Dan Alistarh - RankMe

336 papers A* 80A 22B 27Misc 2Journal 187Unranked 15

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Behemoth: Benchmarking Unlearning in LLMs Using Fully Synthetic Data. CoRR Eugenia Iofinova, Dan Alistarh
2026	J	`jnl`	DASH: Faster Shampoo via Batched Block Preconditioning and Efficient Inverse-Root Solvers. CoRR Ionut-Vlad Modoranu, Philip Zmushko, Erik Schultheis, Mher Safaryan, Dan Alistarh
2026	J	`jnl`	ECO: Quantized Training without Full-Precision Master Weights. CoRR Mahdi Nikdan, Amir Zandieh, Dan Alistarh, Vahab Mirrokni
2026	J	`jnl`	LoRDO: Distributed Low-Rank Optimization with Infrequent Communication. CoRR Andrej Jovanovic, Alex Iacob, Mher Safaryan, Ionut-Vlad Modoranu, Lorenzo Sani, William F. Shen, Xinchi Qiu, Dan Alistarh, Nicholas D. Lane
2026	J	`jnl`	MatGPTQ: Accurate and Efficient Post-Training Matryoshka Quantization. CoRR Maximilian Kleinegger, Elvir Crncevic, Dan Alistarh
2026	A	`conf`	QIGen: A Kernel Generator for Inference on Nonuniformly Quantized Large Language Models. CGO Tommaso Pegolotti, Dan Alistarh, Markus Püschel
2026	J	`jnl`	Quartet II: Accurate LLM Pre-Training in NVFP4 by Improved Unbiased Gradient Estimation. CoRR Andrei Panferov, Erik Schultheis, Soroush Tabesh, Dan Alistarh
2026	—	`conf`	Speculative Decoding Speed-of-Light: Optimal Lower Bounds via Branching Random Walks. EACL (Volume 1: Long Papers) Sergey Pankratov, Dan Alistarh
2026	J	`jnl`	Towards Robust Scaling Laws for Optimizers. CoRR Alexandra Volkova, Mher Safaryan, Christoph H. Lampert, Dan Alistarh
2025	—	`conf`	"Give Me BF16 or Give Me Death"? Accuracy-Performance Trade-Offs in LLM Quantization. ACL (1) Eldar Kurtic, Alexandre Noll Marques, Shubhra Pandit, Mark Kurtz, Dan Alistarh
2025	A	`conf`	An Almost-Logarithmic Lower Bound for Leader Election with Bounded Value Contention. DISC Dan Alistarh, Faith Ellen, Alexander Fedorov
2025	J	`jnl`	Beyond Outliers: A Study of Optimizers Under Quantization. CoRR Georgios Vlassis, Saleh Ashkboos, Alexandra Volkova, Torsten Hoefler, Dan Alistarh
2025	J	`jnl`	Bridging the Gap Between Promise and Performance for Microscaling FP4 Quantization. CoRR Vage Egiazarian, Roberto L. Castro, Denis Kuznedelev, Andrei Panferov, Eldar Kurtic, Shubhra Pandit, Alexandre Noll Marques, Mark Kurtz, Saleh Ashkboos, Torsten Hoefler, Dan Alistarh
2025	J	`jnl`	CAGE: Curvature-Aware Gradient Estimation For Accurate Quantization-Aware Training. CoRR Soroush Tabesh, Mher Safaryan, Dan Alistarh
2025	A*	`conf`	Cache Me If You Must: Adaptive Key-Value Quantization for Large Language Models. ICML Alina Shutova, Vladimir Malinovskii, Vage Egiazarian, Denis Kuznedelev, Denis Mazur, Nikita Surkov, Ivan Ermakov, Dan Alistarh
2025	J	`jnl`	Cache Me If You Must: Adaptive Key-Value Quantization for Large Language Models. CoRR Alina Shutova, Vladimir Malinovskii, Vage Egiazarian, Denis Kuznedelev, Denis Mazur, Nikita Surkov, Ivan Ermakov, Dan Alistarh
2025	J	`jnl`	Compression Scaling Laws:Unifying Sparsity and Quantization. CoRR Elias Frantar, Utku Evci, Wonpyo Park, Neil Houlsby, Dan Alistarh
2025	J	`jnl`	DarwinLM: Evolutionary Structured Pruning of Large Language Models. CoRR Shengkun Tang, Oliver Sieberling, Eldar Kurtic, Zhiqiang Shen, Dan Alistarh
2025	J	`jnl`	Distributed Computing Column 89 SIGACT News Dan Alistarh
2025	J	`jnl`	Efficient Data Selection at Scale via Influence Distillation. CoRR Mahdi Nikdan, Vincent Cohen-Addad, Dan Alistarh, Vahab Mirrokni
2025	A*	`conf`	EvoPress: Accurate Dynamic Model Compression via Evolutionary Search. ICML Oliver Sieberling, Denis Kuznedelev, Eldar Kurtic, Dan Alistarh
2025	J	`jnl`	Expand Neurons, Not Parameters. CoRR Linghao Kong, Inimai Subramanian, Yonadav Shavit, Micah Adler, Dan Alistarh, Nir Shavit
2025	J	`jnl`	HALO: Hadamard-Assisted Lower-Precision Optimization for LLMs. CoRR Saleh Ashkboos, Mahdi Nikdan, Soroush Tabesh, Roberto L. Castro, Torsten Hoefler, Dan Alistarh
2025	—	`conf`	HIGGS: Pushing the Limits of Large Language Model Quantization via the Linearity Theorem. NAACL (Long Papers) Vladimir Malinovskii, Andrei Panferov, Ivan Ilin, Han Guo, Peter Richtárik, Dan Alistarh
2025	J	`jnl`	Hogwild! Inference: Parallel LLM Generation via Concurrent Attention. CoRR Gleb Rodionov, Roman Garipov, Alina Shutova, George Yakushev, Vage Egiazarian, Anton Sinitsin, Denis Kuznedelev, Dan Alistarh
2025	A*	`conf`	Hybrid Decentralized Optimization: Leveraging Both First- and Zeroth-Order Optimizers for Faster Convergence. AAAI Shayan Talaei, Matin Ansaripour, Giorgi Nadiradze, Dan Alistarh
2025	A*	`conf`	LDAdam: Adaptive Optimization from Low-Dimensional Gradient Statistics. ICLR Thomas Robert, Mher Safaryan, Ionut-Vlad Modoranu, Dan Alistarh
2025	J	`jnl`	LLMQ: Efficient Lower-Precision Pretraining for Consumer GPUs. CoRR Erik Schultheis, Dan Alistarh
2025	A*	`conf`	Layer-wise Quantization for Quantized Optimistic Dual Averaging. ICML Anh Duc Nguyen, Ilia Markov, Frank Zhengqing Wu, Ali Ramezani-Kebrya, Kimon Antonakopoulos, Dan Alistarh, Volkan Cevher
2025	J	`jnl`	Layer-wise Quantization for Quantized Optimistic Dual Averaging. CoRR Anh Duc Nguyen, Ilia Markov, Frank Zhengqing Wu, Ali Ramezani-Kebrya, Kimon Antonakopoulos, Dan Alistarh, Volkan Cevher
2025	B	`conf`	MARLIN: Mixed-Precision Auto-Regressive Parallel Inference on Large Language Models. PPoPP Elias Frantar, Roberto L. Castro, Jiale Chen, Torsten Hoefler, Dan Alistarh
2025	J	`jnl`	Near-Optimal Leader Election in Population Protocols on Graphs. Distributed Comput. Dan Alistarh, Joel Rybicki, Sasha Voitovych
2025	J	`jnl`	Optimizers Qualitatively Alter Solutions And We Should Leverage This. CoRR Razvan Pascanu, Clare Lyle, Ionut-Vlad Modoranu, Naima Elosegui Borras, Dan Alistarh, Petar Velickovic, Sarath Chandar, Soham De, James Martens
