Volodymyr Kuleshov

97 papers A* 27A 6B 2Journal 55Unranked 6

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	DUEL: Exact Likelihood for Masked Diffusion via Deterministic Unmasking. CoRR Gilad Turok, Chris De Sa, Volodymyr Kuleshov
2026	J	`jnl`	Learn from Your Mistakes: Self-Correcting Masked Diffusion Models. CoRR Yair Schiff, Omer Belhasin, Roy Uziel, Guanghan Wang, Marianne Arriola, Gilad Turok, Michael Elad, Volodymyr Kuleshov
2025	A*	`conf`	Block Diffusion: Interpolating Between Autoregressive and Diffusion Language Models. ICLR Marianne Arriola, Aaron Gokaslan, Justin T. Chiu, Zhihan Yang, Zhixuan Qi, Jiaqi Han, Subham Sekhar Sahoo, Volodymyr Kuleshov
2025	J	`jnl`	Block Diffusion: Interpolating Between Autoregressive and Diffusion Language Models. CoRR Marianne Arriola, Aaron Gokaslan, Justin T. Chiu, Zhihan Yang, Zhixuan Qi, Jiaqi Han, Subham Sekhar Sahoo, Volodymyr Kuleshov
2025	J	`jnl`	Calibrated Probabilistic Forecasts for Arbitrary Sequences. Trans. Mach. Learn. Res. Charles Marx, Volodymyr Kuleshov, Stefano Ermon
2025	A	`conf`	Calibrated Regression Against An Adversary Without Regret. UAI Shachi Deshpande, Charles Marx, Volodymyr Kuleshov
2025	A*	`conf`	Denoising Diffusion Variational Inference: Diffusion Models as Expressive Variational Posteriors. AAAI Wasu Top Piriyakulkij, Yingheng Wang, Volodymyr Kuleshov
2025	J	`jnl`	Encoder-Decoder Diffusion Language Models for Efficient Training and Inference. CoRR Marianne Arriola, Yair Schiff, Hao Phung, Aaron Gokaslan, Volodymyr Kuleshov
2025	J	`jnl`	Mercury: Ultra-Fast Language Models Based on Diffusion. CoRR Samar Khanna, Siddhant Kharbanda, Shufan Li, Harshit Varma, Eric Wang, Sawyer Birnbaum, Ziyang Luo, Yanis Miraoui, Akash Palrecha, Stefano Ermon, Aditya Grover, Volodymyr Kuleshov
2025	J	`jnl`	Probabilistic Graphical Models: A Concise Tutorial. CoRR Jacqueline R. M. A. Maasch, Willie Neiswanger, Stefano Ermon, Volodymyr Kuleshov
2025	J	`jnl`	RanDeS: Randomized Delta Superposition for Multi-Model Compression. CoRR Hangyu Zhou, Aaron Gokaslan, Volodymyr Kuleshov, Bharath Hariharan
2025	J	`jnl`	Remasking Discrete Diffusion Models with Inference-Time Scaling. CoRR Guanghan Wang, Yair Schiff, Subham Sekhar Sahoo, Volodymyr Kuleshov
2025	A*	`conf`	Simple Guidance Mechanisms for Discrete Diffusion Models. ICLR Yair Schiff, Subham Sekhar Sahoo, Hao Phung, Guanghan Wang, Sam Boshar, Hugo Dalla-torre, Bernardo P. de Almeida, Alexander M. Rush, Thomas Pierrot, Volodymyr Kuleshov
2025	A*	`conf`	The Diffusion Duality. ICML Subham Sekhar Sahoo, Justin Deschenaux, Aaron Gokaslan, Guanghan Wang, Justin T. Chiu, Volodymyr Kuleshov
2025	J	`jnl`	The Diffusion Duality. CoRR Subham Sekhar Sahoo, Justin Deschenaux, Aaron Gokaslan, Guanghan Wang, Justin T. Chiu, Volodymyr Kuleshov
2025	J	`jnl`	The GAN is dead; long live the GAN! A Modern GAN Baseline. CoRR Yiwen Huang, Aaron Gokaslan, Volodymyr Kuleshov, James Tompkin
2025	J	`jnl`	d2: Improved Techniques for Training Reasoning Diffusion Language Models. CoRR Guanghan Wang, Yair Schiff, Gilad Turok, Volodymyr Kuleshov
