneke, Tommi S. Jaakkola, Liu Yang
2015	J	`jnl`	Steps Toward Deep Kernel Methods from Infinite Neural Networks. CoRR Tamir Hazan, Tommi S. Jaakkola
2015	J	`jnl`	Structured Prediction: From Gaussian Perturbations to Linear-Time Principled Algorithms. CoRR Jean Honorio, Tommi S. Jaakkola
2015	J	`jnl`	Word, graph and manifold embedding from Markov processes. CoRR Tatsunori B. Hashimoto, David Alvarez-Melis, Tommi S. Jaakkola
2014	A*	`conf`	A Unified Framework for Consistency of Regularized Loss Minimizers. ICML Jean Honorio, Tommi S. Jaakkola
2014	A	`conf`	Active Boundary Annotation using Random MAP Perturbations. AISTATS Subhransu Maji, Tamir Hazan, Tommi S. Jaakkola
2014	—	`conf`	Controlling privacy in recommender systems. NIPS Yu Xin, Tommi S. Jaakkola
2014	A*	`conf`	Greed is Good if Randomized: New Inference for Dependency Parsing. EMNLP Yuan Zhang, Tao Lei, Regina Barzilay, Tommi S. Jaakkola
2014	A	`conf`	Learning with Maximum A-Posteriori Perturbation Models. AISTATS Andreea Gane, Tamir Hazan, Tommi S. Jaakkola
2014	—	`conf`	Low-Rank Tensors for Scoring Dependency Structures. ACL (1) Tao Lei, Yu Xin, Yuan Zhang, Regina Barzilay, Tommi S. Jaakkola
2014	J	`jnl`	Metric recovery from directed unweighted graphs. CoRR Tatsunori B. Hashimoto, Yi Sun, Tommi S. Jaakkola
2014	A*	`conf`	On Measure Concentration of Random Maximum A-Posteriori Perturbations. ICML Francesco Orabona, Tamir Hazan, Anand D. Sarwate, Tommi S. Jaakkola
2014	—	`conf`	Steps to Excellence: Simple Inference with Refined Scoring of Dependency Trees. ACL (1) Yuan Zhang, Tao Lei, Regina Barzilay, Tommi S. Jaakkola, Amir Globerson
2014	A	`conf`	Tight Bounds for the Expected Risk of Linear Classifiers and PAC-Bayes Finite-Sample Guarantees. AISTATS Jean Honorio, Tommi S. Jaakkola
2013	J	`jnl`	A New Class of Upper Bounds on the Log Partition Function CoRR Martin J. Wainwright, Tommi S. Jaakkola, Alan S. Willsky
2013	J	`jnl`	Computing Upper and Lower Bounds on Likelihoods in Intractable Networks CoRR Tommi S. Jaakkola, Michael I. Jordan
2013	J	`jnl`	Continuation Methods for Mixing Heterogenous Sources CoRR Adrian Corduneanu, Tommi S. Jaakkola
2013	J	`jnl`	Feature Selection and Dualities in Maximum Entropy Discrimination CoRR Tony Jebara, Tommi S. Jaakkola
2013	A	`conf`	Inverse Covariance Estimation for High-Dimensional Data in Linear Time and Space: Spectral Methods for Riccati and Sparse Models. UAI Jean Honorio, Tommi S. Jaakkola
2013	J	`jnl`	Inverse Covariance Estimation for High-Dimensional Data in Linear Time and Space: Spectral Methods for Riccati and Sparse Models. CoRR Jean Honorio, Tommi S. Jaakkola
2013	—	`conf`	Learning Efficient Random Maximum A-Posteriori Predictors with Non-Decomposable Loss Functions. NIPS Tamir Hazan, Subhransu Maji, Joseph Keshet, Tommi S. Jaakkola
2013	J	`jnl`	On Measure Concentration of Random Maximum A-Posteriori Perturbations. CoRR Francesco Orabona, Tamir Hazan, Anand D. Sarwate, Tommi S. Jaakkola