2025	J	`jnl`	Position: It's Time to Act on the Risk of Efficient Personalized Text Generation. CoRR Eugenia Iofinova, Andrej Jovanovic, Dan Alistarh
2025	A*	`conf`	QuEST: Stable Training of LLMs with 1-Bit Weights and Activations. ICML Andrei Panferov, Jiale Chen, Soroush Tabesh, Mahdi Nikdan, Dan Alistarh
2025	J	`jnl`	QuEST: Stable Training of LLMs with 1-Bit Weights and Activations. CoRR Andrei Panferov, Jiale Chen, Soroush Tabesh, Roberto L. Castro, Mahdi Nikdan, Dan Alistarh
2025	J	`jnl`	Quartet: Native FP4 Training Can Be Optimal for Large Language Models. CoRR Roberto L. Castro, Andrei Panferov, Soroush Tabesh, Oliver Sieberling, Jiale Chen, Mahdi Nikdan, Saleh Ashkboos, Dan Alistarh
2025	J	`jnl`	SVD-Free Low-Rank Adaptive Gradient Optimization for Large Language Models. CoRR Ionut-Vlad Modoranu, Mher Safaryan, Erik Schultheis, Dan Alistarh
2025	A*	`conf`	Scalable Mechanistic Neural Networks. ICLR Jiale Chen, Dingling Yao, Adeel Pervez, Dan Alistarh, Francesco Locatello
2025	J	`jnl`	Speculative Decoding Speed-of-Light: Optimal Lower Bounds via Branching Random Walks. CoRR Sergey Pankratov, Dan Alistarh
2025	J	`jnl`	TACO Vision Models Can Be Efficiently Specialized via Few-Shot Task-Aware Compression. Trans. Mach. Learn. Res. Denis Kuznedelev, Soroush Tabesh, Kimia Noorbakhsh, Elias Frantar, Sara Beery, Eldar Kurtic, Dan Alistarh
2025	J	`jnl`	The Geometry of LLM Quantization: GPTQ as Babai's Nearest Plane Algorithm. CoRR Jiale Chen, Torsten Hoefler, Dan Alistarh
2025	A*	`conf`	The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws. ICLR Tian Jin, Ahmed Imtiaz Humayun, Utku Evci, Suvinay Subramanian, Amir Yazdanbakhsh, Dan Alistarh, Gintare Karolina Dziugaite
2025	J	`jnl`	The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws. CoRR Tian Jin, Ahmed Imtiaz Humayun, Utku Evci, Suvinay Subramanian, Amir Yazdanbakhsh, Dan Alistarh, Gintare Karolina Dziugaite
2025	J	`jnl`	The Unseen Frontier: Pushing the Limits of LLM Sparsity with Surrogate-Free ADMM. CoRR Kwanhee Lee, Hyeondo Jang, Dongyeop Lee, Dan Alistarh, Namhoon Lee
2025	J	`jnl`	Towards Combinatorial Interpretability of Neural Computation. CoRR Micah Adler, Dan Alistarh, Nir Shavit
2025	J	`jnl`	Unified Scaling Laws for Compressed Representations. CoRR Andrei Panferov, Alexandra Volkova, Ionut-Vlad Modoranu, Vage Egiazarian, Mher Safaryan, Dan Alistarh
2025	J	`jnl`	WUSH: Near-Optimal Adaptive Transforms for LLM Quantization. CoRR Jiale Chen, Vage Egiazarian, Torsten Hoefler, Dan Alistarh
2025	A*	`conf`	Wasserstein Distances, Neuronal Entanglement, and Sparsity. ICLR Shashata Sawmya, Linghao Kong, Ilia Markov, Dan Alistarh, Nir Shavit
2024	J	`jnl`	"Give Me BF16 or Give Me Death"? Accuracy-Performance Trade-Offs in LLM Quantization. CoRR Eldar Kurtic, Alexandre Noll Marques, Shubhra Pandit, Mark Kurtz, Dan Alistarh
2024	A	`ed.`	38th International Symposium on Distributed Computing, DISC 2024, Madrid, Spain, October 28 - November 1, 2024 DISC Dan Alistarh
2024	J	`jnl`	Accurate Compression of Text-to-Image Diffusion Models via Vector Quantization. CoRR Vage Egiazarian, Denis Kuznedelev, Anton Voronov, Ruslan Svirschevski, Michael Goin, Daniil Pavlov, Dan Alistarh, Dmitry Baranchuk
2024	J	`jnl`	Accurate Neural Network Pruning Requires Rethinking Sparse Optimization. Trans. Mach. Learn. Res. Denis Kuznedelev, Eldar Kurtic, Eugenia Iofinova, Elias Frantar, Alexandra Peste, Dan Alistarh
2024	A	`conf`	AsGrad: A Sharp Unified Analysis of Asynchronous-SGD Algorithms. AISTATS Rustem Islamov, Mher Safaryan, Dan Alistarh
2024	A	`conf`	Communication-Efficient Federated Learning With Data and Client Heterogeneity. AISTATS Hossein Zakerinia, Shayan Talaei, Giorgi Nadiradze, Dan Alistarh
2024	J	`jnl`	Distributed Computing Column 87 SIGACT News Dan Alistarh
2024	J	`jnl`	Distributed Computing Column 88 SIGACT News Dan Alistarh
2024	J	`jnl`	Enabling High-Sparsity Foundational Llama Models with Efficient Pretraining and Deployment. CoRR Abhinav Agarwalla, Abhay Gupta, Alexandre Noll Marques, Shubhra Pandit, Michael Goin, Eldar Kurtic, Kevin Leong, Tuan Nguyen, Mahmoud Salem, Dan Alistarh, Sean Lie, Mark Kurtz
2024	A*	`conf`	Error Feedback Can Accurately Compress Preconditioners. ICML Ionut-Vlad Modoranu, Aleksei Kalinov, Eldar Kurtic, Elias Frantar, Dan Alistarh
2024	J	`jnl`	EvoPress: Towards Optimal Dynamic Model Compression via Evolutionary Search. CoRR Oliver Sieberling, Denis Kuznedelev, Eldar Kurtic, Dan Alistarh
2024	A*	`conf`	Extreme Compression of Large Language Models via Additive Quantization. ICML Vage Egiazarian, Andrei Panferov, Denis Kuznedelev, Elias Frantar, Artem Babenko, Dan Alistarh
2024	J	`jnl`	Extreme Compression of Large Language Models via Additive Quantization. CoRR Vage Egiazarian, Andrei Panferov, Denis Kuznedelev, Elias Frantar, Artem Babenko, Dan Alistarh
2024	A	`conf`	Federated SGD with Local Asynchrony. ICDCS Bapi Chatterjee, Vyacheslav Kungurtsev, Dan Alistarh
2024	A*	`conf`	Game Dynamics and Equilibrium Computation in the Population Protocol Model. PODC Dan Alistarh, Krishnendu Chatterjee, Mehrdad Karrabi, John Lazarsfeld
2024	—	`conf`	How to Prune Your Language Model: Recovering Accuracy on the "Sparsity May Cry" Benchmark. CPAL Eldar Kurtic, Torsten Hoefler, Dan Alistarh
2024	—	`conf`	L-GreCo: Layerwise-adaptive Gradient Compression For Efficient Data-parallel Deep Learning. MLSys Ilia Markov, Kaveh Alim, Elias Frantar, Dan Alistarh
2024	J	`jnl`	LDAdam: Adaptive Optimization from Low-Dimensional Gradient Statistics. CoRR Thomas Robert, Mher Safaryan, Ionut-Vlad Modoranu, Dan Alistarh
2024	J	`jnl`	MARLIN: Mixed-Precision Auto-Regressive Parallel Inference on Large Language Models. CoRR Elias Frantar, Roberto L. Castro, Jiale Chen, Torsten Hoefler, Dan Alistarh
