A Practical Markov Chain Monte Carlo Approach to Decision Problems. - Timothy Huang; Yuriy Nevmyvaka (FLAIRS, 2001)

Electronic Trading in Order-Driven Markets: Efficient Execution. - Yuriy Nevmyvaka; Michael J. Kearns; Amy Papandreou; Katia P. Sycara (CEC, 2005)

Reinforcement learning for optimized trade execution. - Yuriy Nevmyvaka; Yi Feng; Michael J. Kearns (ICML, 2006)

Censored exploration and the dark pool problem. - Kuzman Ganchev; Yuriy Nevmyvaka; Michael J. Kearns; Jennifer Wortman Vaughan (Commun. ACM, 2010)

Pursuit-Evasion Without Regret, with an Application to Trading. - Lili Dworkin; Michael J. Kearns; Yuriy Nevmyvaka (ICML, 2014)

AutoML Decathlon: Diverse Tasks, Modern Methods, and Efficiency at Scale. - Nicholas Roberts; Samuel Guo; Cong Xu; Ameet Talwalkar; David Lander; Lvfang Tao; Linhang Cai; Shuaicheng Niu; Jianyu Heng; Hongyang Qin; Minwen Deng; Johannes Hog; et al. (NeurIPS (Competition and Demos), 2021)

8th SIGKDD International Workshop on Mining and Learning from Time Series - Deep Forecasting: Models, Interpretability, and Applications. - Sanjay Purushotham; Jun Huan; Cong Shen 001; Dongjin Song; Yuyang Wang 001; Jan Gasthaus; Hilaf Hasson; Youngsuk Park; Sungyong Seo; Yuriy Nevmyvaka (KDD, 2022)

Estimating transfer entropy under long ranged dependencies. - Sahil Garg; Umang Gupta; Yu Chen; Syamantak Datta Gupta; Yeshaya Adler; Anderson Schneider; Yuriy Nevmyvaka (UAI, 2022)

Modeling Temporal Data as Continuous Functions with Process Diffusion. - Marin Bilos; Kashif Rasul; Anderson Schneider; Yuriy Nevmyvaka; Stephan Günnemann (CoRR, 2022)

Risk Bounds on Aleatoric Uncertainty Recovery. - Yikai Zhang 003; Jiahe Lin; Fengpei Li; Yeshaya Adler; Kashif Rasul; Anderson Schneider; Yuriy Nevmyvaka (AISTATS, 2023)

Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusion. - Marin Bilos; Kashif Rasul; Anderson Schneider; Yuriy Nevmyvaka; Stephan Günnemann (ICML, 2023)

Provably Convergent Schrödinger Bridge with Applications to Probabilistic Time Series Imputation. - Yu Chen; Wei Deng 002; Shikai Fang; Fengpei Li; Nicole Tianjiao Yang; Yikai Zhang 003; Kashif Rasul; Shandian Zhe; Anderson Schneider; Yuriy Nevmyvaka (ICML, 2023)

The 9th SIGKDD International Workshop on Mining and Learning from Time Series. - Sanjay Purushotham; Dongjin Song; Qingsong Wen; Jun Huan; Cong Shen 001; Yuriy Nevmyvaka (KDD, 2023)

Nearly Tight Bounds For Differentially Private Multiway Cut. - Mina Dalirrooyfard; Slobodan Mitrovic; Yuriy Nevmyvaka (NeurIPS, 2023)

Detection of Short-Term Temporal Dependencies in Hawkes Processes with Heterogeneous Background Dynamics. - Yu Chen; Fengpei Li; Anderson Schneider; Yuriy Nevmyvaka; Asohan Amarasingham; Henry Lam (UAI, 2023)

In- or out-of-distribution detection via dual divergence estimation. - Sahil Garg; Sanghamitra Dutta; Mina Dalirrooyfard; Anderson Schneider; Yuriy Nevmyvaka (UAI, 2023)

Information theoretic clustering via divergence maximization among clusters. - Sahil Garg; Mina Dalirrooyfard; Anderson Schneider; Yeshaya Adler; Yuriy Nevmyvaka; Yu Chen; Fengpei Li; Guillermo A. Cecchi (UAI, 2023)

Short-term Temporal Dependency Detection under Heterogeneous Event Dynamic with Hawkes Processes. - Yu Chen; Fengpei Li; Anderson Schneider; Yuriy Nevmyvaka; Asohan Amarasingham; Henry Lam (CoRR, 2023)

Lag-Llama: Towards Foundation Models for Time Series Forecasting. - Kashif Rasul; Arjun Ashok; Andrew Robert Williams; Arian Khorasani; George Adamopoulos; Rishika Bhagwatkar; Marin Bilos; Hena Ghonia; Nadhir Vincent Hassen; Anderson Schneider; Sahil Garg; Alexandre Drouin; et al. (CoRR, 2023)

Nearly Tight Bounds For Differentially Private Min s-t and Multiway Cut. - Mina Dalirrooyfard; Slobodan Mitrovic; Yuriy Nevmyvaka (CoRR, 2023)

Graph Partitioning with a Move Budget. - Mina Dalirrooyfard; Elaheh Fata; Majid Behbahani; Yuriy Nevmyvaka (AISTATS, 2024)

