default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDVikas Garg 0001
Person information
- affiliation: Aalto University, Espoo, Finland
- affiliation: YaiYai Ltd., Helsinki, Finland
- affiliation (PhD): MIT, Cambridge, MA, USA
- affiliation (former): Toyota Technological Institute at Chicago, IL, USA
- affiliation (former): IBM Research - India
- affiliation (former): Indian Institute of Science, Bangalore, India
Other persons with the same name
- Vikas Garg 0002 — University of Wisconsin, Computer Sciences Department, Madison, WI, USA
- Vikas Garg 0003 — Centre for Development of Advanced Computing, Hyderabad, India
- Vikas Garg 0004 — Amity University, Amity Business School, Noida, India
- Vikas Garg 0005 — Chandigarh University, Department of Mathematics, Mohali, India
- Vikas Garg 0006 — Christ University, India
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
Journal Articles
- 2025
[j3]Haishan Wang, Arno Solin, Vikas K. Garg:
Heterophily-informed Message Passing. Trans. Mach. Learn. Res. 2025 (2025)- 2013
- [j2]Vikas K. Garg, Y. Narahari, M. Narasimha Murty:
Novel Biobjective Clustering (BiGC) Based on Cooperative Game Theory. IEEE Trans. Knowl. Data Eng. 25(5): 1070-1082 (2013) - 2009
- [j1]Vikas K. Garg, M. Narasimha Murty:
Feature subspace SVMs (FS-SVMs) for high dimensional handwritten digit recognition. Int. J. Data Min. Model. Manag. 1(4): 411-436 (2009)
Conference and Workshop Papers
- 2025
- [c49]Alexandru Dumitrescu, Dani Korpela, Markus Heinonen, Yogesh Verma, Valerii Iakovlev, Vikas Garg, Harri Lähdesmäki:
E(3)-equivariant models cannot learn chirality: Field-based molecular generation. ICLR 2025 - [c48]Rafal Karczewski, Markus Heinonen, Vikas Garg:
Diffusion Models as Cartoonists: The Curious Case of High Density Regions. ICLR 2025 - [c47]Najwa Laabid, Severi Rissanen, Markus Heinonen, Arno Solin, Vikas Garg:
Equivariant Denoisers Cannot Copy Graphs: Align Your Graph Diffusion Models. ICLR 2025 - [c46]Tiago da Silva, Rodrigo Barreto Alves, Eliezer de Souza da Silva, Amauri H. Souza, Vikas Garg, Samuel Kaski, Diego Mesquita:
When do GFlowNets learn the right distribution? ICLR 2025 - [c45]Tiago Silva, Amauri H. Souza, Omar Rivasplata, Vikas Garg, Samuel Kaski, Diego Mesquita:
Generalization and Distributed Learning of GFlowNets. ICLR 2025 - [c44]Yogesh Verma, Ayush Bharti, Vikas Garg:
Robust Simulation-Based Inference under Missing Data via Neural Processes. ICLR 2025 - 2024
- [c43]Yue Jiang
, Changkong Zhou, Vikas Garg, Antti Oulasvirta:
Graph4GUI: Graph Neural Networks for Representing Graphical User Interfaces. CHI 2024: 988:1-988:18 - [c42]Yogesh Verma, Markus Heinonen, Vikas Garg:
ClimODE: Climate and Weather Forecasting with Physics-informed Neural ODEs. ICLR 2024 - [c41]Rafal Karczewski, Amauri H. Souza, Vikas Garg:
On the Generalization of Equivariant Graph Neural Networks. ICML 2024 - [c40]Yogesh Verma, Amauri H. Souza, Vikas Garg:
Topological Neural Networks go Persistent, Equivariant, and Continuous. ICML 2024 - [c39]Kirill Brilliantov, Amauri H. Souza, Vikas Garg:
Compositional PAC-Bayes: Generalization of GNNs with persistence and beyond. NeurIPS 2024 - [c38]Konstantinos Kogkalidis, Jean-Philippe Bernardy, Vikas Garg:
Algebraic Positional Encodings. NeurIPS 2024 - [c37]Giangiacomo Mercatali, Yogesh Verma, André Freitas, Vikas Garg:
