Kuleshov, Volodymyr

57 publications

ICLR 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

TMLR 2025 Calibrated Probabilistic Forecasts for Arbitrary Sequences Charles Marx, Volodymyr Kuleshov, Stefano Ermon

UAI 2025 Calibrated Regression Against an Adversary Without Regret Shachi Deshpande, Charles Marx, Volodymyr Kuleshov

AAAI 2025 Denoising Diffusion Variational Inference: Diffusion Models as Expressive Variational Posteriors Wasu Top Piriyakulkij, Yingheng Wang, Volodymyr Kuleshov

NeurIPS 2025 Encoder-Decoder Diffusion Language Models for Efficient Training and Inference Marianne Arriola, Yair Schiff, Hao Phung, Aaron Gokaslan, Volodymyr Kuleshov

ICLRW 2025 MeMDLM: De Novo Membrane Protein Design with Property-Guided Discrete Diffusion Shrey Goel, Vishrut Thoutam, Edgar Mariano Marroquin, Aaron Gokaslan, Arash Firouzbakht, Sophia Vincoff, Volodymyr Kuleshov, Huong T. Kratochvil, Pranam Chatterjee

NeurIPS 2025 Remasking Discrete Diffusion Models with Inference-Time Scaling Guanghan Wang, Yair Schiff, Subham Sekhar Sahoo, Volodymyr Kuleshov

ICLRW 2025 Remasking Discrete Diffusion Models with Inference-Time Scaling Guanghan Wang, Yair Schiff, 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

ICML 2025 The Diffusion Duality Subham Sekhar Sahoo, Justin Deschenaux, Aaron Gokaslan, Guanghan Wang, Justin T Chiu, Volodymyr Kuleshov

ICLRW 2025 The Diffusion Duality Subham Sekhar Sahoo, Justin Deschenaux, Aaron Gokaslan, Guanghan Wang, Justin T Chiu, Volodymyr Kuleshov

ICLRW 2024 Advancing Dna Language Models: The Genomics Long-Range Benchmark Chia Hsiang Kao, Evan Trop, McKinley Polen, Yair Schiff, Bernardo P de Almeida, Aaron Gokaslan, Thomas Pierrot, Volodymyr Kuleshov

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

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

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

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

UAI 2024 Calibrated and Conformal Propensity Scores for Causal Effect Estimation Shachi Deshpande, Volodymyr Kuleshov

CVPR 2024 CommonCanvas: 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

ICMLW 2024 Denoising Diffusion Variational Inference: Diffusion Models as Expressive Variational Posteriors Wasu Top Piriyakulkij, Yingheng Wang, Volodymyr Kuleshov

NeurIPS 2024 Diffusion Models with Learned Adaptive Noise Subham Sekhar Sahoo, Aaron Gokaslan, Chris De Sa, Volodymyr Kuleshov

NeurIPSW 2024 Diffusion Models with Learned Adaptive Noise Subham Sekhar Sahoo, Aaron Gokaslan, Christopher De Sa, Volodymyr Kuleshov

NeurIPSW 2024 Diffusion Models with Learned Adaptive Noise Processes Subham Sekhar Sahoo, Aaron Gokaslan, Christopher De Sa, 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

UAI 2024 Local Discovery by Partitioning: Polynomial-Time Causal Discovery Around Exposure-Outcome Pairs Jacqueline Maasch, Weishen Pan, Shantanu Gupta, Volodymyr Kuleshov, Kyra Gan, Fei Wang

NeurIPSW 2024 MeMDLM: De Novo Membrane Protein Design with Masked Discrete Diffusion Protein Language Models Shrey Goel, Vishrut Thoutam, Edgar Mariano Marroquin, Aaron Gokaslan, Arash Firouzbakht, Sophia Vincoff, Volodymyr Kuleshov, Huong T. Kratochvil, Pranam Chatterjee

TMLR 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

AISTATS 2024 Online Calibrated and Conformal Prediction Improves Bayesian Optimization Shachi Deshpande, Charles Marx, Volodymyr Kuleshov

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

NeurIPS 2024 Simple and Effective Masked Diffusion Language Models Subham Sekhar Sahoo, Marianne Arriola, Yair Schiff, Aaron Gokaslan, Edgar Marroquin, Justin T Chiu, Alexander Rush, Volodymyr Kuleshov

