Tommi Jaakkola

LEAPS: A discrete neural sampler via locally equivariant networks ICML 2025 Fictitious Synthetic Data Can Improve LLM Factuality via Prerequisite Learning ICLR 2025 Fine-Tuning Discrete Diffusion Models via Reward Optimization with Applications to DNA and Protein Design ICLR 2025 An Information Criterion for Controlled Disentanglement of Multimodal Data ICLR 2025 ProtComposer: Compositional Protein Structure Generation with 3D Ellipsoids ICLR 2025 Data Distillation for extrapolative protein design through exact preference optimization ICLR 2025 Generator Matching: Generative modeling with arbitrary Markov processes ICLR 2025 Scaling Inference Time Compute for Diffusion Models CVPR 2025 Composing Unbalanced Flows for Flexible Docking and Relaxation ICLR 2025 Think while You Generate: Discrete Diffusion with Planned Denoising ICLR 2025 Identifying biological perturbation targets through causal differential networks ICML 2025 Symmetry-Driven Discovery of Dynamical Variables in Molecular Simulations ICML 2025 Thought calibration: Efficient and confident test-time scaling EMNLP 2025 Generative Modeling of Molecular Dynamics Trajectories NIPS 2024 Hamiltonian Score Matching and Generative Flows NIPS 2024 Correcting Diffusion Generation through Resampling CVPR 2024 AlphaFold Meets Flow Matching for Generating Protein Ensembles ICML 2024 Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design ICML 2024 Revisiting Who’s Harry Potter: Towards Targeted Unlearning from a Causal Intervention Perspective EMNLP 2024 DisCo-Diff: Enhancing Continuous Diffusion Models with Discrete Latents ICML 2024 Harmonic Self-Conditioned Flow Matching for joint Multi-Ligand Docking and Binding Site Design ICML 2024 Dirichlet Flow Matching with Applications to DNA Sequence Design ICML 2024 A Recipe for Charge Density Prediction NIPS 2024 Neural Network Reparametrization for Accelerated Optimization in Molecular Simulations NIPS 2024 In-Context Symmetries: Self-Supervised Learning through Contextual World Models NIPS 2024 Restart Sampling for Improving Generative Processes NIPS 2023 Towards Coherent Image Inpainting Using Denoising Diffusion Implicit Models ICML 2023 SE(3) diffusion model with application to protein backbone generation ICML 2023 Compositional Sculpting of Iterative Generative Processes NIPS 2023 Compositional Foundation Models for Hierarchical Planning NIPS 2023 PFGM++: Unlocking the Potential of Physics-Inspired Generative Models ICML 2023 Subspace Diffusion Generative Models ECCV 2022 Antibody-Antigen Docking and Design via Hierarchical Structure Refinement ICML 2022 Conformal Prediction Sets with Limited False Positives ICML 2022 Poisson Flow Generative Models NIPS 2022 Torsional Diffusion for Molecular Conformer Generation NIPS 2022 EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction ICML 2022 Information Obfuscation of Graph Neural Networks ICML 2021 Learning Task Informed Abstractions ICML 2021 Few-Shot Conformal Prediction with Auxiliary Tasks ICML 2021 Mol2Image: Improved Conditional Flow Models for Molecule to Image Synthesis CVPR 2021 Consistent Accelerated Inference via Confident Adaptive Transformers EMNLP 2021 GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles NIPS 2021 Understanding Interlocking Dynamics of Cooperative Rationalization NIPS 2021 Predicting deliberative outcomes ICML 2020 Invariant Rationalization ICML 2020 Multi-Objective Molecule Generation using Interpretable Substructures ICML 2020 Blank Language Models EMNLP 2020 Educating Text Autoencoders: Latent Representation Guidance via Denoising ICML 2020 Improving Molecular Design by Stochastic Iterative Target Augmentation ICML 2020 Self-Supervised Learning of Appliance Usage ICLR 2020 Unsupervised