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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
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2020 – today
- 2025
[i31]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)- [i30]Yogesh Verma, Ayush Bharti, Vikas Garg:
Robust Simulation-Based Inference under Missing Data via Neural Processes. CoRR abs/2503.01287 (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 - [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
- [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 - [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
- [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 - [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) - 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 - [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)
2010 – 2019
- 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 - [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
- [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 - [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
- [c18]Vikas K. Garg, Tommi S. Jaakkola:
Local Aggregative Games. NIPS 2017: 5341-5351 - [i4]Vikas K. Garg, Adam Kalai:
Supervising Unsupervised Learning. CoRR abs/1709.05262 (2017) - 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 - [i3]Vikas K. Garg, Adam Tauman Kalai:
Meta-Unsupervised-Learning: A supervised approach to unsupervised learning. CoRR abs/1612.09030 (2016) - 2015
- [c15]Sukrit Shankar, Vikas K. Garg, Roberto Cipolla:
DEEP-CARVING: Discovering visual attributes by carving deep neural nets. CVPR 2015: 3403-3412 - [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) - 2014
- [c14]Risi Kondor, Nedelina Teneva, Vikas K. Garg:
Multiresolution Matrix Factorization. ICML 2014: 1620-1628 - 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) - [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
2000 – 2009
- 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) - [c1]Vikas K. Garg, M. Narasimha Murty:
RACK: RApid clustering using K-means algorithm. CASE 2009: 621-626
Coauthor Index
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