Diffusion Twigs with Loop Guidance for Conditional Graph Generation. NeurIPS 2024 - [c36]Tuan Anh Pham, Vikas Garg:
What do Graph Neural Networks learn? Insights from Tropical Geometry. NeurIPS 2024 - 2023
- [c35]Yogesh Verma, Markus Heinonen, Vikas Garg:
AbODE: Ab initio antibody design using conjoined ODEs. ICML 2023: 35037-35050 - [c34]Timur Garipov, Sebastiaan De Peuter, Ge Yang, Vikas Garg, Samuel Kaski, Tommi S. Jaakkola:
Compositional Sculpting of Iterative Generative Processes. NeurIPS 2023 - [c33]Johanna Immonen, Amauri H. Souza, Vikas Garg:
Going beyond persistent homology using persistent homology. NeurIPS 2023 - 2022
- [c32]David Alvarez-Melis, Vikas Garg, Adam Kalai:
Are GANs overkill for NLP? NeurIPS 2022 - [c31]Giangiacomo Mercatali, André Freitas, Vikas Garg:
Symmetry-induced Disentanglement on Graphs. NeurIPS 2022 - [c30]Amauri H. Souza, Diego Mesquita, Samuel Kaski, Vikas Garg:
Provably expressive temporal graph networks. NeurIPS 2022 - [c29]Yogesh Verma, Samuel Kaski, Markus Heinonen, Vikas Garg:
Modular Flows: Differential Molecular Generation. NeurIPS 2022 - 2021
- [c28]Vikas K. Garg, Adam Tauman Kalai, Katrina Ligett, Zhiwei Steven Wu:
Learn to Expect the Unexpected: Probably Approximately Correct Domain Generalization. AISTATS 2021: 3574-3582 - 2020
- [c27]Vikas K. Garg, Tommi S. Jaakkola:
Predicting deliberative outcomes. ICML 2020: 3408-3418 - [c26]Vikas K. Garg, Stefanie Jegelka, Tommi S. Jaakkola:
Generalization and Representational Limits of Graph Neural Networks. ICML 2020: 3419-3430 - 2019
- [c25]John Ingraham, Vikas K. Garg, Regina Barzilay, Tommi S. Jaakkola:
Generative Models for Graph-Based Protein Design. DGS@ICLR 2019 - [c24]Vikas K. Garg, Tamar Pichkhadze:
Online Markov Decoding: Lower Bounds and Near-Optimal Approximation Algorithms. NeurIPS 2019: 5681-5691 - [c23]Vikas K. Garg, Tommi S. Jaakkola:
Solving graph compression via optimal transport. NeurIPS 2019: 8012-8023 - [c22]John Ingraham, Vikas K. Garg, Regina Barzilay, Tommi S. Jaakkola:
Generative Models for Graph-Based Protein Design. NeurIPS 2019: 15794-15805 - 2018
- [c21]Vikas K. Garg:
Supervising Unsupervised Learning. NeurIPS 2018: 4996-5006 - [c20]Vikas K. Garg, Ofer Dekel, Lin Xiao:
Learning SMaLL Predictors. NeurIPS 2018: 9143-9153 - [c19]Vikas K. Garg, Lin Xiao, Ofer Dekel:
Sparse Multi-Prototype Classification. UAI 2018: 704-714 - 2017
- [c18]Vikas K. Garg, Tommi S. Jaakkola:
Local Aggregative Games. NIPS 2017: 5341-5351 - 2016
- [c17]Vikas K. Garg, Cynthia Rudin, Tommi S. Jaakkola:
CRAFT: ClusteR-specific Assorted Feature selecTion. AISTATS 2016: 305-313 - [c16]Vikas K. Garg, Tommi S. Jaakkola:
Learning Tree Structured Potential Games. NIPS 2016: 1552-1560 - 2015
- [c15]Sukrit Shankar, Vikas K. Garg, Roberto Cipolla:
DEEP-CARVING: Discovering visual attributes by carving deep neural nets. CVPR 2015: 3403-3412 - 2014
- [c14]Risi Kondor, Nedelina Teneva, Vikas K. Garg:
Multiresolution Matrix Factorization. ICML 2014: 1620-1628 - 2013
- [c13]Vikas K. Garg, T. S. Jayram, Balakrishnan Narayanaswamy:
Online Optimization with Dynamic Temporal Uncertainty: Incorporating Short Term Predictions for Renewable Integration in Intelligent Energy Systems. AAAI 2013: 1291-1297 - [c12]Vikas K. Garg, Sneha Chaudhari, Ankur Narang:
