Raj, Anant

28 publications

COLT 2025 Beyond Propagation of Chaos: A Stochastic Algorithm for Mean Field Optimization Chandan Tankala, Dheeraj Nagaraj, Anant Raj

TMLR 2025 Reweighting Improves Conditional Risk Bounds Yikai Zhang, Jiahe Lin, Fengpei Li, Songzhu Zheng, Anant Raj, Anderson Schneider, Yuriy Nevmyvaka

ICML 2024 From Inverse Optimization to Feasibility to ERM Saurabh Kumar Mishra, Anant Raj, Sharan Vaswani

TMLR 2024 Learning to Abstain from Uninformative Data Yikai Zhang, Songzhu Zheng, Mina Dalirrooyfard, Pengxiang Wu, Anderson Schneider, Anant Raj, Yuriy Nevmyvaka, Chao Chen

NeurIPS 2024 Small Steps No More: Global Convergence of Stochastic Gradient Bandits for Arbitrary Learning Rates Jincheng Mei, Bo Dai, Alekh Agarwal, Sharan Vaswani, Anant Raj, Csaba Szepesvári, Dale Schuurmans

ICML 2023 Algorithmic Stability of Heavy-Tailed SGD with General Loss Functions Anant Raj, Lingjiong Zhu, Mert Gurbuzbalaban, Umut Simsekli

ALT 2023 Algorithmic Stability of Heavy-Tailed Stochastic Gradient Descent on Least Squares Anant Raj, Melih Barsbey, Mert Gurbuzbalaban, Lingjiong Zhu, Umut Şim\scekli

NeurIPS 2023 Efficient Sampling of Stochastic Differential Equations with Positive Semi-Definite Models Anant Raj, Umut Simsekli, Alessandro Rudi

AISTATS 2023 Explicit Regularization in Overparametrized Models via Noise Injection Antonio Orvieto, Anant Raj, Hans Kersting, Francis Bach

NeurIPSW 2023 Practical Principled Policy Optimization for Finite MDPs Michael Lu, Matin Aghaei, Anant Raj, Sharan Vaswani

NeurIPS 2023 Uniform-in-Time Wasserstein Stability Bounds for (Noisy) Stochastic Gradient Descent Lingjiong Zhu, Mert Gurbuzbalaban, Anant Raj, Umut Simsekli

COLT 2023 Utilising the CLT Structure in Stochastic Gradient Based Sampling : Improved Analysis and Faster Algorithms Aniket Das, Dheeraj M. Nagaraj, Anant Raj

AISTATS 2022 Faster Rates, Adaptive Algorithms, and Finite-Time Bounds for Linear Composition Optimization and Gradient TD Learning Anant Raj, Pooria Joulani, Andras Gyorgy, Csaba Szepesvari

TMLR 2022 Causal Feature Selection via Orthogonal Search Ashkan Soleymani, Anant Raj, Stefan Bauer, Bernhard Schölkopf, Michel Besserve

ICML 2022 Convergence of Uncertainty Sampling for Active Learning Anant Raj, Francis Bach

AISTATS 2021 Explicit Regularization of Stochastic Gradient Methods Through Duality Anant Raj, Francis Bach

ICML 2020 A Simpler Approach to Accelerated Optimization: Iterative Averaging Meets Optimism Pooria Joulani, Anant Raj, Andras Gyorgy, Csaba Szepesvari

NeurIPS 2020 Dual Instrumental Variable Regression Krikamol Muandet, Arash Mehrjou, Si Kai Lee, Anant Raj

AISTATS 2020 Importance Sampling via Local Sensitivity Anant Raj, Cameron Musco, Lester Mackey

NeurIPS 2020 Stochastic Stein Discrepancies Jackson Gorham, Anant Raj, Lester W. Mackey

ECML-PKDD 2019 A Differentially Private Kernel Two-Sample Test Anant Raj, Ho Chung Leon Law, Dino Sejdinovic, Mijung Park

AISTATS 2019 Sobolev Descent Youssef Mroueh, Tom Sercu, Anant Raj

ICML 2018 On Matching Pursuit and Coordinate Descent Francesco Locatello, Anant Raj, Sai Praneeth Karimireddy, Gunnar Raetsch, Bernhard Schölkopf, Sebastian Stich, Martin Jaggi

ICLR 2018 Sobolev GAN Youssef Mroueh, Chun-Liang Li, Tom Sercu, Anant Raj, Yu Cheng

ICML 2017 Approximate Steepest Coordinate Descent Sebastian U. Stich, Anant Raj, Martin Jaggi

AISTATS 2017 Local Group Invariant Representations via Orbit Embeddings Anant Raj, Abhishek Kumar, Youssef Mroueh, Tom Fletcher, Bernhard Schölkopf

NeurIPS 2017 Safe Adaptive Importance Sampling Sebastian U Stich, Anant Raj, Martin Jaggi

NeurIPS 2014 Scalable Kernel Methods via Doubly Stochastic Gradients Bo Dai, Bo Xie, Niao He, Yingyu Liang, Anant Raj, Maria-Florina F Balcan, Le Song