2013	—	`conf`	On Sampling from the Gibbs Distribution with Random Maximum A-Posteriori Perturbations. NIPS Tamir Hazan, Subhransu Maji, Tommi S. Jaakkola
2013	J	`jnl`	On Sampling from the Gibbs Distribution with Random Maximum A-Posteriori Perturbations. CoRR Tamir Hazan, Subhransu Maji, Tommi S. Jaakkola
2013	J	`jnl`	Tractable Bayesian Learning of Tree Belief Networks CoRR Marina Meila, Tommi S. Jaakkola
2013	—	`conf`	Two-Sided Exponential Concentration Bounds for Bayes Error Rate and Shannon Entropy. ICML (3) Jean Honorio, Tommi S. Jaakkola
2013	J	`jnl`	Unsupervised Active Learning in Large Domains CoRR Harald Steck, Tommi S. Jaakkola
2012	A	`conf`	Approximate Inference in Additive Factorial HMMs with Application to Energy Disaggregation. AISTATS J. Zico Kolter, Tommi S. Jaakkola
2012	—	`conf`	Convergence Rate Analysis of MAP Coordinate Minimization Algorithms. NIPS Ofer Meshi, Tommi S. Jaakkola, Amir Globerson
2012	J	`jnl`	Convergent Propagation Algorithms via Oriented Trees CoRR Amir Globerson, Tommi S. Jaakkola
2012	J	`jnl`	Lineage-based identification of cellular states and expression programs. Bioinform. Tatsunori B. Hashimoto, Tommi S. Jaakkola, Richard Sherwood, Esteban O. Mazzoni, Hynek Wichterle, David K. Gifford
2012	J	`jnl`	On Information Regularization CoRR Adrian Corduneanu, Tommi S. Jaakkola
2012	A*	`conf`	On the Partition Function and Random Maximum A-Posteriori Perturbations. ICML Tamir Hazan, Tommi S. Jaakkola
2012	A	`conf`	Primal-Dual methods for sparse constrained matrix completion. AISTATS Yu Xin, Tommi S. Jaakkola
2012	J	`jnl`	Proceedings of the Twenty-First Conference on Uncertainty in Artificial Intelligence (2005) CoRR Fahiem Bacchus, Tommi S. Jaakkola
2012	J	`jnl`	Special Issue on the Fifth European Workshop on Probabilistic Graphical Models (PGM-2010). Int. J. Approx. Reason. Teemu Roos, Petri Myllymäki, Tommi S. Jaakkola
2012	J	`jnl`	Tightening LP Relaxations for MAP using Message Passing CoRR David A. Sontag, Talya Meltzer, Amir Globerson, Tommi S. Jaakkola, Yair Weiss
2011	J	`jnl`	Variational Probabilistic Inference and the QMR-DT Network CoRR Tommi S. Jaakkola, Michael I. Jordan
2010	A	`conf`	Collaborative future event recommendation. CIKM Einat Minkov, Ben Charrow, Jonathan Ledlie, Seth J. Teller, Tommi S. Jaakkola
2010	J	`jnl`	Discovering homotypic binding events at high spatial resolution. Bioinform. Yuchun Guo, Georgios Papachristoudis, Robert C. Altshuler, Georg K. Gerber, Tommi S. Jaakkola, David K. Gifford, Shaun Mahony
2010	A*	`conf`	Dual Decomposition for Parsing with Non-Projective Head Automata. EMNLP Terry Koo, Alexander M. Rush, Michael Collins, Tommi S. Jaakkola, David A. Sontag
2010	A	`conf`	Learning Bayesian Network Structure using LP Relaxations. AISTATS Tommi S. Jaakkola, David A. Sontag, Amir Globerson, Marina Meila
2010	A*	`conf`	Learning Efficiently with Approximate Inference via Dual Losses. ICML Ofer Meshi, David A. Sontag, Tommi S. Jaakkola, Amir Globerson