Multi-regularization for Fuzzy Co-clustering. ICONIP (2) 2013: 67-75 - [c11]Priyanka Agrawal, Vikas K. Garg, Ramasuri Narayanam:
Link Label Prediction in Signed Social Networks. IJCAI 2013: 2591-2597 - [c10]Samory Kpotufe, Vikas K. Garg:
Adaptivity to Local Smoothness and Dimension in Kernel Regression. NIPS 2013: 3075-3083 - 2012
- [c9]Balakrishnan Narayanaswamy, Vikas K. Garg, T. S. Jayram:
Online optimization for the smart (micro) grid. e-Energy 2012: 19 - [c8]Balakrishnan Narayanaswamy, Vikas K. Garg, T. S. Jayram:
Prediction based storage management in the smart grid. SmartGridComm 2012: 498-503 - 2011
- [c7]Souvik Bhattacherjee, Ankur Narang, Vikas K. Garg:
High throughput data redundancy removal algorithm with scalable performance. HiPEAC 2011: 87-96 - 2010
- [c6]Vikas K. Garg, Ankur Narang, Souvik Bhattacherjee:
Real-time memory efficient data redundancy removal algorithm. CIKM 2010: 1259-1268 - [c5]Ankur Narang, Raj Gupta, Anupam Joshi, Vikas K. Garg:
Highly scalable parallel collaborative filtering algorithm. HiPC 2010: 1-10 - [c4]Vikas K. Garg, Nukala Viswanadham:
EcoSupply: A Machine Learning Framework for Analyzing the Impact of Ecosystem on Global Supply Chain Dynamics. SEAL 2010: 677-686 - [c3]Vikas K. Garg, M. Narasimha Murty:
EPIC: Efficient Integration of Partitional Clustering Algorithms for Classification. SEAL 2010: 706-710 - [c2]Vikas K. Garg:
Toward Optimal Disk Layout of Genome Scale Suffix Trees. SEAL 2010: 711-715 - 2009
- [c1]Vikas K. Garg, M. Narasimha Murty:
RACK: RApid clustering using K-means algorithm. CASE 2009: 621-626
Informal and Other Publications
- 2025
- [i33]Rafal Karczewski, Markus Heinonen, Vikas Garg:
Devil is in the Details: Density Guidance for Detail-Aware Generation with Flow Models. CoRR abs/2502.05807 (2025) - [i32]Yogesh Verma, Ayush Bharti, Vikas Garg:
Robust Simulation-Based Inference under Missing Data via Neural Processes. CoRR abs/2503.01287 (2025) - [i31]Haishan Wang, Arno Solin, Vikas Garg:
Heterophily-informed Message Passing. CoRR abs/2504.19785 (2025) - [i30]Rafal Karczewski, Markus Heinonen, Alison Pouplin, Søren Hauberg, Vikas Garg:
Spacetime Geometry of Denoising in Diffusion Models. CoRR abs/2505.17517 (2025) - 2024
- [i29]Alexandru Dumitrescu, Dani Korpela, Markus Heinonen, Yogesh Verma, Valerii Iakovlev, Vikas Garg, Harri Lähdesmäki:
Field-based Molecule Generation. CoRR abs/2402.15864 (2024) - [i28]Yogesh Verma, Markus Heinonen, Vikas Garg:
ClimODE: Climate and Weather Forecasting with Physics-informed Neural ODEs. CoRR abs/2404.10024 (2024) - [i27]Yue Jiang, Changkong Zhou, Vikas Garg, Antti Oulasvirta:
Graph4GUI: Graph Neural Networks for Representing Graphical User Interfaces. CoRR abs/2404.13521 (2024) - [i26]Fredrik Hagström, Vikas Garg, Fabricio Oliveira:
Employing Federated Learning for Training Autonomous HVAC Systems. CoRR abs/2405.00389 (2024) - [i25]Marshal Arijona Sinaga, Julien Martinelli, Vikas Garg, Samuel Kaski:
Heteroscedastic Preferential Bayesian Optimization with Informative Noise Distributions. CoRR abs/2405.14657 (2024) - [i24]Najwa Laabid, Severi Rissanen, Markus Heinonen, Arno Solin, Vikas Garg:
Alignment is Key for Applying Diffusion Models to Retrosynthesis. CoRR abs/2405.17656 (2024) - [i23]Yogesh Verma, Amauri H. Souza, Vikas Garg:
Topological Neural Networks go Persistent, Equivariant, and Continuous. CoRR abs/2406.03164 (2024) - [i22]Rafal Karczewski, Samuel Kaski, Markus Heinonen, Vikas Garg:
