Significance of variable height-bandwidth group delay filters in the spectral reconstruction of speech. - Devanshu Arya; Anant Raj; Rajesh M. Hegde (INTERSPEECH, 2013)

Scalable Kernel Methods via Doubly Stochastic Gradients. - Bo Dai 001; Bo Xie 002; Niao He; Yingyu Liang; Anant Raj; Maria-Florina Balcan; Le Song (NIPS, 2014)

Subspace Alignment Based Domain Adaptation for RCNN Detector. - Anant Raj; Vinay P. Namboodiri; Tinne Tuytelaars (BMVC, 2015)

Mind the Gap: Subspace based Hierarchical Domain Adaptation. - Anant Raj; Vinay P. Namboodiri; Tinne Tuytelaars (CoRR, 2015)

Unsupervised Domain Adaptation in the Wild: Dealing with Asymmetric Label Sets. - Ayush Mittal; Anant Raj; Vinay P. Namboodiri; Tinne Tuytelaars (CoRR, 2016)

Screening Rules for Convex Problems. - Anant Raj; Jakob Olbrich; Bernd Gärtner; Bernhard Schölkopf; Martin Jaggi (CoRR, 2016)

Local Group Invariant Representations via Orbit Embeddings. - Anant Raj; Abhishek Kumar 001; Youssef Mroueh; Tom Fletcher; Bernhard Schölkopf (AISTATS, 2017)

Approximate Steepest Coordinate Descent. - Sebastian U. Stich; Anant Raj; Martin Jaggi (ICML, 2017)

Safe Adaptive Importance Sampling. - Sebastian U. Stich; Anant Raj; Martin Jaggi (NIPS, 2017)

Sobolev GAN. - Youssef Mroueh; Chun-Liang Li; Tom Sercu; Anant Raj; Yu Cheng (ICLR (Poster), 2018)

On Matching Pursuit and Coordinate Descent. - Francesco Locatello; Anant Raj; Sai Praneeth Karimireddy; Gunnar Rätsch; Bernhard Schölkopf; Sebastian U. Stich; Martin Jaggi (ICML, 2018)

Revisiting First-Order Convex Optimization Over Linear Spaces. - Francesco Locatello; Anant Raj; Sai Praneeth Karimireddy; Gunnar Rätsch; Bernhard Schölkopf; Sebastian U. Stich; Martin Jaggi (CoRR, 2018)

SVRG meets SAGA: k-SVRG - A Tale of Limited Memory. - Anant Raj; Sebastian U. Stich (CoRR, 2018)

Regularized Kernel and Neural Sobolev Descent: Dynamic MMD Transport. - Youssef Mroueh; Tom Sercu; Anant Raj (CoRR, 2018)

Sobolev Descent. - Youssef Mroueh; Tom Sercu; Anant Raj (AISTATS, 2019)

A Differentially Private Kernel Two-Sample Test. - Anant Raj; Ho Chung Leon Law; Dino Sejdinovic; Mijung Park (ECML/PKDD (1), 2019)

Dual IV: A Single Stage Instrumental Variable Regression. - Krikamol Muandet; Arash Mehrjou; Si Kai Lee; Anant Raj (CoRR, 2019)

Phonon dispersion using the ratio of zero-time correlations among conjugate variables: Computing full phonon dispersion surface of graphene. - Anant Raj; Jacob Eapen (Comput. Phys. Commun., 2019)

Importance Sampling via Local Sensitivity. - Anant Raj; Cameron Musco; Lester Mackey (AISTATS, 2020)

A simpler approach to accelerated optimization: iterative averaging meets optimism. - Pooria Joulani; Anant Raj; András György 001; Csaba Szepesvári (ICML, 2020)

Stochastic Stein Discrepancies. - Jackson Gorham; Anant Raj; Lester Mackey (NeurIPS, 2020)

Dual Instrumental Variable Regression. - Krikamol Muandet; Arash Mehrjou; Si Kai Lee; Anant Raj (NeurIPS, 2020)

Model-specific Data Subsampling with Influence Functions. - Anant Raj; Cameron Musco; Lester Mackey; Nicoló Fusi (CoRR, 2020)

Non-stationary Online Regression. - Anant Raj; Pierre Gaillard; Christophe Saad (CoRR, 2020)