2010	—	`conf`	More data means less inference: A pseudo-max approach to structured learning. NIPS David A. Sontag, Ofer Meshi, Tommi S. Jaakkola, Amir Globerson
2010	A*	`conf`	On Dual Decomposition and Linear Programming Relaxations for Natural Language Processing. EMNLP Alexander M. Rush, David A. Sontag, Michael Collins, Tommi S. Jaakkola
2009	A	`conf`	Tree Block Coordinate Descent for MAP in Graphical Models. AISTATS David A. Sontag, Tommi S. Jaakkola
2008	—	`conf`	Clusters and Coarse Partitions in LP Relaxations. NIPS David A. Sontag, Amir Globerson, Tommi S. Jaakkola
2008	A	`conf`	Tightening LP Relaxations for MAP using Message Passing. UAI David A. Sontag, Talya Meltzer, Amir Globerson, Tommi S. Jaakkola, Yair Weiss
2007	A	`conf`	Approximate inference using conditional entropy decompositions. AISTATS Amir Globerson, Tommi S. Jaakkola
2007	J	`jnl`	Automated Discovery of Functional Generality of Human Gene Expression Programs. PLoS Comput. Biol. Georg K. Gerber, Robin D. Dowell, Tommi S. Jaakkola, David K. Gifford
2007	A	`conf`	Convergent Propagation Algorithms via Oriented Trees. UAI Amir Globerson, Tommi S. Jaakkola
2007	—	`conf`	Fixing Max-Product: Convergent Message Passing Algorithms for MAP LP-Relaxations. NIPS Amir Globerson, Tommi S. Jaakkola
2007	—	`conf`	New Outer Bounds on the Marginal Polytope. NIPS David A. Sontag, Tommi S. Jaakkola
2007	A	`conf`	Predictive Discretization during Model Selection. AISTATS Harald Steck, Tommi S. Jaakkola
2006	—	`conf`	Approximate inference using planar graph decomposition. NIPS Amir Globerson, Tommi S. Jaakkola
2006	—	`ch.`	Data-Dependent Regularization. Semi-Supervised Learning Adrian Corduneanu, Tommi S. Jaakkola
2006	—	`conf`	Game Theoretic Algorithms for Protein-DNA binding. NIPS Luis Pérez-Breva, Luis E. Ortiz, Chen-Hsiang Yeang, Tommi S. Jaakkola
2006	J	`jnl`	Modeling the Combinatorial Functions of Multiple Transcription Factors. J. Comput. Biol. Chen-Hsiang Yeang, Tommi S. Jaakkola
2006	—	`conf`	Parameter Expanded Variational Bayesian Methods. NIPS Yuan (Alan) Qi, Tommi S. Jaakkola
2006	—	`conf`	Semi-supervised analysis of gene expression profiles for lineage-specific development in the ISMB (Supplement of Bioinformatics) Yuan (Alan) Qi, Patrycja E. Missiuro, Ashish Kapoor, Craig P. Hunter, Tommi S. Jaakkola, David K. Gifford, Hui Ge
2006	J	`jnl`	Tractable Bayesian learning of tree belief networks. Stat. Comput. Marina Meila, Tommi S. Jaakkola
2005	J	`jnl`	A new class of upper bounds on the log partition function. IEEE Trans. Inf. Theory Martin J. Wainwright, Tommi S. Jaakkola, Alan S. Willsky
2005	A	`conf`	Focused Inference. AISTATS Rómer Rosales, Tommi S. Jaakkola
2005	J	`jnl`	MAP estimation via agreement on (hyper)trees: Message-passing and linear programming CoRR Martin J. Wainwright, Tommi S. Jaakkola, Alan S. Willsky
2005	J	`jnl`	MAP estimation via agreement on trees: message-passing and linear programming. IEEE Trans. Inf. Theory Martin J. Wainwright, Tommi S. Jaakkola, Alan S. Willsky