Hierarchy Matching with Optimal Transport over Hyperbolic Spaces AISTATS 2020 Generalization and Representational Limits of Graph Neural Networks ICML 2020 Hierarchical Generation of Molecular Graphs using Structural Motifs ICML 2020 Solving graph compression via optimal transport NIPS 2019 Direct Optimization through $\arg \max$ for Discrete Variational Auto-Encoder NIPS 2019 A Game Theoretic Approach to Class-wise Selective Rationalization NIPS 2019 Generative Models for Graph-Based Protein Design NIPS 2019 Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifiers NIPS 2019 Rethinking Cooperative Rationalization: Introspective Extraction and Complement Control EMNLP 2019 Learning Multimodal Graph-to-Graph Translation for Molecule Optimization ICLR 2019 Functional Transparency for Structured Data: a Game-Theoretic Approach ICML 2019 Rethinking Cooperative Rationalization: Introspective Extraction and Complement Control IJCNLP 2019 Towards Robust Interpretability with Self-Explaining Neural Networks NIPS 2018 Structured Optimal Transport AISTATS 2018 Junction Tree Variational Autoencoder for Molecular Graph Generation ICML 2018 Gromov-Wasserstein Alignment of Word Embedding Spaces EMNLP 2018 A causal framework for explaining the predictions of black-box sequence-to-sequence models EMNLP 2017 Deriving Neural Architectures from Sequence and Graph Kernels ICML 2017 Sequence to Better Sequence: Continuous Revision of Combinatorial Structures ICML 2017 Local Aggregative Games NIPS 2017 Learning Optimal Interventions AISTATS 2017 Style Transfer from Non-Parallel Text by Cross-Alignment NIPS 2017 Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network NIPS 2017 Learning to refine text based recommendations EMNLP 2016 Rationalizing Neural Predictions EMNLP 2016 Learning Population-Level Diffusions with Generative RNNs ICML 2016 Semi-supervised Question Retrieval with Gated Convolutions NAACL 2016 CRAFT: ClusteR-specific Assorted Feature selecTion AISTATS 2016 Learning Tree Structured Potential Games NIPS 2016 Ten Pairs to Tag – Multilingual POS Tagging via Coarse Mapping between Embeddings NAACL 2016 From random walks to distances on unweighted graphs NIPS 2015 Metric recovery from directed unweighted graphs AISTATS 2015 Molding CNNs for text: non-linear, non-consecutive convolutions EMNLP 2015 Principal Differences Analysis: Interpretable Characterization of Differences between Distributions NIPS 2015 Active Boundary Annotation using Random MAP Perturbations AISTATS 2014 Learning with Maximum A-Posteriori Perturbation Models AISTATS 2014 Low-Rank Tensors for Scoring Dependency Structures ACL 2014 Steps to Excellence: Simple Inference with Refined Scoring of Dependency Trees ACL 2014 A Unified Framework for Consistency of Regularized Loss Minimizers ICML 2014 On Measure Concentration of Random Maximum A-Posteriori Perturbations ICML 2014 Greed is Good if Randomized: New Inference for Dependency Parsing EMNLP 2014 Controlling privacy in recommender systems NIPS 2014 Tight Bounds for the Expected Risk of Linear Classifiers and PAC-Bayes Finite-Sample Guarantees AISTATS 2014 Learning Efficient Random Maximum A-Posteriori Predictors with Non-Decomposable Loss Functions NIPS 2013 On Sampling from the Gibbs Distribution with Random Maximum A-Posteriori Perturbations NIPS 2013 Approximate Inference in Additive Factorial HMMs with Application to Energy Disaggregation AISTATS 2012 Primal-Dual methods for sparse constrained matrix completion AISTATS 2012 Dual Decomposition for Parsing with Non-Projective Head Automata EMNLP 2010 Learning Bayesian Network Structure using LP Relaxations AISTATS 2010 On Dual Decomposition and Linear Programming Relaxations for Natural Language Processing EMNLP 2010