On the Efficiency of Markets with Two-Sided Proportional Allocation Mechanisms. - Volodymyr Kuleshov; Adrian Vetta (SAGT, 2010)

On the Efficiency of the Simplest Pricing Mechanisms in Two-Sided Markets. - Volodymyr Kuleshov; Gordon T. Wilfong (WINE, 2012)

Fast algorithms for sparse principal component analysis based on Rayleigh quotient iteration. - Volodymyr Kuleshov (ICML (3), 2013)

Probabilistic single-individual haplotyping. - Volodymyr Kuleshov (Bioinform., 2014)

Algorithms for multi-armed bandit problems. - Volodymyr Kuleshov; Doina Precup (CoRR, 2014)

Tensor Factorization via Matrix Factorization. - Volodymyr Kuleshov; Arun Tejasvi Chaganty; Percy Liang (AISTATS, 2015)

Calibrated Structured Prediction. - Volodymyr Kuleshov; Percy Liang (NIPS, 2015)

Inverse Game Theory: Learning Utilities in Succinct Games. - Volodymyr Kuleshov; Okke Schrijvers (WINE, 2015)

Simultaneous diagonalization: the asymmetric, low-rank, and noisy settings. - Volodymyr Kuleshov; Arun Tejasvi Chaganty; Percy Liang (CoRR, 2015)

Genome assembly from synthetic long read clouds. - Volodymyr Kuleshov; Michael P. Snyder; Serafim Batzoglou (Bioinform., 2016)

Reliable Confidence Estimation via Online Learning. - Volodymyr Kuleshov; Stefano Ermon (CoRR, 2016)

Estimating Uncertainty Online Against an Adversary. - Volodymyr Kuleshov; Stefano Ermon (AAAI, 2017)

Audio Super-Resolution using Neural Networks. - Volodymyr Kuleshov; S. Zayd Enam; Stefano Ermon (ICLR (Workshop), 2017)

Neural Variational Inference and Learning in Undirected Graphical Models. - Volodymyr Kuleshov; Stefano Ermon (NIPS, 2017)

GATTACA: Lightweight Metagenomic Binning Using Kmer Counting. - Victoria Popic; Volodymyr Kuleshov; Michael P. Snyder; Serafim Batzoglou (RECOMB, 2017)

Hybrid Deep Discriminative/Generative Models for Semi-Supervised Learning. - Volodymyr Kuleshov; Stefano Ermon (UAI, 2017)

Intelligent systems for personalized genomic medicine. - Volodymyr Kuleshov (2017)

Accurate Uncertainties for Deep Learning Using Calibrated Regression. - Volodymyr Kuleshov; Nathan Fenner; Stefano Ermon (ICML, 2018)

Adversarial Constraint Learning for Structured Prediction. - Hongyu Ren; Russell Stewart; Jiaming Song; Volodymyr Kuleshov; Stefano Ermon (IJCAI, 2018)

Learning with Weak Supervision from Physics and Data-Driven Constraints. - Hongyu Ren; Russell Stewart; Jiaming Song; Volodymyr Kuleshov; Stefano Ermon (AI Mag., 2018)

Fast Metagenomic Binning via Hashing and Bayesian Clustering. - Victoria Popic; Volodymyr Kuleshov; Michael P. Snyder; Serafim Batzoglou (J. Comput. Biol., 2018)

Calibrated Model-Based Deep Reinforcement Learning. - Ali Malik; Volodymyr Kuleshov; Jiaming Song; Danny Nemer; Harlan Seymour; Stefano Ermon (ICML, 2019)

Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations. - Sawyer Birnbaum; Volodymyr Kuleshov; S. Zayd Enam; Pang Wei Koh; Stefano Ermon (NeurIPS, 2019)

Clinical Evidence Engine: Proof-of-Concept For A Clinical-Domain-Agnostic Decision Support Infrastructure. - Bojian Hou; Hao Zhang; Gur Ladizhinsky; Stephen Yang; Volodymyr Kuleshov; Fei Wang; Qian Yang (CoRR, 2021)

Calibration Improves Bayesian Optimization. - Shachi Deshpande; Volodymyr Kuleshov (CoRR, 2021)

Quantifying and Understanding Adversarial Examples in Discrete Input Spaces. - Volodymyr Kuleshov; Evgenii Nikishin; Shantanu Thakoor; Tingfung Lau; Stefano Ermon (CoRR, 2021)

Calibrated and Sharp Uncertainties in Deep Learning via Simple Density Estimation. - Volodymyr Kuleshov; Shachi Deshpande (CoRR, 2021)

Model Criticism for Long-Form Text Generation. - Yuntian Deng; Volodymyr Kuleshov; Alexander M. Rush (EMNLP, 2022)

Autoregressive Quantile Flows for Predictive Uncertainty Estimation. - Phillip Si; Allan Bishop; Volodymyr Kuleshov (ICLR, 2022)

Calibrated and Sharp Uncertainties in Deep Learning via Density Estimation. - Volodymyr Kuleshov; Shachi Deshpande (ICML, 2022)