2005	B	`conf`	Modeling the Combinatorial Functions of Multiple Transcription Factors. RECOMB Chen-Hsiang Yeang, Tommi S. Jaakkola
2005	J	`jnl`	Time Series Analysis of Gene Expression and Location Data. Int. J. Artif. Intell. Tools Chen-Hsiang Yeang, Tommi S. Jaakkola
2005	A*	`conf`	Using term informativeness for named entity detection. SIGIR Jason D. M. Rennie, Tommi S. Jaakkola
2004	—	`conf`	Analysis of Signaling Pathways in Human T-Cells Using Bayesian Network Modeling of Single Cell Data. CSB Karen Sachs, Omar D. Perez, Dana Pe'er, Garry P. Nolan, David K. Gifford, Tommi S. Jaakkola, Douglas A. Lauffenburger
2004	—	`conf`	Distributed Information Regularization on Graphs. NIPS Adrian Corduneanu, Tommi S. Jaakkola
2004	—	`conf`	Generalization Error Bounds for Collaborative Prediction with Low-Rank Matrices. NIPS Nathan Srebro, Noga Alon, Tommi S. Jaakkola
2004	—	`conf`	Maximum-Margin Matrix Factorization. NIPS Nathan Srebro, Jason D. M. Rennie, Tommi S. Jaakkola
2004	J	`jnl`	Physical Network Models. J. Comput. Biol. Chen-Hsiang Yeang, Trey Ideker, Tommi S. Jaakkola
2004	—	`conf`	Predictive Discretization During Model Selection. DAGM-Symposium Harald Steck, Tommi S. Jaakkola
2004	J	`jnl`	Tree consistency and bounds on the performance of the max-product algorithm and its generalizations. Stat. Comput. Martin J. Wainwright, Tommi S. Jaakkola, Alan S. Willsky
2003	—	`conf`	Bias-Corrected Bootstrap and Model Uncertainty. NIPS Harald Steck, Tommi S. Jaakkola
2003	J	`jnl`	Continuous Representations of Time-Series Gene Expression Data. J. Comput. Biol. Ziv Bar-Joseph, Georg K. Gerber, David K. Gifford, Tommi S. Jaakkola, Itamar Simon
2003	J	`jnl`	K-ary Clustering with Optimal Leaf Ordering for Gene Expression Data. Bioinform. Ziv Bar-Joseph, Erik D. Demaine, David K. Gifford, Nathan Srebro, Angèle M. Hamel, Tommi S. Jaakkola
2003	—	`conf`	Linear Dependent Dimensionality Reduction. NIPS Nathan Srebro, Tommi S. Jaakkola
2003	A	`conf`	On Information Regularization. UAI Adrian Corduneanu, Tommi S. Jaakkola
2003	—	`conf`	Online Learning of Non-stationary Sequences. NIPS Claire Monteleoni, Tommi S. Jaakkola
2003	B	`conf`	Physical network models and multi-source data integration. RECOMB Chen-Hsiang Yeang, Tommi S. Jaakkola
2003	C	`conf`	Time Series Analysis of Gene Expression and Location Data. BIBE Chen-Hsiang Yeang, Tommi S. Jaakkola
2003	J	`jnl`	Tree-based reparameterization framework for analysis of sum-product and related algorithms. IEEE Trans. Inf. Theory Martin J. Wainwright, Tommi S. Jaakkola, Alan S. Willsky
2003	A	`conf`	Tree-reweighted belief propagation algorithms and approximate ML estimation by pseudo-moment matching. AISTATS Martin J. Wainwright, Tommi S. Jaakkola, Alan S. Willsky
2003	A*	`conf`	Weighted Low-Rank Approximations. ICML Nathan Srebro, Tommi S. Jaakkola
2002	A	`conf`	A New Class of upper Bounds on the Log Partition Function. UAI Martin J. Wainwright, Tommi S. Jaakkola, Alan S. Willsky