What Ails Generative Structure-based Drug Design: Too Little or Too Much Expressivity? CoRR abs/2408.06050 (2024) - [i21]Jonathan D. Thomas, Andrea Silvi, Devdatt P. Dubhashi, Vikas Garg, Moa Johansson:
ACE: Abstractions for Communicating Efficiently. CoRR abs/2409.20120 (2024) - [i20]Giangiacomo Mercatali, Yogesh Verma, André Freitas, Vikas Garg:
Diffusion Twigs with Loop Guidance for Conditional Graph Generation. CoRR abs/2410.24012 (2024) - [i19]Rafal Karczewski, Markus Heinonen, Vikas Garg:
Diffusion Models as Cartoonists! The Curious Case of High Density Regions. CoRR abs/2411.01293 (2024) - 2023
- [i18]Yogesh Verma, Markus Heinonen, Vikas Garg:
AbODE: Ab Initio Antibody Design using Conjoined ODEs. CoRR abs/2306.01005 (2023) - [i17]Timur Garipov, Sebastiaan De Peuter, Ge Yang, Vikas Garg, Samuel Kaski, Tommi S. Jaakkola:
Compositional Sculpting of Iterative Generative Processes. CoRR abs/2309.16115 (2023) - [i16]Johanna Immonen, Amauri H. Souza, Vikas Garg:
Going beyond persistent homology using persistent homology. CoRR abs/2311.06152 (2023) - [i15]Konstantinos Kogkalidis, Jean-Philippe Bernardy, Vikas Garg:
Algebraic Positional Encodings. CoRR abs/2312.16045 (2023) - 2022
- [i14]David Alvarez-Melis, Vikas Garg, Adam Tauman Kalai:
Why GANs are overkill for NLP. CoRR abs/2205.09838 (2022) - [i13]Amauri H. Souza, Diego Mesquita, Samuel Kaski, Vikas Garg:
Provably expressive temporal graph networks. CoRR abs/2209.15059 (2022) - [i12]Yogesh Verma, Samuel Kaski, Markus Heinonen, Vikas Garg:
Modular Flows: Differential Molecular Generation. CoRR abs/2210.06032 (2022) - 2020
- [i11]Vikas K. Garg, Adam Kalai, Katrina Ligett, Zhiwei Steven Wu:
Learn to Expect the Unexpected: Probably Approximately Correct Domain Generalization. CoRR abs/2002.05660 (2020) - [i10]Vikas K. Garg, Stefanie Jegelka, Tommi S. Jaakkola:
Generalization and Representational Limits of Graph Neural Networks. CoRR abs/2002.06157 (2020) - 2019
- [i9]Vikas K. Garg, Tommi S. Jaakkola:
Solving graph compression via optimal transport. CoRR abs/1905.12158 (2019) - [i8]Vikas K. Garg, Tommi S. Jaakkola:
Strategic Prediction with Latent Aggregative Games. CoRR abs/1905.12169 (2019) - [i7]Vikas K. Garg, Inderjit S. Dhillon, Hsiang-Fu Yu:
Multiresolution Transformer Networks: Recurrence is Not Essential for Modeling Hierarchical Structure. CoRR abs/1908.10408 (2019) - 2018
- [i6]Vikas K. Garg, Ofer Dekel, Lin Xiao:
Learning SMaLL Predictors. CoRR abs/1803.02388 (2018) - [i5]Vikas K. Garg, Tamar Pichkhadze:
Peek Search: Near-Optimal Online Markov Decoding. CoRR abs/1810.07301 (2018) - 2017
- [i4]Vikas K. Garg, Adam Kalai:
Supervising Unsupervised Learning. CoRR abs/1709.05262 (2017) - 2016
- [i3]Vikas K. Garg, Adam Tauman Kalai:
Meta-Unsupervised-Learning: A supervised approach to unsupervised learning. CoRR abs/1612.09030 (2016) - 2015
- [i2]Sukrit Shankar, Vikas K. Garg, Roberto Cipolla:
DEEP-CARVING: Discovering Visual Attributes by Carving Deep Neural Nets. CoRR abs/1504.04871 (2015) - [i1]Vikas K. Garg, Cynthia Rudin, Tommi S. Jaakkola:
CRAFT: ClusteR-specific Assorted Feature selecTion. CoRR abs/1506.07609 (2015)
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2025-06-27 19:34 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