2024	A*	`conf`	Mathador-LM: A Dynamic Benchmark for Mathematical Reasoning on Large Language Models. EMNLP Eldar Kurtic, Amir Moeini, Dan Alistarh
2024	J	`jnl`	Mathador-LM: A Dynamic Benchmark for Mathematical Reasoning on Large Language Models. CoRR Eldar Kurtic, Amir Moeini, Dan Alistarh
2024	A*	`conf`	MicroAdam: Accurate Adaptive Optimization with Low Space Overhead and Provable Convergence. NeurIPS Ionut-Vlad Modoranu, Mher Safaryan, Grigory Malinovsky, Eldar Kurtic, Thomas Robert, Peter Richtárik, Dan Alistarh
2024	J	`jnl`	MicroAdam: Accurate Adaptive Optimization with Low Space Overhead and Provable Convergence. CoRR Ionut-Vlad Modoranu, Mher Safaryan, Grigory Malinovsky, Eldar Kurtic, Thomas Robert, Peter Richtárik, Dan Alistarh
2024	J	`jnl`	Mitigating the Impact of Outlier Channels for Language Model Quantization with Activation Regularization. CoRR Aniruddha Nrusimha, Mayank Mishra, Naigang Wang, Dan Alistarh, Rameswar Panda, Yoon Kim
2024	A*	`conf`	PV-Tuning: Beyond Straight-Through Estimation for Extreme LLM Compression. NeurIPS Vladimir Malinovskii, Denis Mazur, Ivan Ilin, Denis Kuznedelev, Konstantin Burlachenko, Kai Yi, Dan Alistarh, Peter Richtárik
2024	J	`jnl`	PV-Tuning: Beyond Straight-Through Estimation for Extreme LLM Compression. CoRR Vladimir Malinovskii, Denis Mazur, Ivan Ilin, Denis Kuznedelev, Konstantin Burlachenko, Kai Yi, Dan Alistarh, Peter Richtárik
2024	J	`jnl`	Panza: A Personalized Text Writing Assistant via Data Playback and Local Fine-Tuning. CoRR Armand Nicolicioiu, Eugenia Iofinova, Eldar Kurtic, Mahdi Nikdan, Andrei Panferov, Ilia Markov, Nir Shavit, Dan Alistarh
2024	J	`jnl`	Pushing the Limits of Large Language Model Quantization via the Linearity Theorem. CoRR Vladimir Malinovskii, Andrei Panferov, Ivan Ilin, Han Guo, Peter Richtárik, Dan Alistarh
2024	—	`conf`	QMoE: Sub-1-Bit Compression of Trillion Parameter Models. MLSys Elias Frantar, Dan Alistarh
2024	A*	`conf`	QUIK: Towards End-to-end 4-Bit Inference on Generative Large Language Models. EMNLP Saleh Ashkboos, Ilia Markov, Elias Frantar, Tingxuan Zhong, Xincheng Wang, Jie Ren, Torsten Hoefler, Dan Alistarh
2024	A*	`conf`	QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs. NeurIPS Saleh Ashkboos, Amirkeivan Mohtashami, Maximilian L. Croci, Bo Li, Pashmina Cameron, Martin Jaggi, Dan Alistarh, Torsten Hoefler, James Hensman
2024	J	`jnl`	QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs. CoRR Saleh Ashkboos, Amirkeivan Mohtashami, Maximilian L. Croci, Bo Li, Martin Jaggi, Dan Alistarh, Torsten Hoefler, James Hensman
2024	A*	`conf`	RoSA: Accurate Parameter-Efficient Fine-Tuning via Robust Adaptation. ICML Mahdi Nikdan, Soroush Tabesh, Elvir Crncevic, Dan Alistarh
2024	J	`jnl`	RoSA: Accurate Parameter-Efficient Fine-Tuning via Robust Adaptation. CoRR Mahdi Nikdan, Soroush Tabesh, Dan Alistarh
2024	A*	`conf`	SPADE: Sparsity-Guided Debugging for Deep Neural Networks. ICML Arshia Soltani Moakhar, Eugenia Iofinova, Elias Frantar, Dan Alistarh
2024	J	`jnl`	Scalable Mechanistic Neural Networks. CoRR Jiale Chen, Dingling Yao, Adeel Pervez, Dan Alistarh, Francesco Locatello
2024	A*	`conf`	Scaling Laws for Sparsely-Connected Foundation Models. ICLR Elias Frantar, Carlos Riquelme Ruiz, Neil Houlsby, Dan Alistarh, Utku Evci
2024	A*	`conf`	SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression. ICLR Tim Dettmers, Ruslan Svirschevski, Vage Egiazarian, Denis Kuznedelev, Elias Frantar, Saleh Ashkboos, Alexander Borzunov, Torsten Hoefler, Dan Alistarh
2024	J	`jnl`	Sparse Expansion and Neuronal Disentanglement. CoRR Shashata Sawmya, Linghao Kong, Ilia Markov, Dan Alistarh, Nir Shavit
2024	A*	`conf`	The Iterative Optimal Brain Surgeon: Faster Sparse Recovery by Leveraging Second-Order Information. NeurIPS Diyuan Wu, Ionut-Vlad Modoranu, Mher Safaryan, Denis Kuznedelev, Dan Alistarh
2024	J	`jnl`	The Iterative Optimal Brain Surgeon: Faster Sparse Recovery by Leveraging Second-Order Information. CoRR Diyuan Wu, Ionut-Vlad Modoranu, Mher Safaryan, Denis Kuznedelev, Dan Alistarh
2024	A	`conf`	Wait-free Trees with Asymptotically-Efficient Range Queries. IPDPS Ilya Kokorin, Victor Yudov, Vitaly Aksenov, Dan Alistarh
2023	J	`jnl`	A Brief Summary of PODC 2022. SIGACT News Dan Alistarh, Alkida Balliu, Dimitrios Los, Sean Ovens
2023	J	`jnl`	Accurate Neural Network Pruning Requires Rethinking Sparse Optimization. CoRR Denis Kuznedelev, Eldar Kurtic, Eugenia Iofinova, Elias Frantar, Alexandra Peste, Dan Alistarh
2023	J	`jnl`	AsGrad: A Sharp Unified Analysis of Asynchronous-SGD Algorithms. CoRR Rustem Islamov, Mher Safaryan, Dan Alistarh
2023	A*	`conf`	Bias in Pruned Vision Models: In-Depth Analysis and Countermeasures. CVPR Eugenia Iofinova, Alexandra Peste, Dan Alistarh
2023	J	`jnl`	Bias in Pruned Vision Models: In-Depth Analysis and Countermeasures. CoRR Eugenia Iofinova, Alexandra Peste, Dan Alistarh
2023	A*	`conf`	CAP: Correlation-Aware Pruning for Highly-Accurate Sparse Vision Models. NeurIPS Denis Kuznedelev, Eldar Kurtic, Elias Frantar, Dan Alistarh
2023	J	`jnl`	CQS: A Formally-Verified Framework for Fair and Abortable Synchronization. Proc. ACM Program. Lang. Nikita Koval, Dmitry Khalanskiy, Dan Alistarh
2023	A*	`conf`	CrAM: A Compression-Aware Minimizer. ICLR Alexandra Peste, Adrian Vladu, Eldar Kurtic, Christoph H. Lampert, Dan Alistarh
2023	J	`jnl`	Decentralized Learning Dynamics in the Gossip Model. CoRR John Lazarsfeld, Dan Alistarh
2023	J	`jnl`	Distributed Computing Column 86 SIGACT News Dan Alistarh
2023	J	`jnl`	Distributed Computing Column 86: A Summary of PODC 2022. SIGACT News Dan Alistarh
2023	J	`jnl`	Distributed Computing Column 87 Recent Advances in Multi-Pass Graph Streaming Lower Bounds. SIGACT News Dan Alistarh