Deep Multi-Modal Structural Equations For Causal Effect Estimation With Unstructured Proxies. - Shachi Deshpande; Kaiwen Wang; Dhruv Sreenivas; Zheng Li; Volodymyr Kuleshov (NeurIPS, 2022)

Multi-Modal Causal Inference with Deep Structural Equation Models. - Shachi Deshpande; Zheng Li; Volodymyr Kuleshov (CoRR, 2022)

Semi-Parametric Deep Neural Networks in Linear Time and Memory. - Richa Rastogi; Yuntian Deng; Ian Lee; Mert R. Sabuncu; Volodymyr Kuleshov (CoRR, 2022)

Gradient Backpropagation Through Combinatorial Algorithms: Identity with Projection Works. - Subham Sekhar Sahoo; Marin Vlastelica; Anselm Paulus; Vít Musil; Volodymyr Kuleshov; Georg Martius (CoRR, 2022)

Energy Flows: Towards Determinant-Free Training of Normalizing Flows. - Phillip Si; Volodymyr Kuleshov (CoRR, 2022)

Regularized Data Programming with Bayesian Priors. - Jacqueline R. M. A. Maasch; Hao Zhang; Qian Yang; Fei Wang; Volodymyr Kuleshov (CoRR, 2022)

Harnessing Biomedical Literature to Calibrate Clinicians' Trust in AI Decision Support Systems. - Qian Yang 004; Yuexing Hao; Kexin Quan; Stephen Yang; Yiran Zhao 002; Volodymyr Kuleshov; Fei Wang 001 (CHI, 2023)

Text Embeddings Reveal (Almost) As Much As Text. - John X. Morris; Volodymyr Kuleshov; Vitaly Shmatikov; Alexander M. Rush (EMNLP, 2023)

Semi-Parametric Inducing Point Networks and Neural Processes. - Richa Rastogi; Yair Schiff; Alon Hacohen; Zhaozhi Li; Ian Lee; Yuntian Deng; Mert R. Sabuncu; Volodymyr Kuleshov (ICLR, 2023)

Backpropagation through Combinatorial Algorithms: Identity with Projection Works. - Subham Sekhar Sahoo; Anselm Paulus; Marin Vlastelica; Vít Musil; Volodymyr Kuleshov; Georg Martius (ICLR, 2023)

Semi-Autoregressive Energy Flows: Exploring Likelihood-Free Training of Normalizing Flows. - Phillip Si; Zeyi Chen; Subham Sekhar Sahoo; Yair Schiff; Volodymyr Kuleshov (ICML, 2023)

InfoDiffusion: Representation Learning Using Information Maximizing Diffusion Models. - Yingheng Wang; Yair Schiff; Aaron Gokaslan; Weishen Pan; Fei Wang 001; Christopher De Sa; Volodymyr Kuleshov (ICML, 2023)

QuIP: 2-Bit Quantization of Large Language Models With Guarantees. - Jerry Chee; Yaohui Cai; Volodymyr Kuleshov; Christopher De Sa (NeurIPS, 2023)

Online Calibrated Regression for Adversarially Robust Forecasting. - Volodymyr Kuleshov; Shachi Deshpande (CoRR, 2023)

Calibrated Propensity Scores for Causal Effect Estimation. - Shachi Deshpande; Volodymyr Kuleshov (CoRR, 2023)

ModuLoRA: Finetuning 3-Bit LLMs on Consumer GPUs by Integrating with Modular Quantizers. - Junjie Yin; Jiahao Dong; Yingheng Wang; Christopher De Sa; Volodymyr Kuleshov (CoRR, 2023)

CommonCanvas: An Open Diffusion Model Trained with Creative-Commons Images. - Aaron Gokaslan; A. Feder Cooper; Jasmine Collins; Landan Seguin; Austin Jacobson; Mihir Patel; Jonathan Frankle; Cory Stephenson; Volodymyr Kuleshov (CoRR, 2023)

Active Preference Inference using Language Models and Probabilistic Reasoning. - Top Piriyakulkij; Volodymyr Kuleshov; Kevin Ellis (CoRR, 2023)

Online Calibrated and Conformal Prediction Improves Bayesian Optimization. - Shachi Deshpande; Charles Marx; Volodymyr Kuleshov (AISTATS, 2024)

Common Canvas: Open Diffusion Models Trained on Creative-Commons Images. - Aaron Gokaslan; A. Feder Cooper; Jasmine Collins; Landan Seguin; Austin Jacobson; Mihir Patel; Jonathan Frankle; Cory Stephenson; Volodymyr Kuleshov (CVPR, 2024)

Caduceus: Bi-Directional Equivariant Long-Range DNA Sequence Modeling. - Yair Schiff; Chia-Hsiang Kao; Aaron Gokaslan; Tri Dao; Albert Gu; Volodymyr Kuleshov (ICML, 2024)

