Volodymyr Kuleshov
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
31
Venues
11
Active years
2010–2025
Best venue rank
A*
Where they publish
Papers
31 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2025 | AAAI | Denoising Diffusion Variational Inference: Diffusion Models as Expressive Variational Posteriors. | Wasu Top Piriyakulkij, Yingheng Wang, Volodymyr Kuleshov |
| 2025 | ICLR | 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 |
| 2025 | ICLR | 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 |
| 2025 | ICML | The Diffusion Duality. | Subham Sekhar Sahoo, Justin Deschenaux, Aaron Gokaslan, Guanghan Wang, Justin T. Chiu, Volodymyr Kuleshov |
| 2025 | UAI | Calibrated Regression Against An Adversary Without Regret. | Shachi Deshpande, Charles Marx, Volodymyr Kuleshov |
| 2024 | AISTATS | Online Calibrated and Conformal Prediction Improves Bayesian Optimization. | Shachi Deshpande, Charles Marx, Volodymyr Kuleshov |
| 2024 | CVPR | 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 |
| 2024 | ICML | Caduceus: Bi-Directional Equivariant Long-Range DNA Sequence Modeling. | Yair Schiff, Chia-Hsiang Kao, Aaron Gokaslan, Tri Dao, Albert Gu, Volodymyr Kuleshov |
| 2024 | ICML | 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-Nez |
| 2024 | ICML | QuIP#: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks. | Albert Tseng, Jerry Chee, Qingyao Sun, Volodymyr Kuleshov, Christopher De Sa |
| 2024 | UAI | Calibrated and Conformal Propensity Scores for Causal Effect Estimation. | Shachi Deshpande, Volodymyr Kuleshov |
| 2024 | UAI | 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 |
| 2023 | CHI | Harnessing Biomedical Literature to Calibrate Clinicians' Trust in AI Decision Support Systems. | Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, Fei Wang |
| 2023 | EMNLP | Text Embeddings Reveal (Almost) As Much As Text. | John X. Morris, Volodymyr Kuleshov, Vitaly Shmatikov, Alexander M. Rush |
| 2023 | ICLR | 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 |
| 2023 | ICLR | Backpropagation through Combinatorial Algorithms: Identity with Projection Works. | Subham Sekhar Sahoo, Anselm Paulus, Marin Vlastelica, Vt Musil, Volodymyr Kuleshov, Georg Martius |
| 2023 | ICML | Semi-Autoregressive Energy Flows: Exploring Likelihood-Free Training of Normalizing Flows. | Phillip Si, Zeyi Chen, Subham Sekhar Sahoo, Yair Schiff, Volodymyr Kuleshov |
| 2023 | ICML | InfoDiffusion: Representation Learning Using Information Maximizing Diffusion Models. | Yingheng Wang, Yair Schiff, Aaron Gokaslan, Weishen Pan, Fei Wang, Christopher De Sa, Volodymyr Kuleshov |
| 2022 | EMNLP | Model Criticism for Long-Form Text Generation. | Yuntian Deng, Volodymyr Kuleshov, Alexander M. Rush |
| 2022 | ICLR | Autoregressive Quantile Flows for Predictive Uncertainty Estimation. | Phillip Si, Allan Bishop, Volodymyr Kuleshov |
| 2022 | ICML | Calibrated and Sharp Uncertainties in Deep Learning via Density Estimation. | Volodymyr Kuleshov, Shachi Deshpande |
| 2019 | ICML | Calibrated Model-Based Deep Reinforcement Learning. | Ali Malik, Volodymyr Kuleshov, Jiaming Song, Danny Nemer, Harlan Seymour, Stefano Ermon |
| 2018 | ICML | Accurate Uncertainties for Deep Learning Using Calibrated Regression. | Volodymyr Kuleshov, Nathan Fenner, Stefano Ermon |
| 2018 | IJCAI | Adversarial Constraint Learning for Structured Prediction. | Hongyu Ren, Russell Stewart, Jiaming Song, Volodymyr Kuleshov, Stefano Ermon |
| 2017 | AAAI | Estimating Uncertainty Online Against an Adversary. | Volodymyr Kuleshov, Stefano Ermon |
| 2017 | ICLR | Audio Super-Resolution using Neural Networks. | Volodymyr Kuleshov, S. Zayd Enam, Stefano Ermon |
| 2017 | RECOMB | GATTACA: Lightweight Metagenomic Binning Using Kmer Counting. | Victoria Popic, Volodymyr Kuleshov, Michael P. Snyder, Serafim Batzoglou |
| 2017 | UAI | Hybrid Deep Discriminative/Generative Models for Semi-Supervised Learning. | Volodymyr Kuleshov, Stefano Ermon |
| 2015 | AISTATS | Tensor Factorization via Matrix Factorization. | Volodymyr Kuleshov, Arun Tejasvi Chaganty, Percy Liang |
| 2013 | ICML | Fast algorithms for sparse principal component analysis based on Rayleigh quotient iteration. | Volodymyr Kuleshov |
| 2010 | SAGT | On the Efficiency of Markets with Two-Sided Proportional Allocation Mechanisms. | Volodymyr Kuleshov, Adrian Vetta |