2023	J	`jnl`	ELSA: Partial Weight Freezing for Overhead-Free Sparse Network Deployment. CoRR Paniz Halvachi, Alexandra Peste, Dan Alistarh, Christoph H. Lampert
2023	J	`jnl`	Efficient Self-Adjusting Search Trees via Lazy Updates. CoRR Alexander Slastin, Dan Alistarh, Vitaly Aksenov
2023	J	`jnl`	Error Feedback Can Accurately Compress Preconditioners. CoRR Ionut-Vlad Modoranu, Aleksei Kalinov, Eldar Kurtic, Dan Alistarh
2023	B	`conf`	Fast and Scalable Channels in Kotlin Coroutines. PPoPP Nikita Koval, Dan Alistarh, Roman Elizarov
2023	J	`jnl`	How to Prune Your Language Model: Recovering Accuracy on the "Sparsity May Cry" Benchmark. CoRR Eldar Kurtic, Torsten Hoefler, Dan Alistarh
2023	A*	`conf`	Knowledge Distillation Performs Partial Variance Reduction. NeurIPS Mher Safaryan, Alexandra Peste, Dan Alistarh
2023	J	`jnl`	Knowledge Distillation Performs Partial Variance Reduction. CoRR Mher Safaryan, Alexandra Peste, Dan Alistarh
2023	—	`conf`	Lincheck: A Practical Framework for Testing Concurrent Data Structures on JVM. CAV (1) Nikita Koval, Alexander Fedorov, Maria Sokolova, Dmitry Tsitelov, Dan Alistarh
2023	A*	`conf`	OPTQ: Accurate Quantization for Generative Pre-trained Transformers. ICLR Elias Frantar, Saleh Ashkboos, Torsten Hoefler, Dan Alistarh
2023	B	`conf`	Provably-Efficient and Internally-Deterministic Parallel Union-Find. SPAA Alexander Fedorov, Diba Hashemi, Giorgi Nadiradze, Dan Alistarh
2023	J	`jnl`	Provably-Efficient and Internally-Deterministic Parallel Union-Find. CoRR Alexander Fedorov, Diba Hashemi, Giorgi Nadiradze, Dan Alistarh
2023	J	`jnl`	QIGen: Generating Efficient Kernels for Quantized Inference on Large Language Models. CoRR Tommaso Pegolotti, Elias Frantar, Dan Alistarh, Markus Püschel
2023	J	`jnl`	QMoE: Practical Sub-1-Bit Compression of Trillion-Parameter Models. CoRR Elias Frantar, Dan Alistarh
2023	A*	`conf`	Quantized Distributed Training of Large Models with Convergence Guarantees. ICML Ilia Markov, Adrian Vladu, Qi Guo, Dan Alistarh
2023	J	`jnl`	Quantized Distributed Training of Large Models with Convergence Guarantees. CoRR Ilia Markov, Adrian Vladu, Qi Guo, Dan Alistarh
2023	J	`jnl`	Repeated Game Dynamics in Population Protocols. CoRR Dan Alistarh, Krishnendu Chatterjee, Mehrdad Karrabi, John Lazarsfeld
2023	J	`jnl`	SPADE: Sparsity-Guided Debugging for Deep Neural Networks. CoRR Arshia Soltani Moakhar, Eugenia Iofinova, Dan Alistarh
2023	J	`jnl`	Scaling Laws for Sparsely-Connected Foundation Models. CoRR Elias Frantar, Carlos Riquelme, Neil Houlsby, Dan Alistarh, Utku Evci
2023	J	`jnl`	SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression. CoRR Tim Dettmers, Ruslan Svirschevski, Vage Egiazarian, Denis Kuznedelev, Elias Frantar, Saleh Ashkboos, Alexander Borzunov, Torsten Hoefler, Dan Alistarh
2023	J	`jnl`	Sparse Fine-tuning for Inference Acceleration of Large Language Models. CoRR Eldar Kurtic, Denis Kuznedelev, Elias Frantar, Michael Goin, Dan Alistarh
2023	J	`jnl`	SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot. CoRR Elias Frantar, Dan Alistarh
2023	A*	`conf`	SparseGPT: Massive Language Models Can be Accurately Pruned in One-Shot. ICML Elias Frantar, Dan Alistarh
2023	A*	`conf`	SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks at the Edge. ICML Mahdi Nikdan, Tommaso Pegolotti, Eugenia Iofinova, Eldar Kurtic, Dan Alistarh
2023	J	`jnl`	SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks. CoRR Mahdi Nikdan, Tommaso Pegolotti, Eugenia Iofinova, Eldar Kurtic, Dan Alistarh
2023	J	`jnl`	The splay-list: a distribution-adaptive concurrent skip-list. Distributed Comput. Vitaly Aksenov, Dan Alistarh, Alexandra Drozdova, Amirkeivan Mohtashami
2023	J	`jnl`	Towards End-to-end 4-Bit Inference on Generative Large Language Models. CoRR Saleh Ashkboos, Ilia Markov, Elias Frantar, Tingxuan Zhong, Xincheng Wang, Jie Ren, Torsten Hoefler, Dan Alistarh
2023	J	`jnl`	Vision Models Can Be Efficiently Specialized via Few-Shot Task-Aware Compression. CoRR Denis Kuznedelev, Soroush Tabesh, Kimia Noorbakhsh, Elias Frantar, Sara Beery, Eldar Kurtic, Dan Alistarh
2023	J	`jnl`	Wait-free Trees with Asymptotically-Efficient Range Queries. CoRR Ilya Kokorin, Dan Alistarh, Vitaly Aksenov
2023	J	`jnl`	Wait-free approximate agreement on graphs. Theor. Comput. Sci. Dan Alistarh, Faith Ellen, Joel Rybicki
2023	J	`jnl`	Why Extension-Based Proofs Fail. SIAM J. Comput. Dan Alistarh, James Aspnes, Faith Ellen, Rati Gelashvili, Leqi Zhu
2023	J	`jnl`	ZipLM: Hardware-Aware Structured Pruning of Language Models. CoRR Eldar Kurtic, Elias Frantar, Dan Alistarh
2023	A*	`conf`	ZipLM: Inference-Aware Structured Pruning of Language Models. NeurIPS Eldar Kurtic, Elias Frantar, Dan Alistarh
2022	A	`conf`	CGX: adaptive system support for communication-efficient deep learning. Middleware Ilia Markov, Hamidreza Ramezani-Kebrya, Dan Alistarh
2022	J	`jnl`	CrAM: A Compression-Aware Minimizer. CoRR Alexandra Peste, Adrian Vladu, Dan Alistarh, Christoph H. Lampert
2022	J	`jnl`	Distributed Computing Column 85 Elastic Consistency: A Consistency Criterion for Distributed Optimization. SIGACT News Dan Alistarh
2022	J	`jnl`	Dynamic Averaging Load Balancing on Cycles. Algorithmica Dan Alistarh, Giorgi Nadiradze, Amirmojtaba Sabour
2022	J	`jnl`	Elastic Consistency: A Consistency Criterion for Distributed Optimization. SIGACT News Dan Alistarh, Ilia Markov, Giorgi Nadiradze
2022	J	`jnl`	Fast and Scalable Channels in Kotlin Coroutines. CoRR Nikita Koval, Dan Alistarh, Roman Elizarov
2022	J	`jnl`	GMP*: Well-Tuned Global Magnitude Pruning Can Outperform Most BERT-Pruning Methods. CoRR Eldar Kurtic, Dan Alistarh