ICMLW 2024 Simple and Effective Masked Diffusion Language Models Subham Sekhar Sahoo, Marianne Arriola, Aaron Gokaslan, Edgar Mariano Marroquin, Alexander M Rush, Yair Schiff, Justin T Chiu, Volodymyr Kuleshov

ICMLW 2024 Simple and Effective Masked Diffusion Language Models Subham Sekhar Sahoo, Marianne Arriola, Aaron Gokaslan, Edgar Mariano Marroquin, Alexander M Rush, Yair Schiff, Justin T Chiu, Volodymyr Kuleshov

NeurIPSW 2024 Simple and Effective Masked Diffusion Language Models Subham Sekhar Sahoo, Marianne Arriola, Aaron Gokaslan, Yair Schiff, Edgar Mariano Marroquin, Justin T Chiu, Alexander M Rush, Volodymyr Kuleshov

ICMLW 2024 The GAN Is Dead; Long Live the GAN! a Modern Baseline GAN Nick Huang, Aaron Gokaslan, Volodymyr Kuleshov, James Tompkin

NeurIPS 2024 The GAN Is Dead; Long Live the GAN! a Modern GAN Baseline Yiwen Huang, Aaron Gokaslan, Volodymyr Kuleshov, James Tompkin

NeurIPSW 2024 The GAN Is Dead; Long Live the GAN! a Modern GAN Baseline Nick Huang, Aaron Gokaslan, Volodymyr Kuleshov, James Tompkin

NeurIPSW 2023 Active Preference Inference Using Language Models and Probabilistic Reasoning Top Piriyakulkij, Volodymyr Kuleshov, Kevin Ellis

ICLR 2023 Backpropagation Through Combinatorial Algorithms: Identity with Projection Works Subham Sekhar Sahoo, Anselm Paulus, Marin Vlastelica, Vít Musil, Volodymyr Kuleshov, Georg Martius

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

NeurIPSW 2023 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

NeurIPS 2023 QuIP: 2-Bit Quantization of Large Language Models with Guarantees Jerry Chee, Yaohui Cai, Volodymyr Kuleshov, Christopher M De Sa

ICMLW 2023 Regularized Data Programming with Automated Bayesian Prior Selection Jacqueline R. M. A. Maasch, Hao Zhang, Qian Yang, Fei Wang, Volodymyr Kuleshov

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

ICLR 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 2022 Autoregressive Quantile Flows for Predictive Uncertainty Estimation Phillip Si, Allan Bishop, Volodymyr Kuleshov

ICML 2022 Calibrated and Sharp Uncertainties in Deep Learning via Density Estimation Volodymyr Kuleshov, Shachi Deshpande

NeurIPS 2022 Deep Multi-Modal Structural Equations for Causal Effect Estimation with Unstructured Proxies Shachi Deshpande, Kaiwen Wang, Dhruv Sreenivas, Zheng Li, Volodymyr Kuleshov

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

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

ICML 2018 Accurate Uncertainties for Deep Learning Using Calibrated Regression Volodymyr Kuleshov, Nathan Fenner, Stefano Ermon

IJCAI 2018 Adversarial Constraint Learning for Structured Prediction Hongyu Ren, Russell Stewart, Jiaming Song, Volodymyr Kuleshov, Stefano Ermon

ICLR 2017 Audio Super-Resolution Using Neural Networks Volodymyr Kuleshov, S. Zayd Enam, Stefano Ermon

AAAI 2017 Estimating Uncertainty Online Against an Adversary Volodymyr Kuleshov, Stefano Ermon

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

NeurIPS 2017 Neural Variational Inference and Learning in Undirected Graphical Models Volodymyr Kuleshov, Stefano Ermon

NeurIPS 2015 Calibrated Structured Prediction Volodymyr Kuleshov, Percy Liang

AISTATS 2015 Tensor Factorization via Matrix Factorization Volodymyr Kuleshov, Arun Tejasvi Chaganty, Percy Liang

ICML 2013 Fast Algorithms for Sparse Principal Component Analysis Based on Rayleigh Quotient Iteration Volodymyr Kuleshov