2002	B	`conf`	A new approach to analyzing gene expression time series data. RECOMB Ziv Bar-Joseph, Georg K. Gerber, David K. Gifford, Tommi S. Jaakkola, Itamar Simon
2002	J	`jnl`	Bayesian Methods for Elucidating Genetic Regulatory Networks. IEEE Intell. Syst. Alexander J. Hartemink, David K. Gifford, Tommi S. Jaakkola, Richard A. Young
2002	Misc	`conf`	Combining Location and Expression Data for Principled Discovery of Genetic Regulatory Network Models. Pacific Symposium on Biocomputing Alexander J. Hartemink, David K. Gifford, Tommi S. Jaakkola, Richard A. Young
2002	A	`conf`	Continuation Methods for Mixing Heterogenous Sources. UAI Adrian Corduneanu, Tommi S. Jaakkola
2002	—	`conf`	Exact MAP Estimates by (Hyper)tree Agreement. NIPS Martin J. Wainwright, Tommi S. Jaakkola, Alan S. Willsky
2002	—	`conf`	Information Regularization with Partially Labeled Data. NIPS Martin Szummer, Tommi S. Jaakkola
2002	C	`conf`	K-ary Clustering with Optimal Leaf Ordering for Gene Expression Data. WABI Ziv Bar-Joseph, Erik D. Demaine, David K. Gifford, Angèle M. Hamel, Tommi S. Jaakkola, Nathan Srebro
2002	—	`conf`	On the Dirichlet Prior and Bayesian Regularization. NIPS Harald Steck, Tommi S. Jaakkola
2002	A	`conf`	Unsupervised Active Learning in Large Domains. UAI Harald Steck, Tommi S. Jaakkola
2001	—	`conf`	Active Information Retrieval. NIPS Tommi S. Jaakkola, Hava T. Siegelmann
2001	—	`conf`	Fast optimal leaf ordering for hierarchical clustering. ISMB (Supplement of Bioinformatics) Ziv Bar-Joseph, David K. Gifford, Tommi S. Jaakkola
2001	—	`conf`	Partially labeled classification with Markov random walks. NIPS Martin Szummer, Tommi S. Jaakkola
2001	A	`ed.`	Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, AISTATS 2001, Key West, Florida, USA, January 4-7, 2001 AISTATS Thomas S. Richardson, Tommi S. Jaakkola
2001	—	`conf`	Tree-based reparameterization for approximate inference on loopy graphs. NIPS Martin J. Wainwright, Tommi S. Jaakkola, Alan S. Willsky
2001	Misc	`conf`	Using Graphical Models and Genomic Expression Data to Statistically Validate Models of Genetic Regulatory Networks. Pacific Symposium on Biocomputing Alexander J. Hartemink, David K. Gifford, Tommi S. Jaakkola, Richard A. Young
2000	J	`jnl`	A Discriminative Framework for Detecting Remote Protein Homologies. J. Comput. Biol. Tommi S. Jaakkola, Mark Diekhans, David Haussler
2000	J	`jnl`	Bayesian parameter estimation via variational methods. Stat. Comput. Tommi S. Jaakkola, Michael I. Jordan
2000	J	`jnl`	Convergence Results for Single-Step On-Policy Reinforcement-Learning Algorithms. Mach. Learn. Satinder Singh, Tommi S. Jaakkola, Michael L. Littman, Csaba Szepesvári
2000	A	`conf`	Feature Selection and Dualities in Maximum Entropy Discrimination. UAI Tony Jebara, Tommi S. Jaakkola
2000	—	`conf`	Kernel Expansions with Unlabeled Examples. NIPS Martin Szummer, Tommi S. Jaakkola