2022	J	`jnl`	GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers. CoRR Elias Frantar, Saleh Ashkboos, Torsten Hoefler, Dan Alistarh
2022	A*	`conf`	How Well Do Sparse ImageNet Models Transfer? CVPR Eugenia Iofinova, Alexandra Peste, Mark Kurtz, Dan Alistarh
2022	J	`jnl`	Hybrid Decentralized Optimization: First- and Zeroth-Order Optimizers Can Be Jointly Leveraged For Faster Convergence. CoRR Shayan Talaei, Giorgi Nadiradze, Dan Alistarh
2022	J	`jnl`	L-GreCo: An Efficient and General Framework for Layerwise-Adaptive Gradient Compression. CoRR Mohammadreza Alimohammadi, Ilia Markov, Elias Frantar, Dan Alistarh
2022	B	`conf`	Multi-queues can be state-of-the-art priority schedulers. PPoPP Anastasiia Postnikova, Nikita Koval, Giorgi Nadiradze, Dan Alistarh
2022	A*	`conf`	Near-Optimal Leader Election in Population Protocols on Graphs. PODC Dan Alistarh, Joel Rybicki, Sasha Voitovych
2022	J	`jnl`	Near-Optimal Leader Election in Population Protocols on Graphs. CoRR Dan Alistarh, Joel Rybicki, Sasha Voitovych
2022	A*	`conf`	Optimal Brain Compression: A Framework for Accurate Post-Training Quantization and Pruning. NeurIPS Elias Frantar, Dan Alistarh
2022	J	`jnl`	Optimal Brain Compression: A Framework for Accurate Post-Training Quantization and Pruning. CoRR Elias Frantar, Dan Alistarh
2022	J	`jnl`	PathCAS: An Efficient Middle Ground for Concurrent Search Data Structures. CoRR Trevor Brown, William Sigouin, Dan Alistarh
2022	B	`conf`	PathCAS: an efficient middle ground for concurrent search data structures. PPoPP Trevor Brown, William Sigouin, Dan Alistarh
2022	J	`jnl`	QuAFL: Federated Averaging Can Be Both Asynchronous and Communication-Efficient. CoRR Hossein Zakerinia, Shayan Talaei, Giorgi Nadiradze, Dan Alistarh
2022	A*	`conf`	SPDY: Accurate Pruning with Speedup Guarantees. ICML Elias Frantar, Dan Alistarh
2022	J	`jnl`	SPDY: Accurate Pruning with Speedup Guarantees. CoRR Elias Frantar, Dan Alistarh
2022	J	`jnl`	Scaling the Wild: Decentralizing Hogwild!-style Shared-memory SGD. CoRR Bapi Chatterjee, Vyacheslav Kungurtsev, Dan Alistarh
2022	A*	`conf`	The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models. EMNLP Eldar Kurtic, Daniel Campos, Tuan Nguyen, Elias Frantar, Mark Kurtz, Benjamin Fineran, Michael Goin, Dan Alistarh
2022	J	`jnl`	The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models. CoRR Eldar Kurtic, Daniel Campos, Tuan Nguyen, Elias Frantar, Mark Kurtz, Benjamin Fineran, Michael Goin, Dan Alistarh
2022	J	`jnl`	oViT: An Accurate Second-Order Pruning Framework for Vision Transformers. CoRR Denis Kuznedelev, Eldar Kurtic, Elias Frantar, Dan Alistarh
2021	J	`jnl`	A Formally-Verified Framework for Fair Synchronization in Kotlin Coroutines. CoRR Nikita Koval, Dmitry Khalanskiy, Dan Alistarh
2021	B	`conf`	A Scalable Concurrent Algorithm for Dynamic Connectivity. SPAA Alexander Fedorov, Nikita Koval, Dan Alistarh
2021	J	`jnl`	A Scalable Concurrent Algorithm for Dynamic Connectivity. CoRR Alexander Fedorov, Nikita Koval, Dan Alistarh
2021	A*	`conf`	AC/DC: Alternating Compressed/DeCompressed Training of Deep Neural Networks. NeurIPS Alexandra Peste, Eugenia Iofinova, Adrian Vladu, Dan Alistarh
2021	J	`jnl`	AC/DC: Alternating Compressed/DeCompressed Training of Deep Neural Networks. CoRR Alexandra Peste, Eugenia Iofinova, Adrian Vladu, Dan Alistarh
2021	A*	`conf`	Asynchronous Decentralized SGD with Quantized and Local Updates. NeurIPS Giorgi Nadiradze, Amirmojtaba Sabour, Peter Davies, Shigang Li, Dan Alistarh
2021	A*	`conf`	Asynchronous Optimization Methods for Efficient Training of Deep Neural Networks with Guarantees. AAAI Vyacheslav Kungurtsev, Malcolm Egan, Bapi Chatterjee, Dan Alistarh
2021	J	`jnl`	Breaking (Global) Barriers in Parallel Stochastic Optimization With Wait-Avoiding Group Averaging. IEEE Trans. Parallel Distributed Syst. Shigang Li, Tal Ben-Nun, Giorgi Nadiradze, Salvatore Di Girolamo, Nikoli Dryden, Dan Alistarh, Torsten Hoefler
2021	A	`conf`	Brief Announcement: Fast Graphical Population Protocols. DISC Dan Alistarh, Rati Gelashvili, Joel Rybicki
2021	A*	`conf`	Byzantine-Resilient Non-Convex Stochastic Gradient Descent. ICLR Zeyuan Allen-Zhu, Faeze Ebrahimianghazani, Jerry Li, Dan Alistarh
2021	B	`conf`	Collecting Coupons is Faster with Friends. SIROCCO Dan Alistarh, Peter Davies
2021	J	`jnl`	Collecting Coupons is Faster with Friends. CoRR Dan Alistarh, Peter Davies
2021	A*	`conf`	Communication-Efficient Distributed Optimization with Quantized Preconditioners. ICML Foivos Alimisis, Peter Davies, Dan Alistarh
2021	J	`jnl`	Communication-Efficient Distributed Optimization with Quantized Preconditioners. CoRR Foivos Alimisis, Peter Davies, Dan Alistarh
2021	A*	`conf`	Comparison Dynamics in Population Protocols. PODC Dan Alistarh, Martin Töpfer, Przemyslaw Uznanski
2021	J	`jnl`	Distributed Computing Column 81: Byzantine Agreement with Less Communication: Recent Advances. SIGACT News Dan Alistarh
2021	J	`jnl`	Distributed Computing Column 82 SIGACT News Dan Alistarh
2021	J	`jnl`	Distributed Computing Column 83 Five Ways Not To Fool Yourself: Designing Experiments for Understanding Performance. SIGACT News Dan Alistarh
2021	J	`jnl`	Distributed Computing Column 84: Perspectives on the Paper "CCS Expressions, Finite State Processes, and Three Problems of Equivalence". SIGACT News Dan Alistarh
2021	A*	`conf`	Distributed Principal Component Analysis with Limited Communication. NeurIPS Foivos Alimisis, Peter Davies, Bart Vandereycken, Dan Alistarh
2021	J	`jnl`	Distributed Principal Component Analysis with Limited Communication. CoRR Foivos Alimisis, Peter Davies, Bart Vandereycken, Dan Alistarh
2021	J	`jnl`	Efficient Matrix-Free Approximations of Second-Order Information, with Applications to Pruning and Optimization. CoRR Elias Frantar, Eldar Kurtic, Dan Alistarh