2024	A*	`conf`	Caduceus: Bi-Directional Equivariant Long-Range DNA Sequence Modeling. ICML Yair Schiff, Chia-Hsiang Kao, Aaron Gokaslan, Tri Dao, Albert Gu, Volodymyr Kuleshov
2024	J	`jnl`	Caduceus: Bi-Directional Equivariant Long-Range DNA Sequence Modeling. CoRR Yair Schiff, Chia-Hsiang Kao, Aaron Gokaslan, Tri Dao, Albert Gu, Volodymyr Kuleshov
2024	J	`jnl`	Calibrated Probabilistic Forecasts for Arbitrary Sequences. CoRR Charles Marx, Volodymyr Kuleshov, Stefano Ermon
2024	A	`conf`	Calibrated and Conformal Propensity Scores for Causal Effect Estimation. UAI Shachi Deshpande, Volodymyr Kuleshov
2024	A*	`conf`	Common Canvas: Open Diffusion Models Trained on Creative-Commons Images. CVPR Aaron Gokaslan, A. Feder Cooper, Jasmine Collins, Landan Seguin, Austin Jacobson, Mihir Patel, Jonathan Frankle, Cory Stephenson, Volodymyr Kuleshov
2024	A*	`conf`	Diffusion Models With Learned Adaptive Noise. NeurIPS Subham S. Sahoo, Aaron Gokaslan, Christopher De Sa, Volodymyr Kuleshov
2024	J	`jnl`	Diffusion Variational Inference: Diffusion Models as Expressive Variational Posteriors. CoRR Top Piriyakulkij, Yingheng Wang, Volodymyr Kuleshov
2024	A*	`conf`	DySLIM: Dynamics Stable Learning by Invariant Measure for Chaotic Systems. ICML Yair Schiff, Zhong Yi Wan, Jeffrey B. Parker, Stephan Hoyer, Volodymyr Kuleshov, Fei Sha, Leonardo Zepeda-Núñez
2024	J	`jnl`	DySLIM: Dynamics Stable Learning by Invariant Measure for Chaotic Systems. CoRR Yair Schiff, Zhong Yi Wan, Jeffrey B. Parker, Stephan Hoyer, Volodymyr Kuleshov, Fei Sha, Leonardo Zepeda-Núñez
2024	A	`conf`	Local Discovery by Partitioning: Polynomial-Time Causal Discovery Around Exposure-Outcome Pairs. UAI Jacqueline R. M. A. Maasch, Weishen Pan, Shantanu Gupta, Volodymyr Kuleshov, Kyra Gan, Fei Wang
2024	J	`jnl`	ModuLoRA: Finetuning 2-Bit LLMs on Consumer GPUs by Integrating with Modular Quantizers. Trans. Mach. Learn. Res. Junjie Yin, Jiahao Dong, Yingheng Wang, Christopher De Sa, Volodymyr Kuleshov
2024	A	`conf`	Online Calibrated and Conformal Prediction Improves Bayesian Optimization. AISTATS Shachi Deshpande, Charles Marx, Volodymyr Kuleshov
2024	A*	`conf`	QuIP#: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks. ICML Albert Tseng, Jerry Chee, Qingyao Sun, Volodymyr Kuleshov, Christopher De Sa
2024	J	`jnl`	QuIP#: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks. CoRR Albert Tseng, Jerry Chee, Qingyao Sun, Volodymyr Kuleshov, Christopher De Sa
2024	J	`jnl`	Simple Guidance Mechanisms for Discrete Diffusion Models. CoRR Yair Schiff, Subham Sekhar Sahoo, Hao Phung, Guanghan Wang, Sam Boshar, Hugo Dalla-torre, Bernardo P. de Almeida, Alexander Rush, Thomas Pierrot, Volodymyr Kuleshov
2024	A*	`conf`	Simple and Effective Masked Diffusion Language Models. NeurIPS Subham S. Sahoo, Marianne Arriola, Yair Schiff, Aaron Gokaslan, Edgar Marroquin, Justin T. Chiu, Alexander Rush, Volodymyr Kuleshov
2024	J	`jnl`	Simple and Effective Masked Diffusion Language Models. CoRR Subham Sekhar Sahoo, Marianne Arriola, Yair Schiff, Aaron Gokaslan, Edgar Marroquin, Justin T. Chiu, Alexander M. Rush, Volodymyr Kuleshov