Explicit Regularization of Stochastic Gradient Methods through Duality. - Anant Raj; Francis R. Bach (AISTATS, 2021)

Faster Rates, Adaptive Algorithms, and Finite-Time Bounds for Linear Composition Optimization and Gradient TD Learning. - Anant Raj; Pooria Joulani; András György 001; Csaba Szepesvári (AISTATS, 2022)

Convergence of Uncertainty Sampling for Active Learning. - Anant Raj; Francis R. Bach (ICML, 2022)

MAViC: Multimodal Active Learning for Video Captioning. - Gyanendra Das; Xavier Thomas; Anant Raj; Vikram Gupta (CoRR, 2022)

Causal Feature Selection via Orthogonal Search. - Ashkan Soleymani; Anant Raj; Stefan Bauer; Bernhard Schölkopf; Michel Besserve (Trans. Mach. Learn. Res., 2022)

Explicit Regularization in Overparametrized Models via Noise Injection. - Antonio Orvieto; Anant Raj; Hans Kersting; Francis R. Bach (AISTATS, 2023)

Algorithmic Stability of Heavy-Tailed Stochastic Gradient Descent on Least Squares. - Anant Raj; Melih Barsbey; Mert Gürbüzbalaban; Lingjiong Zhu; Umut Simsekli (ALT, 2023)

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

Algorithmic Stability of Heavy-Tailed SGD with General Loss Functions. - Anant Raj; Lingjiong Zhu; Mert Gürbüzbalaban; Umut Simsekli (ICML, 2023)

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

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

Open X-Embodiment: Robotic Learning Datasets and RT-X Models. - Open X.-Embodiment Collaboration; Abhishek Padalkar; Acorn Pooley; Ajinkya Jain; Alex Bewley; Alexander Herzog; Alex Irpan; Alexander Khazatsky; Anant Raj; Anikait Singh; Anthony Brohan; Antonin Raffin; et al. (CoRR, 2023)

Variational Principles for Mirror Descent and Mirror Langevin Dynamics. - Belinda Tzen; Anant Raj; Maxim Raginsky; Francis R. Bach (IEEE Control. Syst. Lett., 2023)

From Inverse Optimization to Feasibility to ERM. - Saurabh Mishra; Anant Raj; Sharan Vaswani (ICML, 2024)

Small steps no more: Global convergence of stochastic gradient bandits for arbitrary learning rates. - Jincheng Mei; Bo Dai 001; Alekh Agarwal; Sharan Vaswani; Anant Raj; Csaba Szepesvári; Dale Schuurmans (NeurIPS, 2024)

Towards Principled, Practical Policy Gradient for Bandits and Tabular MDPs. - Michael Lu; Matin Aghaei; Anant Raj; Sharan Vaswani (RLJ, 2024)

Deep Generative Sampling in the Dual Divergence Space: A Data-efficient & Interpretative Approach for Generative AI. - Sahil Garg; Anderson Schneider; Anant Raj; Kashif Rasul; Yuriy Nevmyvaka; Sneihil Gopal; Amit Dhurandhar; Guillermo A. Cecchi; Irina Rish (CoRR, 2024)

Learning to Abstain From Uninformative Data. - Yikai Zhang 003; Songzhu Zheng; Mina Dalirrooyfard; Pengxiang Wu; Anderson Schneider; Anant Raj; Yuriy Nevmyvaka; Chao Chen 012 (Trans. Mach. Learn. Res., 2024)

Beyond propagation of chaos: A stochastic algorithm for mean field optimization. - Chandan Tankala; Dheeraj Nagaraj; Anant Raj (COLT, 2025)

Reweighting Improves Conditional Risk Bounds. - Yikai Zhang 003; Jiahe Lin; Fengpei Li; Songzhu Zheng; Anant Raj; Anderson Schneider; Yuriy Nevmyvaka (Trans. Mach. Learn. Res., 2025)

Exploring the Capabilities of the Frontier Large Language Models for Nuclear Energy Research. - Ahmed Almeldein; Mohammed Alnaggar; Rick Archibald; Tom Beck; Arpan Biswas; Rike Bostelmann; Wes Brewer; Chris Bryan; Christopher Calle; Cihangir Celik; Rajni Chahal; Jong Youl Choi; et al. (CoRR, 2025)