Publications
You can also find all my articles on my Google Scholar profile
(“*” indicates equal contribution)
Optimization
HyperDPO: Conditioned One-Shot Multi-Objective Fine-Tuning Framework Yinuo Ren, Tesi Xiao, Michael Shavlovsky, Lexing Ying, Holakou Rahmanian. NeurIPS Workshop on Fine-Tuning in Modern Machine Learning: Principles and Scalability, 2024 [pdf]
Optimal Algorithms for Stochastic Bilevel Optimization under Relaxed Smoothness Conditions Xuxing Chen*, Tesi Xiao*, Krishnakumar Balasubramanian. Journal of Machine Learning Research, 2024 [pdf]
A Sinkhorn-type Algorithm for Constrained Optimal Transport Xun Tang, Holakou Rahmanian, Michael Shavlovsky, Kiran Koshy Thekumparampil, Tesi Xiao, Lexing Ying. ArXiv preprint, 2024 [pdf]
Multi-Objective Optimization via Wasserstein-Fisher-Rao Gradient Flow Yinuo Ren, Tesi Xiao, Tanmay Gangwani, Anshuka Rangi, Holakou Rahmanian, Lexing Ying, Subhajit Sanyal. AISTATS, 2024 [pdf][code][poster]
Accelerating Sinkhorn Algorithm with Sparse Newton Iterations Xun Tang, Michael Shavlovsky, Holakou Rahmanian, Elisa Tardini, Kiran Koshy Thekumparampil, Tesi Xiao, Lexing Ying. ICLR, 2024 [pdf]
A One-Sample Decentralized Proximal Algorithm for Non-Convex Stochastic Composite Optimization Tesi Xiao*, Xuxing Chen*, Krishnakumar Balasubramanian, Saeed Ghadimi. UAI, 2023 [pdf] [code] [poster]
A Projection-free Algorithm for Constrained Stochastic Multi-level Composition Optimization Tesi Xiao, Krishnakumar Balasubramanian, Saeed Ghadimi. NeurIPS, 2022 [pdf] [poster]
Improved Complexities for Stochastic Conditional Gradient Methods under Interpolation-like Conditions Tesi Xiao, Krishnakumar Balasubramanian, Saeed Ghadimi. Operations Research Letters, 2022 [pdf]
Statistical Inference for Polyak-Ruppert Averaged Stochastic Zeroth-order Gradient Algorithm Yanhao Jin*, Tesi Xiao*, Krishnakumar Balasubramanian. ArXiv preprint (2021) [pdf]
Recommender Systems & Ranking
Orbit: A Framework for Designing and Evaluating Multi-objective Rankers Chenyang Yang, Tesi Xiao*, Michael Shavlovsky*, Christian Kästner, Tongshuang Wu. International Conference on Intelligent User Interfaces (IUI), March 2025 (to appear) [pdf]
Towards Sequential Counterfactual Learning to Rank Tesi Xiao, Branislav Kveton, Sumeet Katariya, Tanmay Gangwani, Anshuka Rangi. SIGIR-AP. 2023. [pdf]
Field-wise Embedding Size Search via Structural Hard Auxiliary Mask Pruning for Click-Through Rate Prediction Tesi Xiao, Xia Xiao, Ming Chen, Youlong Cheng. CIKM, 2022, DL4SR (Deep Learning for Search and Recommendation) workshop [pdf]
Deep Learning
How Does Noise Help Robustness? Explanation and Exploration Under the Continuous Limit Xuanqing Liu, Tesi Xiao, Si Si, Qin Cao, Sanjiv Kumar, Cho-Jui Hsieh. CVPR, 2020 (Oral Presentation, 5.7% out of 5,865) [pdf]
Neural SDE: Stabilizing Neural ODE Networks with Stochastic Noise Xuanqing Liu, Tesi Xiao, Si Si, Qin Cao, Sanjiv Kumar, Cho-Jui Hsieh. ArXiv preprint (2019) [pdf]