← Authors

Vikas Garg

32 papers · 2013–2025 · 4 conferences · across top CS/AI conferences

Achievements

πŸƒ Academic Marathon (12) πŸŒ‰ Interdisciplinary Bridge 🐝 Cross-Pollinator (13) 🌈 Renaissance Researcher (8) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird πŸ—ΊοΈ Taxonomy Completionist (56) 🌍 Conference Polyglot (4) 🌱 Topic Pioneer 🧬 Topic Evolution πŸ† Keyword Champion (2) πŸ‘‘ Triple Crown πŸ—ƒοΈ Keyword Collector (116) ⚑ Prolific Year (7) πŸ’Ž Century Club (32) πŸ”₯ Unstoppable (10) πŸ“ˆ Trend Setter ❓ The Questioner (3)

Conferences

NIPS (18) ICLR (7) ICML (6) AISTATS (1)

Top co-authors

Tommi Jaakkola (7) Yogesh Verma (7) Markus Heinonen (6) Samuel Kaski (5) Amauri H Souza (4) Diego Mesquita (3) Andre Freitas (2) Giangiacomo Mercatali (2) Tiago Silva (2) Rafal Karczewski (2)

Keywords

graph neural network (9) message passing (4) molecular generation (3) generalization bound (3) boolean relaxation (2) graph generation (2) protein design (2) persistent homology (2) game theory (2) multi-agent system (2) topological feature (2) graph theory (1) binary classification (1) graph classification (1) feature selection (1) matrix factorization (1) domain generalization (1) representation learning (1) structured prediction (1) pac learning (1)

Papers

Generalization and Distributed Learning of GFlowNets ICLR 2025
Diffusion Models as Cartoonists: The Curious Case of High Density Regions ICLR 2025
When do GFlowNets learn the right distribution? ICLR 2025
Robust Simulation-Based Inference under Missing Data via Neural Processes ICLR 2025
Equivariant Denoisers Cannot Copy Graphs: Align Your Graph Diffusion Models ICLR 2025
E(3)-equivariant models cannot learn chirality: Field-based molecular generation ICLR 2025
Topological Neural Networks go Persistent, Equivariant, and Continuous ICML 2024
What do Graph Neural Networks learn? Insights from Tropical Geometry NIPS 2024
Algebraic Positional Encodings NIPS 2024
Compositional PAC-Bayes: Generalization of GNNs with persistence and beyond NIPS 2024
Diffusion Twigs with Loop Guidance for Conditional Graph Generation NIPS 2024
ClimODE: Climate and Weather Forecasting with Physics-informed Neural ODEs ICLR 2024
On the Generalization of Equivariant Graph Neural Networks ICML 2024
Going beyond persistent homology using persistent homology NIPS 2023
AbODE: Ab initio antibody design using conjoined ODEs ICML 2023
Compositional Sculpting of Iterative Generative Processes NIPS 2023
Are GANs overkill for NLP? NIPS 2022
Provably expressive temporal graph networks NIPS 2022
Symmetry-induced Disentanglement on Graphs NIPS 2022
Modular Flows: Differential Molecular Generation NIPS 2022
Learn to Expect the Unexpected: Probably Approximately Correct Domain Generalization AISTATS 2021
Generalization and Representational Limits of Graph Neural Networks ICML 2020
Predicting deliberative outcomes ICML 2020
Generative Models for Graph-Based Protein Design NIPS 2019
Solving graph compression via optimal transport NIPS 2019
Online Markov Decoding: Lower Bounds and Near-Optimal Approximation Algorithms NIPS 2019
Supervising Unsupervised Learning NIPS 2018
Learning SMaLL Predictors NIPS 2018
Local Aggregative Games NIPS 2017
Learning Tree Structured Potential Games NIPS 2016
Multiresolution Matrix Factorization ICML 2014
Adaptivity to Local Smoothness and Dimension in Kernel Regression NIPS 2013