VQ-TR: Vector Quantized Attention for Time Series Forecasting. - Kashif Rasul; Andrew Bennett; Pablo Vicente; Umang Gupta; Hena Ghonia; Anderson Schneider; Yuriy Nevmyvaka (ICLR, 2024)

Variational Schrödinger Diffusion Models. - Wei Deng 002; Weijian Luo; Yixin Tan; Marin Bilos; Yu Chen; Yuriy Nevmyvaka; Ricky T. Q. Chen (ICML, 2024)

S2IP-LLM: Semantic Space Informed Prompt Learning with LLM for Time Series Forecasting. - Zijie Pan; Yushan Jiang; Sahil Garg; Anderson Schneider; Yuriy Nevmyvaka; Dongjin Song (ICML, 2024)

Empowering Time Series Analysis with Large Language Models: A Survey. - Yushan Jiang; Zijie Pan; Xikun Zhang 002; Sahil Garg; Anderson Schneider; Yuriy Nevmyvaka; Dongjin Song (IJCAI, 2024)

The 10th Mining and Learning from Time Series Workshop: From Classical Methods to LLMs. - Sanjay Purushotham; Dongjin Song; Qingsong Wen; Jun Huan; Cong Shen 001; Stefan Zohren; Yuriy Nevmyvaka (KDD, 2024)

Structural Knowledge Informed Continual Multivariate Time Series Forecasting. - Zijie Pan; Yushan Jiang; Dongjin Song; Sahil Garg; Kashif Rasul; Anderson Schneider; Yuriy Nevmyvaka (CoRR, 2024)

Graph Partitioning With Limited Moves. - Majid Behbahani; Mina Dalirrooyfard; Elaheh Fata; Yuriy Nevmyvaka (CoRR, 2024)

S2IP-LLM: Semantic Space Informed Prompt Learning with LLM for Time Series Forecasting. - Zijie Pan; Yushan Jiang; Sahil Garg; Anderson Schneider; Yuriy Nevmyvaka; Dongjin Song (CoRR, 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)

Differentially Private Gomory-Hu Trees. - Anders Aamand; Justin Y. Chen; Mina Dalirrooyfard; Slobodan Mitrovic; Yuriy Nevmyvaka; Sandeep Silwal; Yinzhan Xu (CoRR, 2024)

Prediction-Enhanced Monte Carlo: A Machine Learning View on Control Variate. - Fengpei Li; Haoxian Chen 002; Jiahe Lin; Arkin Gupta; Xiaowei Tan; Gang Xu; Yuriy Nevmyvaka; Agostino Capponi; Henry Lam (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)

Scaling Laws and Efficient Inference for Ternary Language Models. - Tejas Vaidhya; Ayush Kaushal; Vineet Jain; Francis Couture Harpin; Prashant Shishodia; Majid Behbahani; Yuriy Nevmyvaka; Irina Rish (ACL (1), 2025)

Variational Schrödinger Momentum Diffusion. - Kevin Rojas; Yixin Tan; Molei Tao; Yuriy Nevmyvaka; Wei Deng 002 (AISTATS, 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)

Privacy Amplification by Structured Subsampling for Deep Differentially Private Time Series Forecasting. - Jan Schuchardt; Mina Dalirrooyfard; Jed Guzelkabaagac; Anderson Schneider; Yuriy Nevmyvaka; Stephan Günnemann (CoRR, 2025)

TS-RAG: Retrieval-Augmented Generation based Time Series Foundation Models are Stronger Zero-Shot Forecaster. - Kanghui Ning; Zijie Pan; Yu Liu; Yushan Jiang; James Y. Zhang; Kashif Rasul; Anderson Schneider; Lintao Ma; Yuriy Nevmyvaka; Dongjin Song (CoRR, 2025)

Multi-modal Time Series Analysis: A Tutorial and Survey. - Yushan Jiang; Kanghui Ning; Zijie Pan; Xuyang Shen; Jingchao Ni; Wenchao Yu; Anderson Schneider; Haifeng Chen; Yuriy Nevmyvaka; Dongjin Song (CoRR, 2025)

Reinforcing Multi-Turn Reasoning in LLM Agents via Turn-Level Credit Assignment. - Siliang Zeng; Quan Wei 001; William Brown; Oana Frunza; Yuriy Nevmyvaka; Mingyi Hong 001 (CoRR, 2025)

Random Initialization Can't Catch Up: The Advantage of Language Model Transfer for Time Series Forecasting. - Roland Riachi; Kashif Rasul; Arjun Ashok; Prateek Humane; Alexis Roger; Andrew Robert Williams; Yuriy Nevmyvaka; Irina Rish (CoRR, 2025)

Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models. - Tejas Vaidhya; Ayush Kaushal; Vineet Jain; Francis Couture Harpin; Prashant Shishodia; Majid Behbahani; Yuriy Nevmyvaka; Irina Rish (CoRR, 2025)

Breaking the n1.5 Additive Error Barrier for Private and Efficient Graph Sparsification via Private Expander Decomposition. - Anders Aamand; Justin Y. Chen; Mina Dalirrooyfard; Slobodan Mitrovic; Yuriy Nevmyvaka; Sandeep Silwal; Yinzhan Xu (CoRR, 2025)

Technical Report: Full-Stack Fine-Tuning for the Q Programming Language. - Brendan R. Hogan; Will Brown; Adel Boyarsky; Anderson Schneider; Yuriy Nevmyvaka (CoRR, 2025)