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Gorantla, S and Gowda, KN and Deshpande, A and Louis, A (2023) Socially Fair Center-Based and Linear Subspace Clustering. In: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2023, 18-22 September 2023, Turin, pp. 727-742.
Gorantla, S and Deshpande, A and Louis, A (2023) Sampling Ex-Post Group-Fair Rankings. In: International Joint Conference on Artificial Intelligence, IJCAI 2023, 19-25 August 2023, Macao, pp. 409-417.
Louis, A and Nasre, M and Nimbhorkar, P and Sankar, GS (2023) Online Algorithms for Matchings with Proportional Fairness Constraints and Diversity Constraints. In: UNSPECIFIED, pp. 1601-1608.
Ghoshal, S and Louis, A (2022) Approximating CSPs with Outliers. In: 25th International Conference on Approximation Algorithms for Combinatorial Optimization Problems and the 26th International Conference on Randomization and Computation, APPROX/RANDOM 2022, 19 - 21 September 2022, Virtual, Urbana-Champaign.
Khanna, Y and Louis, A and Paul, R (2021) Independent Sets in Semi-random Hypergraphs. In: Lecture Notes in Computer Science, 9 August - 11 August 2021, Virtual, Online, pp. 528-542.
Yadati, N and Gao, T and Asoodeh, S and Talukdar, P and Louis, A (2021) Graph Neural Networks for Soft Semi-Supervised Learning on Hypergraphs. In: 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining, 11-14 May 2021, pp. 447-458.
Patel, D and Khan, A and Louis, A (2021) Group fairness for knapsack problems. In: 20th International Conference on Autonomous Agents and Multiagent Systems, 3-7 May 2021, pp. 989-997.
Sankar, GS and Louis, A and Nasre, M and Nimbhorkar, P (2021) Matchings with Group Fairness Constraints: Online and Offline Algorithms. In: 30th International Joint Conference on Artificial Intelligence, IJCAI 2021, 19-27 Aug 2021, Virtual, Online, pp. 377-383.
Yadati, N and Nitin, V and Nimishakavi, M and Yadav, P and Louis, A and Talukdar, P (2020) NHP: Neural Hypergraph Link Prediction. In: 29th ACM International Conference on Information and Knowledge Management, 19-23 oct 2020, Virtual, Online, pp. 1705-1714.
Louis, A and Venkat, R (2019) Planted models for k-way edge and vertex expansion. In: 39th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science, FSTTCS 2019, 11-13 December 2019, Institute of Technology BombayBombay; India.
Ghoshal, S and Louis, A and Raychaudhury, R (2019) Approximation algorithms for partially colorable graphs. In: 22nd International Conference on Approximation Algorithms for Combinatorial Optimization Problems and 23rd International Conference on Randomization and Computation, APPROX/RANDOM 2019, 20 - 22 September 2019, Cambridge.
Sankararaman, KA and Louis, A and Goyal, N (2019) Stability of linear structural equation models of causal inference. In: 35th Conference on Uncertainty in Artificial Intelligence, UAI 2019, 22-25 Jul 2019, Tel Aviv, Israel.
Yadati, N and Yadav, P and Louis, A and Nimishakavi, M and Nitin, V and Talukdar, P (2019) HyperGCN: A new method of training graph convolutional networks on hypergraphs. In: 33rd Annual Conference on Neural Information Processing Systems, NeurIPS 2019, 8 - 14 December 2019, Vancouver.
Louis, A and Venkat, R (2018) Semi-random graphs with planted sparse vertex cuts: Algorithms for exact and approximate recovery. In: 45th International Colloquium on Automata, Languages, and Programming, ICALP 2018, 9 - 13 July 2018, Prague.
Kumar, A and Louis, A and Tulsiani, M (2018) Finding pseudorandom colorings of pseudorandom graphs. In: 37th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2017), 12 - 14 December 2017, Kanpur.