2000	—	`conf`	Sequentially Fitting "Inclusive" Trees for Inference in Noisy-OR Networks. NIPS Brendan J. Frey, Relu Patrascu, Tommi S. Jaakkola, Jodi Moran
2000	A	`conf`	Tractable Bayesian Learning of Tree Belief Networks. UAI Marina Meila, Tommi S. Jaakkola
1999	J	`jnl`	An Introduction to Variational Methods for Graphical Models. Mach. Learn. Michael I. Jordan, Zoubin Ghahramani, Tommi S. Jaakkola, Lawrence K. Saul
1999	—	`conf`	Maximum Entropy Discrimination. NIPS Tommi S. Jaakkola, Marina Meila, Tony Jebara
1999	A	`conf`	Probabilistic kernel regression models. AISTATS Tommi S. Jaakkola, David Haussler
1999	A	`conf`	Using the Fisher Kernel Method to Detect Remote Protein Homologies. ISMB Tommi S. Jaakkola, Mark Diekhans, David Haussler
1999	J	`jnl`	Variational Probabilistic Inference and the QMR-DT Network. J. Artif. Intell. Res. Tommi S. Jaakkola, Michael I. Jordan
1998	—	`ch.`	An Introduction to Variational Methods for Graphical Models. Learning in Graphical Models Michael I. Jordan, Zoubin Ghahramani, Tommi S. Jaakkola, Lawrence K. Saul
1998	—	`conf`	Exploiting Generative Models in Discriminative Classifiers. NIPS Tommi S. Jaakkola, David Haussler
1998	—	`ch.`	Improving the Mean Field Approximation Via the Use of Mixture Distributions. Learning in Graphical Models Tommi S. Jaakkola, Michael I. Jordan
1997	A	`conf`	A Variational Approach to Bayesian Logistic Regression Models and their Extensions. AISTATS Tommi S. Jaakkola, Michael I. Jordan
1997	—	`conf`	Approximating Posterior Distributions in Belief Networks Using Mixtures. NIPS Christopher M. Bishop, Neil D. Lawrence, Tommi S. Jaakkola, Michael I. Jordan
1996	A	`conf`	Computing upper and lower bounds on likelihoods in intractable networks. UAI Tommi S. Jaakkola, Michael I. Jordan
1996	J	`jnl`	Mean Field Theory for Sigmoid Belief Networks CoRR Lawrence K. Saul, Tommi S. Jaakkola, Michael I. Jordan
1996	J	`jnl`	Mean Field Theory for Sigmoid Belief Networks. J. Artif. Intell. Res. Lawrence K. Saul, Tommi S. Jaakkola, Michael I. Jordan
1996	—	`conf`	Recursive Algorithms for Approximating Probabilities in Graphical Models. NIPS Tommi S. Jaakkola, Michael I. Jordan
1995	—	`conf`	Fast Learning by Bounding Likelihoods in Sigmoid Type Belief Networks. NIPS Tommi S. Jaakkola, Lawrence K. Saul, Michael I. Jordan
1994	A*	`conf`	Learning Without State-Estimation in Partially Observable Markovian Decision Processes. ICML Satinder P. Singh, Tommi S. Jaakkola, Michael I. Jordan
1994	J	`jnl`	On the Convergence of Stochastic Iterative Dynamic Programming Algorithms. Neural Comput. Tommi S. Jaakkola, Michael I. Jordan, Satinder P. Singh
1994	—	`conf`	Reinforcement Learning Algorithm for Partially Observable Markov Decision Problems. NIPS Tommi S. Jaakkola, Satinder Singh, Michael I. Jordan
1994	—	`conf`	Reinforcement Learning with Soft State Aggregation. NIPS Satinder Singh, Tommi S. Jaakkola, Michael I. Jordan
1993	—	`conf`	Convergence of Stochastic Iterative Dynamic Programming Algorithms. NIPS Tommi S. Jaakkola, Michael I. Jordan, Satinder Singh