Yuriy Nevmyvaka

According to our database, Yuriy Nevmyvaka authored at least 58 papers between 2001 and 2026.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2026

A Mechanistic Study of Tabular Foundation Models.

Marin Bilos, James T. Wilson, Anderson Schneider, Yuriy Nevmyvaka

CoRR, May, 2026

Cubit: Token Mixer with Kernel Ridge Regression.

Chuanyang Zheng, Jiankai Sun, Yihang Gao, Yuehao Wang, Liangchen Tan, Mac Schwager, Anderson Schneider, Yuriy Nevmyvaka, Xiaodong Liu

CoRR, May, 2026

AlphaLab: Autonomous Multi-Agent Research Across Optimization Domains with Frontier LLMs.

Brendan R. Hogan, Xiwen Chen, James T. Wilson, Kashif Rasul, Adel Boyarsky, Thomas Kamei, Anderson Schneider, Yuriy Nevmyvaka

CoRR, April, 2026

Hiding in Plain Text: Detecting Concealed Jailbreaks via Activation Disentanglement.

Amirhossein Farzam, Majid Behabahani, Mani Malek, Yuriy Nevmyvaka, Guillermo Sapiro

CoRR, February, 2026

GeoNorm: Unify Pre-Norm and Post-Norm with Geodesic Optimization.

Chuanyang Zheng, Jiankai Sun, Yihang Gao, Chi Wang, Yuehao Wang, Jing Xiong, Liliang Ren, Bo Peng, Qingmei Wang, Xiaoran Shang, Mac Schwager, Anderson Schneider, Yuriy Nevmyvaka, Xiaodong Liu

CoRR, January, 2026

2025

AHA: Aligning Large Audio-Language Models for Reasoning Hallucinations via Counterfactual Hard Negatives.

Yanxi Chen, Wenhui Zhu, Xiwen Chen, Zhipeng Wang, Xin Li, Peijie Qiu, Hao Wang, Xuanzhao Dong, Yujian Xiong, Anderson Schneider, Yuriy Nevmyvaka, Yalin Wang

CoRR, December, 2025

Small Vocabularies, Big Gains: Pretraining and Tokenization in Time Series Models.

Alexis Roger, Gwen Legate, Kashif Rasul, Yuriy Nevmyvaka, Irina Rish

CoRR, November, 2025

Speculative Sampling for Parametric Temporal Point Processes.

Marin Bilos, Anderson Schneider, Yuriy Nevmyvaka

CoRR, October, 2025

Chart-RVR: Reinforcement Learning with Verifiable Rewards for Explainable Chart Reasoning.

Sanchit Sinha, Oana Frunza, Kashif Rasul, Yuriy Nevmyvaka, Aidong Zhang

CoRR, October, 2025

Improving Reasoning for Diffusion Language Models via Group Diffusion Policy Optimization.

Kevin Rojas, Jiahe Lin, Kashif Rasul, Anderson Schneider, Yuriy Nevmyvaka, Molei Tao, Wei Deng

CoRR, October, 2025

Understanding the Mixture-of-Experts with Nadaraya-Watson Kernel.

Chuanyang Zheng, Jiankai Sun, Yihang Gao, Enze Xie, Yuehao Wang, Peihao Wang, Ting Xu, Matthew Chang, Liliang Ren, Jingyao Li, Jing Xiong, Kashif Rasul, Mac Schwager, Anderson Schneider, Zhangyang Wang, Yuriy Nevmyvaka

CoRR, September, 2025

Technical Report: Full-Stack Fine-Tuning for the Q Programming Language.

Brendan R. Hogan, Will Brown, Adel Boyarsky, Anderson Schneider, Yuriy Nevmyvaka

CoRR, August, 2025

Breaking the n^1.5 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, July, 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, June, 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, June, 2025

Reinforcing Multi-Turn Reasoning in LLM Agents via Turn-Level Credit Assignment.