DySLIM: Dynamics Stable Learning by Invariant Measure for Chaotic Systems. - Yair Schiff; Zhong Yi Wan; Jeffrey B. Parker; Stephan Hoyer; Volodymyr Kuleshov; Fei Sha; Leonardo Zepeda-Núñez (ICML, 2024)

QuIP#: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks. - Albert Tseng; Jerry Chee; Qingyao Sun; Volodymyr Kuleshov; Christopher De Sa (ICML, 2024)

The GAN is dead; long live the GAN! A Modern GAN Baseline. - Nick Huang; Aaron Gokaslan; Volodymyr Kuleshov; James Tompkin 001 (NeurIPS, 2024)

Simple and Effective Masked Diffusion Language Models. - Subham S. Sahoo; Marianne Arriola; Yair Schiff; Aaron Gokaslan; Edgar Marroquin; Justin T. Chiu; Alexander Rush; Volodymyr Kuleshov (NeurIPS, 2024)

Diffusion Models With Learned Adaptive Noise. - Subham S. Sahoo; Aaron Gokaslan; Christopher De Sa; Volodymyr Kuleshov (NeurIPS, 2024)

Calibrated and Conformal Propensity Scores for Causal Effect Estimation. - Shachi Deshpande; Volodymyr Kuleshov (UAI, 2024)

Local Discovery by Partitioning: Polynomial-Time Causal Discovery Around Exposure-Outcome Pairs. - Jacqueline R. M. A. Maasch; Weishen Pan; Shantanu Gupta; Volodymyr Kuleshov; Kyra Gan; Fei Wang 001 (UAI, 2024)

Diffusion Variational Inference: Diffusion Models as Expressive Variational Posteriors. - Top Piriyakulkij; Yingheng Wang; Volodymyr Kuleshov (CoRR, 2024)

Simple and Effective Masked Diffusion Language Models. - Subham Sekhar Sahoo; Marianne Arriola; Yair Schiff; Aaron Gokaslan; Edgar Marroquin; Justin T. Chiu; Alexander M. Rush; Volodymyr Kuleshov (CoRR, 2024)

Simple Guidance Mechanisms for Discrete Diffusion Models. - Yair Schiff; Subham Sekhar Sahoo; Hao Phung; Guanghan Wang; Sam Boshar; Hugo Dalla-torre; Bernardo P. de Almeida; Alexander Rush; Thomas Pierrot; Volodymyr Kuleshov (CoRR, 2024)

ModuLoRA: Finetuning 2-Bit LLMs on Consumer GPUs by Integrating with Modular Quantizers. - Junjie Yin; Jiahao Dong; Yingheng Wang; Christopher De Sa; Volodymyr Kuleshov (Trans. Mach. Learn. Res., 2024)

Denoising Diffusion Variational Inference: Diffusion Models as Expressive Variational Posteriors. - Wasu Top Piriyakulkij; Yingheng Wang; Volodymyr Kuleshov (AAAI, 2025)

Block Diffusion: Interpolating Between Autoregressive and Diffusion Language Models. - Marianne Arriola; Aaron Gokaslan; Justin T. Chiu; Zhihan Yang; Zhixuan Qi; Jiaqi Han; Subham Sekhar Sahoo; Volodymyr Kuleshov (ICLR, 2025)

Simple Guidance Mechanisms for Discrete Diffusion Models. - 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 (ICLR, 2025)

Calibrated Regression Against An Adversary Without Regret. - Shachi Deshpande; Charles Marx; Volodymyr Kuleshov (UAI, 2025)

Calibrated Probabilistic Forecasts for Arbitrary Sequences. - Charles Marx; Volodymyr Kuleshov; Stefano Ermon (Trans. Mach. Learn. Res., 2025)

The GAN is dead; long live the GAN! A Modern GAN Baseline. - Yiwen Huang; Aaron Gokaslan; Volodymyr Kuleshov; James Tompkin 001 (CoRR, 2025)

Remasking Discrete Diffusion Models with Inference-Time Scaling. - Guanghan Wang; Yair Schiff; Subham Sekhar Sahoo; Volodymyr Kuleshov (CoRR, 2025)

RanDeS: Randomized Delta Superposition for Multi-Model Compression. - Hangyu Zhou; Aaron Gokaslan; Volodymyr Kuleshov; Bharath Hariharan (CoRR, 2025)

The Diffusion Duality. - Subham Sekhar Sahoo; Justin Deschenaux; Aaron Gokaslan; Guanghan Wang; Justin T. Chiu; Volodymyr Kuleshov (CoRR, 2025)

Mercury: Ultra-Fast Language Models Based on Diffusion. - Samar Khanna; Siddhant Kharbanda; Shufan Li; Harshit Varma; Eric Wang; Sawyer Birnbaum; Ziyang Luo; Yanis Miraoui; Akash Palrecha; Stefano Ermon; Aditya Grover; Volodymyr Kuleshov (CoRR, 2025)

Probabilistic Graphical Models: A Concise Tutorial. - Jacqueline R. M. A. Maasch; Willie Neiswanger; Stefano Ermon; Volodymyr Kuleshov (CoRR, 2025)