Garg, Vikas

43 publications

ICLR 2025 Diffusion Models as Cartoonists: The Curious Case of High Density Regions Rafal Karczewski, Markus Heinonen, Vikas Garg

ICLR 2025 E(3)-Equivariant Models Cannot Learn Chirality: Field-Based Molecular Generation Alexandru Dumitrescu, Dani Korpela, Markus Heinonen, Yogesh Verma, Valerii Iakovlev, Vikas Garg, Harri Lähdesmäki

ICLR 2025 Equivariant Denoisers Cannot Copy Graphs: Align Your Graph Diffusion Models Najwa Laabid, Severi Rissanen, Markus Heinonen, Arno Solin, Vikas Garg

ICLR 2025 Generalization and Distributed Learning of GFlowNets Tiago Silva, Amauri H Souza, Omar Rivasplata, Vikas Garg, Samuel Kaski, Diego Mesquita

ICLR 2025 Robust Simulation-Based Inference Under Missing Data via Neural Processes Yogesh Verma, Ayush Bharti, Vikas Garg

ICLR 2025 When Do GFlowNets Learn the Right Distribution? Tiago Silva, Rodrigo Barreto Alves, Eliezer de Souza da Silva, Amauri H Souza, Vikas Garg, Samuel Kaski, Diego Mesquita

NeurIPS 2024 Algebraic Positional Encodings Konstantinos Kogkalidis, Jean-Philippe Bernardy, Vikas Garg

ICMLW 2024 Aligned Diffusion Models for Retrosynthesis Najwa Laabid, Severi Rissanen, Markus Heinonen, Arno Solin, Vikas Garg

ICMLW 2024 Aligned Diffusion Models for Retrosynthesis Najwa Laabid, Severi Rissanen, Markus Heinonen, Arno Solin, Vikas Garg

ICMLW 2024 Analyzing GFlowNets: Stability, Expressiveness, and Assessment Tiago Silva, Eliezer de Souza da Silva, Rodrigo Barreto Alves, Luiz Max Carvalho, Amauri H Souza, Samuel Kaski, Vikas Garg, Diego Mesquita

ICLR 2024 ClimODE: Climate and Weather Forecasting with Physics-Informed Neural ODEs Yogesh Verma, Markus Heinonen, Vikas Garg

NeurIPS 2024 Compositional PAC-Bayes: Generalization of GNNs with Persistence and Beyond Kirill Brilliantov, Amauri H. Souza, Vikas Garg

ICMLW 2024 Conditional Flow Matching for Time Series Modelling Ella Tamir, Najwa Laabid, Markus Heinonen, Vikas Garg, Arno Solin

NeurIPS 2024 Diffusion Twigs with Loop Guidance for Conditional Graph Generation Giangiacomo Mercatali, Yogesh Verma, Andre Freitas, Vikas Garg

ICML 2024 On the Generalization of Equivariant Graph Neural Networks Rafal Karczewski, Amauri H Souza, Vikas Garg

NeurIPSW 2024 PACE: Procedural Abstractions for Communicating Efficiently Jonathan David Thomas, Andrea Silvi, Devdatt Dubhashi, Vikas Garg, Moa Johansson

ICMLW 2024 Topological Neural Networks Go Persistent, Equivariant and Continuous Yogesh Verma, Amauri H Souza, Vikas Garg

ICML 2024 Topological Neural Networks Go Persistent, Equivariant, and Continuous Yogesh Verma, Amauri H Souza, Vikas Garg

NeurIPS 2024 What Do Graph Neural Networks Learn? Insights from Tropical Geometry Tuan Anh Pham, Vikas Garg

ICML 2023 AbODE: Ab Initio Antibody Design Using Conjoined ODEs Yogesh Verma, Markus Heinonen, Vikas Garg

ICMLW 2023 AbODE: Ab Initio Antibody Design Using Conjoined ODEs Yogesh Verma, Markus Heinonen, Vikas Garg

ICMLW 2023 AbODE: Ab Initio Antibody Design Using Conjoined ODEs Yogesh Verma, Markus Heinonen, Vikas Garg

NeurIPS 2023 Compositional Sculpting of Iterative Generative Processes Timur Garipov, Sebastiaan De Peuter, Ge Yang, Vikas Garg, Samuel Kaski, Tommi Jaakkola

NeurIPS 2023 Going Beyond Persistent Homology Using Persistent Homology Johanna Immonen, Amauri Souza, Vikas Garg

NeurIPS 2022 Are GANs Overkill for NLP? David Alvarez-Melis, Vikas Garg, Adam Kalai

NeurIPS 2022 Modular Flows: Differential Molecular Generation Yogesh Verma, Samuel Kaski, Markus Heinonen, Vikas Garg

NeurIPSW 2022 Modular Flows: Differential Molecular Generation Yogesh Verma, Samuel Kaski, Markus Heinonen, Vikas Garg

NeurIPSW 2022 Modular Flows: Differential Molecular Generation Yogesh Verma, Samuel Kaski, Markus Heinonen, Vikas Garg

NeurIPS 2022 Provably Expressive Temporal Graph Networks Amauri Souza, Diego Mesquita, Samuel Kaski, Vikas Garg

NeurIPSW 2022 Provably Expressive Temporal Graph Networks Amauri H Souza, Diego Mesquita, Samuel Kaski, Vikas Garg

NeurIPS 2022 Symmetry-Induced Disentanglement on Graphs Giangiacomo Mercatali, Andre Freitas, Vikas Garg

AISTATS 2021 Learn to Expect the Unexpected: Probably Approximately Correct Domain Generalization Vikas Garg, Adam Tauman Kalai, Katrina Ligett, Steven Wu

ICML 2020 Generalization and Representational Limits of Graph Neural Networks Vikas Garg, Stefanie Jegelka, Tommi Jaakkola

ICML 2020 Predicting Deliberative Outcomes Vikas Garg, Tommi Jaakkola

NeurIPS 2019 Generative Models for Graph-Based Protein Design John Ingraham, Vikas Garg, Regina Barzilay, Tommi Jaakkola

NeurIPS 2019 Online Markov Decoding: Lower Bounds and Near-Optimal Approximation Algorithms Vikas Garg, Tamar Pichkhadze

NeurIPS 2019 Solving Graph Compression via Optimal Transport Vikas Garg, Tommi Jaakkola

NeurIPS 2018 Learning SMaLL Predictors Vikas Garg, Ofer Dekel, Lin Xiao

NeurIPS 2018 Supervising Unsupervised Learning Vikas Garg, Adam T Kalai

NeurIPS 2017 Local Aggregative Games Vikas Garg, Tommi Jaakkola

NeurIPS 2016 Learning Tree Structured Potential Games Vikas Garg, Tommi Jaakkola

ICML 2014 Multiresolution Matrix Factorization Risi Kondor, Nedelina Teneva, Vikas Garg

NeurIPS 2013 Adaptivity to Local Smoothness and Dimension in Kernel Regression Samory Kpotufe, Vikas Garg