Siliang Zeng, Quan Wei, William Brown, Oana Frunza, Yuriy Nevmyvaka, Mingyi Hong

CoRR, May, 2025

Reweighting Improves Conditional Risk Bounds.

Yikai Zhang, Jiahe Lin, Fengpei Li, Songzhu Zheng, Anant Raj, Anderson Schneider, Yuriy Nevmyvaka

Trans. Mach. Learn. Res., 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

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025

Differentially Private Gomory-Hu Trees.

Anders Aamand, Justin Y. Chen, Mina Dalirrooyfard, Slobodan Mitrovic, Yuriy Nevmyvaka, Sandeep Silwal, Yinzhan Xu

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025

The 11th Mining and Learning from Time Series (MILETS): From Classical Methods to LLMs.

Sanjay Purushotham, Dongjin Song, Qingsong Wen, Jun Huan, Yuxuan Liang, Cong Shen, Stefan Zohren, Yuriy Nevmyvaka

Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, V.2, 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

Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, V.2, 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

Proceedings of the Forty-second International Conference on Machine Learning, 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

Proceedings of the Forty-second International Conference on Machine Learning, 2025

Towards Interpretable and Trustworthy Time Series Reasoning: A BlueSky Vision.

Kanghui Ning, Zijie Pan, Yushan Jiang, Anderson Schneider, Yuriy Nevmyvaka, Dongjin Song

Proceedings of the IEEE International Conference on Data Mining, 2025

Variational Schrödinger Momentum Diffusion.

Kevin Rojas, Yixin Tan, Molei Tao, Yuriy Nevmyvaka, Wei Deng

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2025

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

Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2025

2024

Learning to Abstain From Uninformative Data.

Yikai Zhang, Songzhu Zheng, Mina Dalirrooyfard, Pengxiang Wu, Anderson Schneider, Anant Raj, Yuriy Nevmyvaka, Chao Chen

Trans. Mach. Learn. Res., 2024

Prediction-Enhanced Monte Carlo: A Machine Learning View on Control Variate.

Fengpei Li, Haoxian Chen, Jiahe Lin, Arkin Gupta, Xiaowei Tan, Gang Xu, Yuriy Nevmyvaka, Agostino Capponi, Henry Lam

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

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

Graph Partitioning With Limited Moves.

Majid Behbahani, Mina Dalirrooyfard, Elaheh Fata, Yuriy Nevmyvaka

CoRR, 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

The 10th Mining and Learning from Time Series Workshop: From Classical Methods to LLMs.

Sanjay Purushotham, Dongjin Song, Qingsong Wen, Jun Huan, Cong Shen, Stefan Zohren, Yuriy Nevmyvaka

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

Empowering Time Series Analysis with Large Language Models: A Survey.

Yushan Jiang, Zijie Pan, Xikun Zhang, Sahil Garg, Anderson Schneider, Yuriy Nevmyvaka, Dongjin Song

Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 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

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Variational Schrödinger Diffusion Models.

Wei Deng, Weijian Luo, Yixin Tan, Marin Bilos, Yu Chen, Yuriy Nevmyvaka, Ricky T. Q. Chen

Proceedings of the Forty-first International Conference on Machine Learning, 2024

VQ-TR: Vector Quantized Attention for Time Series Forecasting.

Kashif Rasul, Andrew Bennett, Pablo Vicente, Umang Gupta, Hena Ghonia, Anderson Schneider, Yuriy Nevmyvaka

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Graph Partitioning with a Move Budget.

Mina Dalirrooyfard, Elaheh Fata, Majid Behbahani, Yuriy Nevmyvaka

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Nearly Tight Bounds For Differentially Private Min s-t and Multiway Cut.

Mina Dalirrooyfard, Slobodan Mitrovic, Yuriy Nevmyvaka

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, Nicolas Chapados, Yuriy Nevmyvaka, Irina Rish

CoRR, 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

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

Proceedings of the Uncertainty in Artificial Intelligence, 2023

In- or out-of-distribution detection via dual divergence estimation.

