Towards a theory of societal co-evolution: Individualism versus collectivism. - Kartik Ahuja; Simpson Zhang; Mihaela van der Schaar (GlobalSIP, 2014)

Spectrum sharing for delay-sensitive applications with continuing QoS guarantees. - Yuanzhang Xiao; Kartik Ahuja; Mihaela van der Schaar (GLOBECOM, 2014)

To participate or not in spectrum auctions with entry fee: Bayesian game theoretic approach. - Kartik Ahuja; Mai H. Hassan; Md. Jahangir Hossain 002 (WCNC, 2014)

Evolution of Social Networks: A Microfounded Model. - Ahmed M. Alaa; Kartik Ahuja; Mihaela van der Schaar (CoRR, 2015)

Distributed Interference Management Policies for Heterogeneous Small Cell Networks. - Kartik Ahuja; Yuanzhang Xiao; Mihaela van der Schaar (IEEE J. Sel. Areas Commun., 2015)

The Population Dynamics of Websites: [Extended Abstract]. - Kartik Ahuja; Simpson Zhang; Mihaela van der Schaar (SIGMETRICS Perform. Evaluation Rev., 2015)

Self-Organizing Networks of Information Gathering Cognitive Agents. - Ahmed M. Alaa; Kartik Ahuja; Mihaela van der Schaar (IEEE Trans. Cogn. Commun. Netw., 2015)

Efficient Interference Management Policies for Femtocell Networks. - Kartik Ahuja; Yuanzhang Xiao; Mihaela van der Schaar (IEEE Trans. Wirel. Commun., 2015)

Repeated Matching Mechanism Design with Moral Hazard and Adverse Selection. - Kartik Ahuja; Mihaela van der Schaar (CoRR, 2016)

DPSCREEN: Dynamic Personalized Screening. - Kartik Ahuja; William R. Zame; Mihaela van der Schaar (NIPS, 2017)

Piecewise Approximations of Black Box Models for Model Interpretation. - Kartik Ahuja; William R. Zame; Mihaela van der Schaar (CoRR, 2018)

Risk-Stratify: Confident Stratification Of Patients Based On Risk. - Kartik Ahuja; Mihaela van der Schaar (CoRR, 2018)

A Micro-Foundation of Social Capital in Evolving Social Networks. - Ahmed M. Alaa; Kartik Ahuja; Mihaela van der Schaar (IEEE Trans. Netw. Sci. Eng., 2018)

Estimating Kullback-Leibler Divergence Using Kernel Machines. - Kartik Ahuja (ACSSC, 2019)

Joint Concordance Index. - Kartik Ahuja; Mihaela van der Schaar (ACSSC, 2019)

Optimal Piecewise Approximations for Model Interpretation. - Kartik Ahuja; William R. Zame; Mihaela van der Schaar (ACSSC, 2019)

Optimization Methods for Resource Allocation and Machine Learning Applications. - Kartik Ahuja (2019)

Invariant Risk Minimization Games. - Kartik Ahuja; Karthikeyan Shanmugam; Kush R. Varshney; Amit Dhurandhar (ICML, 2020)

Learning to Initialize Gradient Descent Using Gradient Descent. - Kartik Ahuja; Amit Dhurandhar; Kush R. Varshney (CoRR, 2020)

Dynamic Matching and Allocation of Tasks. - Kartik Ahuja; Mihaela van der Schaar (ACM Trans. Economics and Comput., 2020)

Linear Regression Games: Convergence Guarantees to Approximate Out-of-Distribution Solutions. - Kartik Ahuja; Karthikeyan Shanmugam; Amit Dhurandhar (AISTATS, 2021)

Treatment Effect Estimation Using Invariant Risk Minimization. - Abhin Shah; Kartik Ahuja; Karthikeyan Shanmugam; Dennis Wei; Kush R. Varshney; Amit Dhurandhar (ICASSP, 2021)

Empirical or Invariant Risk Minimization? A Sample Complexity Perspective. - Kartik Ahuja; Jun Wang 006; Amit Dhurandhar; Karthikeyan Shanmugam; Kush R. Varshney (ICLR, 2021)

Can Subnetwork Structure Be the Key to Out-of-Distribution Generalization? - Dinghuai Zhang; Kartik Ahuja; Yilun Xu; Yisen Wang 001; Aaron C. Courville (ICML, 2021)

Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization. - Kartik Ahuja; Ethan Caballero; Dinghuai Zhang; Jean-Christophe Gagnon-Audet; Yoshua Bengio; Ioannis Mitliagkas; Irina Rish (NeurIPS, 2021)

Adversarial Feature Desensitization. - Pouya Bashivan; Reza Bayat; Adam Ibrahim; Kartik Ahuja; Mojtaba Faramarzi; Touraj Laleh; Blake A. Richards; Irina Rish (NeurIPS, 2021)

Conditionally independent data generation. - Kartik Ahuja; Prasanna Sattigeri; Karthikeyan Shanmugam; Dennis Wei; Karthikeyan Natesan Ramamurthy; Murat Kocaoglu (UAI, 2021)

SAND-mask: An Enhanced Gradient Masking Strategy for the Discovery of Invariances in Domain Generalization. - Soroosh Shahtalebi; Jean-Christophe Gagnon-Audet; Touraj Laleh; Mojtaba Faramarzi; Kartik Ahuja; Irina Rish (CoRR, 2021)

Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization. - Kartik Ahuja; Ethan Caballero; Dinghuai Zhang; Yoshua Bengio; Ioannis Mitliagkas; Irina Rish (CoRR, 2021)

Finding Valid Adjustments under Non-ignorability with Minimal DAG Knowledge. - Abhin Shah; Karthikeyan Shanmugam; Kartik Ahuja (AISTATS, 2022)

Towards efficient representation identification in supervised learning. - Kartik Ahuja; Divyat Mahajan; Vasilis Syrgkanis; Ioannis Mitliagkas (CLeaR, 2022)

Properties from mechanisms: an equivariance perspective on identifiable representation learning. - Kartik Ahuja; Jason S. Hartford; Yoshua Bengio (ICLR, 2022)

Weakly Supervised Representation Learning with Sparse Perturbations. - Kartik Ahuja; Jason S. Hartford; Yoshua Bengio (NeurIPS, 2022)

WOODS: Benchmarks for Out-of-Distribution Generalization in Time Series Tasks. - Jean-Christophe Gagnon-Audet; Kartik Ahuja; Mohammad-Javad Darvishi Bayazi; Guillaume Dumas; Irina Rish (CoRR, 2022)

FL Games: A Federated Learning Framework for Distribution Shifts. - Sharut Gupta; Kartik Ahuja; Mohammad Havaei; Niladri Chatterjee; Yoshua Bengio (CoRR, 2022)

Recycling diverse models for out-of-distribution generalization. - Alexandre Ramé; Kartik Ahuja; Jianyu Zhang; Matthieu Cord; Léon Bottou; David Lopez-Paz (CoRR, 2022)

Interventional Causal Representation Learning. - Kartik Ahuja; Divyat Mahajan; Yixin Wang; Yoshua Bengio (ICML, 2023)

Why does Throwing Away Data Improve Worst-Group Error? - Kamalika Chaudhuri; Kartik Ahuja; Martín Arjovsky; David Lopez-Paz (ICML, 2023)

Model Ratatouille: Recycling Diverse Models for Out-of-Distribution Generalization. - Alexandre Ramé; Kartik Ahuja; Jianyu Zhang; Matthieu Cord; Léon Bottou; David Lopez-Paz (ICML, 2023)

Locally Invariant Explanations: Towards Stable and Unidirectional Explanations through Local Invariant Learning. - Amit Dhurandhar; Karthikeyan Natesan Ramamurthy; Kartik Ahuja; Vijay Arya (NeurIPS, 2023)

Reusable Slotwise Mechanisms. - Bailey Trang Nguyen; Amin Mansouri; Kanika Madan; Khuong Nguyen; Kartik Ahuja; Dianbo Liu; Yoshua Bengio (NeurIPS, 2023)

A Closer Look at In-Context Learning under Distribution Shifts. - Kartik Ahuja; David Lopez-Paz (CoRR, 2023)

Identifiability of Discretized Latent Coordinate Systems via Density Landmarks Detection. - Vitória Barin Pacela; Kartik Ahuja; Simon Lacoste-Julien; Pascal Vincent (CoRR, 2023)

WOODS: Benchmarks for Out-of-Distribution Generalization in Time Series. - Jean-Christophe Gagnon-Audet; Kartik Ahuja; Mohammad-Javad Darvishi Bayazi; Pooneh Mousavi; Guillaume Dumas; Irina Rish (Trans. Mach. Learn. Res., 2023)

Empirical Study on Optimizer Selection for Out-of-Distribution Generalization. - Hiroki Naganuma; Kartik Ahuja; Shiro Takagi; Tetsuya Motokawa; Rio Yokota; Kohta Ishikawa; Ikuro Sato; Ioannis Mitliagkas (Trans. Mach. Learn. Res., 2023)

Multi-Domain Causal Representation Learning via Weak Distributional Invariances. - Kartik Ahuja; Amin Mansouri; Yixin Wang (AISTATS, 2024)

On the Identifiability of Quantized Factors. - Vitória Barin Pacela; Kartik Ahuja; Simon Lacoste-Julien; Pascal Vincent (CLeaR, 2024)

Context is Environment. - Sharut Gupta; Stefanie Jegelka; David Lopez-Paz; Kartik Ahuja (ICLR, 2024)

On Provable Length and Compositional Generalization. - Kartik Ahuja; Amin Mansouri (CoRR, 2024)

Robust Data Pruning: Uncovering and Overcoming Implicit Bias. - Artem Vysogorets; Kartik Ahuja; Julia Kempe (CoRR, 2024)

Compositional Risk Minimization. - Divyat Mahajan; Mohammad Pezeshki; Ioannis Mitliagkas; Kartik Ahuja; Pascal Vincent (CoRR, 2024)

DRoP: Distributionally Robust Data Pruning. - Artem M. Vysogorets; Kartik Ahuja; Julia Kempe (ICLR, 2025)

Unveiling Simplicities of Attention: Adaptive Long-Context Head Identification. - Konstantin Donhauser; Charles Arnal; Mohammad Pezeshki; Vivien Cabannes; David Lopez-Paz; Kartik Ahuja (CoRR, 2025)