2024	A*	`conf`	The GAN is dead; long live the GAN! A Modern GAN Baseline. NeurIPS Nick Huang, Aaron Gokaslan, Volodymyr Kuleshov, James Tompkin
2023	J	`jnl`	Active Preference Inference using Language Models and Probabilistic Reasoning. CoRR Top Piriyakulkij, Volodymyr Kuleshov, Kevin Ellis
2023	A*	`conf`	Backpropagation through Combinatorial Algorithms: Identity with Projection Works. ICLR Subham Sekhar Sahoo, Anselm Paulus, Marin Vlastelica, Vít Musil, Volodymyr Kuleshov, Georg Martius
2023	J	`jnl`	Calibrated Propensity Scores for Causal Effect Estimation. CoRR Shachi Deshpande, Volodymyr Kuleshov
2023	J	`jnl`	CommonCanvas: An Open Diffusion Model Trained with Creative-Commons Images. CoRR Aaron Gokaslan, A. Feder Cooper, Jasmine Collins, Landan Seguin, Austin Jacobson, Mihir Patel, Jonathan Frankle, Cory Stephenson, Volodymyr Kuleshov
2023	J	`jnl`	Diffusion Models With Learned Adaptive Noise. CoRR Subham Sekhar Sahoo, Aaron Gokaslan, Chris De Sa, Volodymyr Kuleshov
2023	A*	`conf`	Harnessing Biomedical Literature to Calibrate Clinicians' Trust in AI Decision Support Systems. CHI Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, Fei Wang
2023	A*	`conf`	InfoDiffusion: Representation Learning Using Information Maximizing Diffusion Models. ICML Yingheng Wang, Yair Schiff, Aaron Gokaslan, Weishen Pan, Fei Wang, Christopher De Sa, Volodymyr Kuleshov
2023	J	`jnl`	InfoDiffusion: Representation Learning Using Information Maximizing Diffusion Models. CoRR Yingheng Wang, Yair Schiff, Aaron Gokaslan, Weishen Pan, Fei Wang, Christopher De Sa, Volodymyr Kuleshov
2023	J	`jnl`	Local Discovery by Partitioning: Polynomial-Time Causal Discovery Around Exposure-Outcome Pairs. CoRR Jacqueline R. M. A. Maasch, Weishen Pan, Shantanu Gupta, Volodymyr Kuleshov, Kyra Gan, Fei Wang
2023	J	`jnl`	ModuLoRA: Finetuning 3-Bit LLMs on Consumer GPUs by Integrating with Modular Quantizers. CoRR Junjie Yin, Jiahao Dong, Yingheng Wang, Christopher De Sa, Volodymyr Kuleshov
2023	J	`jnl`	Online Calibrated Regression for Adversarially Robust Forecasting. CoRR Volodymyr Kuleshov, Shachi Deshpande
2023	A*	`conf`	QuIP: 2-Bit Quantization of Large Language Models With Guarantees. NeurIPS Jerry Chee, Yaohui Cai, Volodymyr Kuleshov, Christopher De Sa
2023	J	`jnl`	QuIP: 2-Bit Quantization of Large Language Models With Guarantees. CoRR Jerry Chee, Yaohui Cai, Volodymyr Kuleshov, Christopher De Sa
2023	A*	`conf`	Semi-Autoregressive Energy Flows: Exploring Likelihood-Free Training of Normalizing Flows. ICML Phillip Si, Zeyi Chen, Subham Sekhar Sahoo, Yair Schiff, Volodymyr Kuleshov
2023	A*	`conf`	Semi-Parametric Inducing Point Networks and Neural Processes. ICLR Richa Rastogi, Yair Schiff, Alon Hacohen, Zhaozhi Li, Ian Lee, Yuntian Deng, Mert R. Sabuncu, Volodymyr Kuleshov
2023	A*	`conf`	Text Embeddings Reveal (Almost) As Much As Text. EMNLP John X. Morris, Volodymyr Kuleshov, Vitaly Shmatikov, Alexander M. Rush
2023	J	`jnl`	Text Embeddings Reveal (Almost) As Much As Text. CoRR John X. Morris, Volodymyr Kuleshov, Vitaly Shmatikov, Alexander M. Rush