Sahil Garg, Sanghamitra Dutta, Mina Dalirrooyfard, Anderson Schneider, Yuriy Nevmyvaka

Proceedings of the Uncertainty in Artificial Intelligence, 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

Proceedings of the Uncertainty in Artificial Intelligence, 2023

Nearly Tight Bounds For Differentially Private Multiway Cut.

Mina Dalirrooyfard, Slobodan Mitrovic, Yuriy Nevmyvaka

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

The 9th SIGKDD International Workshop on Mining and Learning from Time Series.

Sanjay Purushotham, Dongjin Song, Qingsong Wen, Jun Huan, Cong Shen, Yuriy Nevmyvaka

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

Provably Convergent Schrödinger Bridge with Applications to Probabilistic Time Series Imputation.

Yu Chen, Wei Deng, Shikai Fang, Fengpei Li, Nicole Tianjiao Yang, Yikai Zhang, Kashif Rasul, Shandian Zhe, Anderson Schneider, Yuriy Nevmyvaka

Proceedings of the International Conference on Machine Learning, 2023

Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusion.

Marin Bilos, Kashif Rasul, Anderson Schneider, Yuriy Nevmyvaka, Stephan Günnemann

Proceedings of the International Conference on Machine Learning, 2023

Risk Bounds on Aleatoric Uncertainty Recovery.

Yikai Zhang, Jiahe Lin, Fengpei Li, Yeshaya Adler, Kashif Rasul, Anderson Schneider, Yuriy Nevmyvaka

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Modeling Temporal Data as Continuous Functions with Process Diffusion.

Marin Bilos, Kashif Rasul, Anderson Schneider, Yuriy Nevmyvaka, Stephan Günnemann

CoRR, 2022

Estimating transfer entropy under long ranged dependencies.

Sahil Garg, Umang Gupta, Yu Chen, Syamantak Datta Gupta, Yeshaya Adler, Anderson Schneider, Yuriy Nevmyvaka

Proceedings of the Uncertainty in Artificial Intelligence, 2022

8th SIGKDD International Workshop on Mining and Learning from Time Series - Deep Forecasting: Models, Interpretability, and Applications.

Sanjay Purushotham, Jun Huan, Cong Shen, Dongjin Song, Yuyang Wang, Jan Gasthaus, Hilaf Hasson, Youngsuk Park, Sungyong Seo, Yuriy Nevmyvaka

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

2021

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, Alexander Pfefferle, Sushil Ammanaghatta Shivakumar, Arjun Krishnakumar, Yubo Wang, Rhea Sanjay Sukthanker, Frank Hutter, Euxhen Hasanaj, Tien-Dung Le, Mikhail Khodak, Yuriy Nevmyvaka, Kashif Rasul, Frederic Sala, Anderson Schneider, Junhong Shen, Evan Randall Sparks

Proceedings of the NeurIPS 2022 Competition Track, 2021

2014

Pursuit-Evasion Without Regret, with an Application to Trading.

Lili Dworkin, Michael J. Kearns, Yuriy Nevmyvaka

Proceedings of the 31th International Conference on Machine Learning, 2014

2009

Censored Exploration and the Dark Pool Problem.

Kuzman Ganchev, Michael J. Kearns, Yuriy Nevmyvaka, Jennifer Wortman Vaughan

Proceedings of the UAI 2009, 2009

2006

Reinforcement learning for optimized trade execution.

Yuriy Nevmyvaka, Yi Feng, Michael J. Kearns

Proceedings of the Machine Learning, 2006

2005

Electronic Trading in Order-Driven Markets: Efficient Execution.

Yuriy Nevmyvaka, Michael J. Kearns, Amy Papandreou, Katia P. Sycara

Proceedings of the 7th IEEE International Conference on E-Commerce Technology (CEC 2005), 2005

2001

A Practical Markov Chain Monte Carlo Approach to Decision Problems.

Timothy Huang, Yuriy Nevmyvaka

Proceedings of the Fourteenth International Florida Artificial Intelligence Research Society Conference, 2001