2021	A*	`conf`	Elastic Consistency: A Practical Consistency Model for Distributed Stochastic Gradient Descent. AAAI Giorgi Nadiradze, Ilia Markov, Bapi Chatterjee, Vyacheslav Kungurtsev, Dan Alistarh
2021	B	`conf`	Fast Graphical Population Protocols. OPODIS Dan Alistarh, Rati Gelashvili, Joel Rybicki
2021	J	`jnl`	Fast Graphical Population Protocols. CoRR Dan Alistarh, Rati Gelashvili, Joel Rybicki
2021	J	`jnl`	How Well Do Sparse Imagenet Models Transfer? CoRR Eugenia Iofinova, Alexandra Peste, Mark Kurtz, Dan Alistarh
2021	A	`conf`	Lower Bounds for Shared-Memory Leader Election Under Bounded Write Contention. DISC Dan Alistarh, Rati Gelashvili, Giorgi Nadiradze
2021	J	`jnl`	Lower Bounds for Shared-Memory Leader Election under Bounded Write Contention. CoRR Dan Alistarh, Rati Gelashvili, Giorgi Nadiradze
2021	A*	`conf`	M-FAC: Efficient Matrix-Free Approximations of Second-Order Information. NeurIPS Elias Frantar, Eldar Kurtic, Dan Alistarh
2021	J	`jnl`	Multi-Queues Can Be State-of-the-Art Priority Schedulers. CoRR Anastasiia Postnikova, Nikita Koval, Giorgi Nadiradze, Dan Alistarh
2021	J	`jnl`	NUQSGD: Provably Communication-efficient Data-parallel SGD via Nonuniform Quantization. J. Mach. Learn. Res. Ali Ramezani-Kebrya, Fartash Faghri, Ilya Markov, Vitalii Aksenov, Dan Alistarh, Daniel M. Roy
2021	A*	`conf`	New Bounds For Distributed Mean Estimation and Variance Reduction. ICLR Peter Davies, Vijaykrishna Gurunanthan, Niusha Moshrefi, Saleh Ashkboos, Dan Alistarh
2021	J	`jnl`	Project CGX: Scalable Deep Learning on Commodity GPUs. CoRR Ilia Markov, Hamidreza Ramezani-Kebrya, Dan Alistarh
2021	J	`jnl`	SSSE: Efficiently Erasing Samples from Trained Machine Learning Models. CoRR Alexandra Peste, Dan Alistarh, Christoph H. Lampert
2021	J	`jnl`	Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks. CoRR Torsten Hoefler, Dan Alistarh, Tal Ben-Nun, Nikoli Dryden, Alexandra Peste
2021	J	`jnl`	Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks. J. Mach. Learn. Res. Torsten Hoefler, Dan Alistarh, Tal Ben-Nun, Nikoli Dryden, Alexandra Peste
2021	A*	`conf`	Towards Tight Communication Lower Bounds for Distributed Optimisation. NeurIPS Janne H. Korhonen, Dan Alistarh
2021	B	`conf`	Wait-Free Approximate Agreement on Graphs. SIROCCO Dan Alistarh, Faith Ellen, Joel Rybicki
2021	J	`jnl`	Wait-free approximate agreement on graphs. CoRR Dan Alistarh, Faith Ellen, Joel Rybicki
2020	A*	`conf`	Adaptive Gradient Quantization for Data-Parallel SGD. NeurIPS Fartash Faghri, Iman Tabrizian, Ilia Markov, Dan Alistarh, Daniel M. Roy, Ali Ramezani-Kebrya
2020	J	`jnl`	Adaptive Gradient Quantization for Data-Parallel SGD. CoRR Fartash Faghri, Iman Tabrizian, Ilia Markov, Dan Alistarh, Daniel M. Roy, Ali Ramezani-Kebrya
2020	J	`jnl`	Analysis and Evaluation of Non-Blocking Interpolation Search Trees. CoRR Aleksandar Prokopec, Trevor Brown, Dan Alistarh
2020	J	`jnl`	Breaking (Global) Barriers in Parallel Stochastic Optimization with Wait-Avoiding Group Averaging. CoRR Shigang Li, Tal Ben-Nun, Dan Alistarh, Salvatore Di Girolamo, Nikoli Dryden, Torsten Hoefler
2020	A*	`conf`	Brief Announcement: Why Extension-Based Proofs Fail. PODC Dan Alistarh, James Aspnes, Faith Ellen, Rati Gelashvili, Leqi Zhu
2020	J	`jnl`	Byzantine-Resilient Non-Convex Stochastic Gradient Descent. CoRR Zeyuan Allen-Zhu, Faeze Ebrahimian, Jerry Li, Dan Alistarh
2020	J	`jnl`	Compressive Sensing Using Iterative Hard Thresholding With Low Precision Data Representation: Theory and Applications. IEEE Trans. Signal Process. Nezihe Merve Gürel, Kaan Kara, Alen Stojanov, Tyler M. Smith, Thomas Lemmin, Dan Alistarh, Markus Püschel, Ce Zhang
2020	J	`jnl`	Distributed Computing Column 77 Consensus Dynamics: An Overview. SIGACT News Dan Alistarh
2020	J	`jnl`	Distributed Computing Column 78: 60 Years of Mastering Concurrent Computing through Sequential Thinking. SIGACT News Dan Alistarh
2020	J	`jnl`	Distributed Computing Column 79: Using Round Elimination to Understand Locality. SIGACT News Dan Alistarh
2020	J	`jnl`	Distributed Computing Column 80: Annual Review 2020. SIGACT News Dan Alistarh
2020	J	`jnl`	Distributed Mean Estimation with Optimal Error Bounds. CoRR Dan Alistarh, Saleh Ashkboos, Peter Davies
2020	A*	`conf`	Dynamic Averaging Load Balancing on Cycles. ICALP Dan Alistarh, Giorgi Nadiradze, Amirmojtaba Sabour
2020	J	`jnl`	Dynamic Averaging Load Balancing on Cycles. CoRR Dan Alistarh, Giorgi Nadiradze, Amirmojtaba Sabour
2020	J	`jnl`	Efficiency Guarantees for Parallel Incremental Algorithms under Relaxed Schedulers. CoRR Dan Alistarh, Nikita Koval, Giorgi Nadiradze
2020	J	`jnl`	Elastic Consistency: A General Consistency Model for Distributed Stochastic Gradient Descent. CoRR Dan Alistarh, Bapi Chatterjee, Vyacheslav Kungurtsev
2020	J	`jnl`	Fast General Distributed Transactions with Opacity using Global Time. CoRR Alex Shamis, Matthew Renzelmann, Stanko Novakovic, Georgios Chatzopoulos, Anders T. Gjerdrum, Dan Alistarh, Aleksandar Dragojevic, Dushyanth Narayanan, Miguel Castro
2020	J	`jnl`	Improved Communication Lower Bounds for Distributed Optimisation. CoRR Dan Alistarh, Janne H. Korhonen
2020	A*	`conf`	Inducing and Exploiting Activation Sparsity for Fast Inference on Deep Neural Networks. ICML Mark Kurtz, Justin Kopinsky, Rati Gelashvili, Alexander Matveev, John Carr, Michael Goin, William M. Leiserson, Sage Moore, Nir Shavit, Dan Alistarh
2020	B	`conf`	Memory Tagging: Minimalist Synchronization for Scalable Concurrent Data Structures. SPAA Dan Alistarh, Trevor Brown, Nandini Singhal
2020	B	`conf`	Non-blocking interpolation search trees with doubly-logarithmic running time. PPoPP Trevor Brown, Aleksandar Prokopec, Dan Alistarh