2022	A*	`conf`	Autoregressive Quantile Flows for Predictive Uncertainty Estimation. ICLR Phillip Si, Allan Bishop, Volodymyr Kuleshov
2022	A*	`conf`	Calibrated and Sharp Uncertainties in Deep Learning via Density Estimation. ICML Volodymyr Kuleshov, Shachi Deshpande
2022	A*	`conf`	Deep Multi-Modal Structural Equations For Causal Effect Estimation With Unstructured Proxies. NeurIPS Shachi Deshpande, Kaiwen Wang, Dhruv Sreenivas, Zheng Li, Volodymyr Kuleshov
2022	J	`jnl`	Energy Flows: Towards Determinant-Free Training of Normalizing Flows. CoRR Phillip Si, Volodymyr Kuleshov
2022	J	`jnl`	Gradient Backpropagation Through Combinatorial Algorithms: Identity with Projection Works. CoRR Subham Sekhar Sahoo, Marin Vlastelica, Anselm Paulus, Vít Musil, Volodymyr Kuleshov, Georg Martius
2022	A*	`conf`	Model Criticism for Long-Form Text Generation. EMNLP Yuntian Deng, Volodymyr Kuleshov, Alexander M. Rush
2022	J	`jnl`	Model Criticism for Long-Form Text Generation. CoRR Yuntian Deng, Volodymyr Kuleshov, Alexander M. Rush
2022	J	`jnl`	Multi-Modal Causal Inference with Deep Structural Equation Models. CoRR Shachi Deshpande, Zheng Li, Volodymyr Kuleshov
2022	J	`jnl`	Regularized Data Programming with Bayesian Priors. CoRR Jacqueline R. M. A. Maasch, Hao Zhang, Qian Yang, Fei Wang, Volodymyr Kuleshov
2022	J	`jnl`	Semi-Parametric Deep Neural Networks in Linear Time and Memory. CoRR Richa Rastogi, Yuntian Deng, Ian Lee, Mert R. Sabuncu, Volodymyr Kuleshov
2021	J	`jnl`	Autoregressive Quantile Flows for Predictive Uncertainty Estimation. CoRR Phillip Si, Allan Bishop, Volodymyr Kuleshov
2021	J	`jnl`	Calibrated and Sharp Uncertainties in Deep Learning via Simple Density Estimation. CoRR Volodymyr Kuleshov, Shachi Deshpande
2021	J	`jnl`	Calibration Improves Bayesian Optimization. CoRR Shachi Deshpande, Volodymyr Kuleshov
2021	J	`jnl`	Clinical Evidence Engine: Proof-of-Concept For A Clinical-Domain-Agnostic Decision Support Infrastructure. CoRR Bojian Hou, Hao Zhang, Gur Ladizhinsky, Stephen Yang, Volodymyr Kuleshov, Fei Wang, Qian Yang
2021	J	`jnl`	Quantifying and Understanding Adversarial Examples in Discrete Input Spaces. CoRR Volodymyr Kuleshov, Evgenii Nikishin, Shantanu Thakoor, Tingfung Lau, Stefano Ermon
2019	A*	`conf`	Calibrated Model-Based Deep Reinforcement Learning. ICML Ali Malik, Volodymyr Kuleshov, Jiaming Song, Danny Nemer, Harlan Seymour, Stefano Ermon
2019	J	`jnl`	Calibrated Model-Based Deep Reinforcement Learning. CoRR Ali Malik, Volodymyr Kuleshov, Jiaming Song, Danny Nemer, Harlan Seymour, Stefano Ermon
2019	A*	`conf`	Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations. NeurIPS Sawyer Birnbaum, Volodymyr Kuleshov, S. Zayd Enam, Pang Wei Koh, Stefano Ermon
2019	J	`jnl`	Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations. CoRR Sawyer Birnbaum, Volodymyr Kuleshov, S. Zayd Enam, Pang Wei Koh, Stefano Ermon
2018	A*	`conf`	Accurate Uncertainties for Deep Learning Using Calibrated Regression. ICML Volodymyr Kuleshov, Nathan Fenner, Stefano Ermon
2018	J	`jnl`	Accurate Uncertainties for Deep Learning Using Calibrated Regression. CoRR Volodymyr Kuleshov, Nathan Fenner, Stefano Ermon