2020	A*	`conf`	On the Sample Complexity of Adversarial Multi-Source PAC Learning. ICML Nikola Konstantinov, Elias Frantar, Dan Alistarh, Christoph Lampert
2020	J	`jnl`	On the Sample Complexity of Adversarial Multi-Source PAC Learning. CoRR Nikola Konstantinov, Elias Frantar, Dan Alistarh, Christoph H. Lampert
2020	J	`jnl`	Relaxed Scheduling for Scalable Belief Propagation. CoRR Vitaly Aksenov, Dan Alistarh, Janne H. Korhonen
2020	J	`jnl`	Robust Comparison in Population Protocols. CoRR Dan Alistarh, Martin Töpfer, Przemyslaw Uznanski
2020	A*	`conf`	Scalable Belief Propagation via Relaxed Scheduling. NeurIPS Vitaly Aksenov, Dan Alistarh, Janne H. Korhonen
2020	J	`jnl`	Stochastic Gradient Langevin with Delayed Gradients. CoRR Vyacheslav Kungurtsev, Bapi Chatterjee, Dan Alistarh
2020	B	`conf`	Taming unbalanced training workloads in deep learning with partial collective operations. PPoPP Shigang Li, Tal Ben-Nun, Salvatore Di Girolamo, Dan Alistarh, Torsten Hoefler
2020	B	`conf`	Testing concurrency on the JVM with lincheck. PPoPP Nikita Koval, Maria Sokolova, Alexander Fedorov, Dan Alistarh, Dmitry Tsitelov
2020	A	`conf`	The Splay-List: A Distribution-Adaptive Concurrent Skip-List. DISC Vitaly Aksenov, Dan Alistarh, Alexandra Drozdova, Amirkeivan Mohtashami
2020	J	`jnl`	The Splay-List: A Distribution-Adaptive Concurrent Skip-List. CoRR Vitaly Aksenov, Dan Alistarh, Alexandra Drozdova, Amirkeivan Mohtashami
2020	A*	`conf`	WoodFisher: Efficient Second-Order Approximation for Neural Network Compression. NeurIPS Sidak Pal Singh, Dan Alistarh
2020	J	`jnl`	WoodFisher: Efficient second-order approximations for model compression. CoRR Sidak Pal Singh, Dan Alistarh
2019	J	`jnl`	Distributed Computing Column 76: Annual Review 2019. SIGACT News Dan Alistarh
2019	A*	`conf`	Distributed Learning over Unreliable Networks. ICML Chen Yu, Hanlin Tang, Cédric Renggli, Simon Kassing, Ankit Singla, Dan Alistarh, Ce Zhang, Ji Liu
2019	B	`conf`	Efficiency Guarantees for Parallel Incremental Algorithms under Relaxed Schedulers. SPAA Dan Alistarh, Giorgi Nadiradze, Nikita Koval
2019	B	`conf`	In Search of the Fastest Concurrent Union-Find Algorithm. OPODIS Dan Alistarh, Alexander Fedorov, Nikita Koval
2019	J	`jnl`	In Search of the Fastest Concurrent Union-Find Algorithm. CoRR Dan Alistarh, Alexander Fedorov, Nikita Koval
2019	B	`conf`	Lock-free channels for programming via communicating sequential processes: poster. PPoPP Nikita Koval, Dan Alistarh, Roman Elizarov
2019	J	`jnl`	Performance Prediction for Coarse-Grained Locking. CoRR Vitaly Aksenov, Dan Alistarh, Petr Kuznetsov
2019	J	`jnl`	PopSGD: Decentralized Stochastic Gradient Descent in the Population Model. CoRR Giorgi Nadiradze, Amirmojtaba Sabour, Aditya Sharma, Ilia Markov, Vitaly Aksenov, Dan Alistarh
2019	A*	`conf`	Powerset Convolutional Neural Networks. NeurIPS Chris Wendler, Markus Püschel, Dan Alistarh
2019	J	`jnl`	Powerset Convolutional Neural Networks. CoRR Chris Wendler, Dan Alistarh, Markus Püschel
2019	—	`conf`	Scalable FIFO Channels for Programming via Communicating Sequential Processes. Euro-Par Nikita Koval, Dan Alistarh, Roman Elizarov
2019	A	`conf`	SparCML: high-performance sparse communication for machine learning. SC Cédric Renggli, Saleh Ashkboos, Mehdi Aghagolzadeh, Dan Alistarh, Torsten Hoefler
2019	J	`jnl`	SysML: The New Frontier of Machine Learning Systems. CoRR Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Eric S. Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros G. Dimakis, Pradeep Dubey, Charles Elkan, Grigori Fursin, Gregory R. Ganger, Lise Getoor, Phillip B. Gibbons, Garth A. Gibson, Joseph E. Gonzalez, Justin Gottschlich, Song Han, Kim M. Hazelwood, Furong Huang, Martin Jaggi, Kevin Jamieson, Michael I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konecný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Gordon Murray, Dimitris S. Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan Randall Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric P. Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar
2019	J	`jnl`	Taming Unbalanced Training Workloads in Deep Learning with Partial Collective Operations. CoRR Shigang Li, Tal Ben-Nun, Salvatore Di Girolamo, Dan Alistarh, Torsten Hoefler
2019	A*	`conf`	Why extension-based proofs fail. STOC Dan Alistarh, James Aspnes, Faith Ellen, Rati Gelashvili, Leqi Zhu
2018	A*	`conf`	A Brief Tutorial on Distributed and Concurrent Machine Learning. PODC Dan Alistarh
2018	—	`ed.`	Algorithmic Aspects of Cloud Computing - Third International Workshop, ALGOCLOUD 2017, Vienna, Austria, September 5, 2017, Revised Selected Papers ALGOCLOUD Dan Alistarh, Alex Delis, George Pallis
2018	A*	`conf`	Brief Announcement: Performance Prediction for Coarse-Grained Locking. PODC Vitaly Aksenov, Dan Alistarh, Petr Kuznetsov
2018	A*	`conf`	Byzantine Stochastic Gradient Descent. NeurIPS Dan Alistarh, Zeyuan Allen-Zhu, Jerry Li
2018	J	`jnl`	Byzantine Stochastic Gradient Descent. CoRR Dan Alistarh, Zeyuan Allen-Zhu, Jerry Li
2018	J	`jnl`	Communication-efficient randomized consensus. Distributed Comput. Dan Alistarh, James Aspnes, Valerie King, Jared Saia
2018	J	`jnl`	Compressive Sensing with Low Precision Data Representation: Radio Astronomy and Beyond. CoRR Nezihe Merve Gürel, Kaan Kara, Dan Alistarh, Ce Zhang
2018	J	`jnl`	DataBright: Towards a Global Exchange for Decentralized Data Ownership and Trusted Computation. CoRR David Dao, Dan Alistarh, Claudiu Musat, Ce Zhang
2018	J	`jnl`	Distributed Learning over Unreliable Networks. CoRR Hanlin Tang, Chen Yu, Cédric Renggli, Simon Kassing, Ankit Singla, Dan Alistarh, Ji Liu, Ce Zhang
2018	B	`conf`	Distributionally Linearizable Data Structures. SPAA Dan Alistarh, Trevor Brown, Justin Kopinsky, Jerry Zheng Li, Giorgi Nadiradze