2018	A*	`conf`	Adversarial Constraint Learning for Structured Prediction. IJCAI Hongyu Ren, Russell Stewart, Jiaming Song, Volodymyr Kuleshov, Stefano Ermon
2018	J	`jnl`	Adversarial Constraint Learning for Structured Prediction. CoRR Hongyu Ren, Russell Stewart, Jiaming Song, Volodymyr Kuleshov, Stefano Ermon
2018	J	`jnl`	Fast Metagenomic Binning via Hashing and Bayesian Clustering. J. Comput. Biol. Victoria Popic, Volodymyr Kuleshov, Michael P. Snyder, Serafim Batzoglou
2018	J	`jnl`	Learning with Weak Supervision from Physics and Data-Driven Constraints. AI Mag. Hongyu Ren, Russell Stewart, Jiaming Song, Volodymyr Kuleshov, Stefano Ermon
2017	J	`jnl`	Audio Super Resolution using Neural Networks. CoRR Volodymyr Kuleshov, S. Zayd Enam, Stefano Ermon
2017	—	`conf`	Audio Super-Resolution using Neural Networks. ICLR (Workshop) Volodymyr Kuleshov, S. Zayd Enam, Stefano Ermon
2017	A*	`conf`	Estimating Uncertainty Online Against an Adversary. AAAI Volodymyr Kuleshov, Stefano Ermon
2017	B	`conf`	GATTACA: Lightweight Metagenomic Binning Using Kmer Counting. RECOMB Victoria Popic, Volodymyr Kuleshov, Michael P. Snyder, Serafim Batzoglou
2017	A	`conf`	Hybrid Deep Discriminative/Generative Models for Semi-Supervised Learning. UAI Volodymyr Kuleshov, Stefano Ermon
2017	—	`—`	Intelligent systems for personalized genomic medicine. Volodymyr Kuleshov
2017	—	`conf`	Neural Variational Inference and Learning in Undirected Graphical Models. NIPS Volodymyr Kuleshov, Stefano Ermon
2017	J	`jnl`	Neural Variational Inference and Learning in Undirected Graphical Models. CoRR Volodymyr Kuleshov, Stefano Ermon
2016	J	`jnl`	Genome assembly from synthetic long read clouds. Bioinform. Volodymyr Kuleshov, Michael P. Snyder, Serafim Batzoglou
2016	J	`jnl`	Reliable Confidence Estimation via Online Learning. CoRR Volodymyr Kuleshov, Stefano Ermon
2015	—	`conf`	Calibrated Structured Prediction. NIPS Volodymyr Kuleshov, Percy Liang
2015	—	`conf`	Inverse Game Theory: Learning Utilities in Succinct Games. WINE Volodymyr Kuleshov, Okke Schrijvers
2015	J	`jnl`	Simultaneous diagonalization: the asymmetric, low-rank, and noisy settings. CoRR Volodymyr Kuleshov, Arun Tejasvi Chaganty, Percy Liang
2015	A	`conf`	Tensor Factorization via Matrix Factorization. AISTATS Volodymyr Kuleshov, Arun Tejasvi Chaganty, Percy Liang
2015	J	`jnl`	Tensor Factorization via Matrix Factorization. CoRR Volodymyr Kuleshov, Arun Tejasvi Chaganty, Percy Liang
2014	J	`jnl`	Algorithms for multi-armed bandit problems. CoRR Volodymyr Kuleshov, Doina Precup
2014	J	`jnl`	Probabilistic single-individual haplotyping. Bioinform. Volodymyr Kuleshov
2013	—	`conf`	Fast algorithms for sparse principal component analysis based on Rayleigh quotient iteration. ICML (3) Volodymyr Kuleshov
2012	—	`conf`	On the Efficiency of the Simplest Pricing Mechanisms in Two-Sided Markets. WINE Volodymyr Kuleshov, Gordon T. Wilfong
2010	B	`conf`	On the Efficiency of Markets with Two-Sided Proportional Allocation Mechanisms. SAGT Volodymyr Kuleshov, Adrian Vetta