2018	J	`jnl`	Distributionally Linearizable Data Structures. CoRR Dan Alistarh, Trevor Brown, Justin Kopinsky, Jerry Zheng Li, Giorgi Nadiradze
2018	—	`conf`	Fast Quantized Arithmetic on x86: Trading Compute for Data Movement. SiPS Alen Stojanov, Tyler Michael Smith, Dan Alistarh, Markus Püschel
2018	—	`conf`	Gradient compression for communication-limited convex optimization. CDC Sarit Khirirat, Mikael Johansson, Dan Alistarh
2018	J	`jnl`	Inherent limitations of hybrid transactional memory. Distributed Comput. Dan Alistarh, Justin Kopinsky, Petr Kuznetsov, Srivatsan Ravi, Nir Shavit
2018	—	`conf`	Model compression via distillation and quantization. ICLR (Poster) Antonio Polino, Razvan Pascanu, Dan Alistarh
2018	J	`jnl`	Model compression via distillation and quantization. CoRR Antonio Polino, Razvan Pascanu, Dan Alistarh
2018	J	`jnl`	Recent Algorithmic Advances in Population Protocols. SIGACT News Dan Alistarh, Rati Gelashvili
2018	A*	`conf`	Relaxed Schedulers Can Efficiently Parallelize Iterative Algorithms. PODC Dan Alistarh, Trevor Brown, Justin Kopinsky, Giorgi Nadiradze
2018	J	`jnl`	Relaxed Schedulers Can Efficiently Parallelize Iterative Algorithms. CoRR Dan Alistarh, Trevor Brown, Justin Kopinsky, Giorgi Nadiradze
2018	A*	`conf`	Session details: Session 1B: Shared Memory Theory. PODC Dan Alistarh
2018	A*	`conf`	Space-Optimal Majority in Population Protocols. SODA Dan Alistarh, James Aspnes, Rati Gelashvili
2018	J	`jnl`	SparCML: High-Performance Sparse Communication for Machine Learning. CoRR Cédric Renggli, Dan Alistarh, Torsten Hoefler
2018	B	`conf`	Synchronous Multi-GPU Training for Deep Learning with Low-Precision Communications: An Empirical Study. EDBT Demjan Grubic, Leo Tam, Dan Alistarh, Ce Zhang
2018	A*	`conf`	The Convergence of Sparsified Gradient Methods. NeurIPS Dan Alistarh, Torsten Hoefler, Mikael Johansson, Nikola Konstantinov, Sarit Khirirat, Cédric Renggli
2018	J	`jnl`	The Convergence of Sparsified Gradient Methods. CoRR Dan Alistarh, Torsten Hoefler, Mikael Johansson, Sarit Khirirat, Nikola Konstantinov, Cédric Renggli
2018	A*	`conf`	The Convergence of Stochastic Gradient Descent in Asynchronous Shared Memory. PODC Dan Alistarh, Christopher De Sa, Nikola Konstantinov
2018	J	`jnl`	The Convergence of Stochastic Gradient Descent in Asynchronous Shared Memory. CoRR Dan Alistarh, Christopher De Sa, Nikola Konstantinov
2018	B	`conf`	The Transactional Conflict Problem. SPAA Dan Alistarh, Syed Kamran Haider, Raphael Kübler, Giorgi Nadiradze
2018	J	`jnl`	The Transactional Conflict Problem. CoRR Dan Alistarh, Syed Kamran Haider, Raphael Kübler, Giorgi Nadiradze
2018	J	`jnl`	ThreadScan: Automatic and Scalable Memory Reclamation. ACM Trans. Parallel Comput. Dan Alistarh, William M. Leiserson, Alexander Matveev, Nir Shavit
2018	J	`jnl`	Why Extension-Based Proofs Fail. CoRR Dan Alistarh, James Aspnes, Faith Ellen, Rati Gelashvili, Leqi Zhu
2017	Misc	`conf`	FPGA-Accelerated Dense Linear Machine Learning: A Precision-Convergence Trade-Off. FCCM Kaan Kara, Dan Alistarh, Gustavo Alonso, Onur Mutlu, Ce Zhang
2017	A	`conf`	Forkscan: Conservative Memory Reclamation for Modern Operating Systems. EuroSys Dan Alistarh, William M. Leiserson, Alexander Matveev, Nir Shavit
2017	J	`jnl`	Lease/Release: Architectural Support for Scaling Contended Data Structures. ACM Trans. Parallel Comput. Syed Kamran Haider, William Hasenplaugh, Dan Alistarh
2017	—	`conf`	QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding. NIPS Dan Alistarh, Demjan Grubic, Jerry Li, Ryota Tomioka, Milan Vojnovic
2017	B	`conf`	Robust Detection in Leak-Prone Population Protocols. DNA Dan Alistarh, Bartlomiej Dudek, Adrian Kosowski, David Soloveichik, Przemyslaw Uznanski
2017	J	`jnl`	Robust Detection in Leak-Prone Population Protocols. CoRR Dan Alistarh, Bartlomiej Dudek, Adrian Kosowski, David Soloveichik, Przemyslaw Uznanski
2017	J	`jnl`	Space-Optimal Majority in Population Protocols. CoRR Dan Alistarh, James Aspnes, Rati Gelashvili
2017	A*	`conf`	The Power of Choice in Priority Scheduling. PODC Dan Alistarh, Justin Kopinsky, Jerry Li, Giorgi Nadiradze
2017	J	`jnl`	The Power of Choice in Priority Scheduling. CoRR Dan Alistarh, Justin Kopinsky, Jerry Li, Giorgi Nadiradze
2017	A*	`conf`	Time-Space Trade-offs in Population Protocols. SODA Dan Alistarh, James Aspnes, David Eisenstat, Rati Gelashvili, Ronald L. Rivest
2017	A	`conf`	Towards unlicensed cellular networks in TV white spaces. CoNEXT Ghufran Baig, Dan Alistarh, Thomas Karagiannis, Bozidar Radunovic, Matthew Balkwill, Lili Qiu
2017	A*	`conf`	ZipML: Training Linear Models with End-to-End Low Precision, and a Little Bit of Deep Learning. ICML Hantian Zhang, Jerry Li, Kaan Kara, Dan Alistarh, Ji Liu, Ce Zhang
2016	J	`jnl`	Are Lock-Free Concurrent Algorithms Practically Wait-Free? J. ACM Dan Alistarh, Keren Censor-Hillel, Nir Shavit
2016	B	`conf`	Lease/release: architectural support for scaling contended data structures. PPoPP Syed Kamran Haider, William Hasenplaugh, Dan Alistarh
2016	J	`jnl`	QSGD: Randomized Quantization for Communication-Optimal Stochastic Gradient Descent. CoRR Dan Alistarh, Jerry Li, Ryota Tomioka, Milan Vojnovic
2016	J	`jnl`	Time-Space Trade-offs in Population Protocols. CoRR Dan Alistarh, James Aspnes, David Eisenstat, Rati Gelashvili, Ronald L. Rivest
2016	J	`jnl`	ZipML: An End-to-end Bitwise Framework for Dense Generalized Linear Models. CoRR Hantian Zhang, Kaan Kara, Jerry Li, Dan Alistarh, Ji Liu, Ce Zhang
2015	A*	`conf`	A High-Radix, Low-Latency Optical Switch for Data Centers. SIGCOMM Dan Alistarh, Hitesh Ballani, Paolo Costa, Adam C. Funnell, Joshua Benjamin, Philip M. Watts, Benn Thomsen
2015	A*	`conf`	Fast and Exact Majority in Population Protocols. PODC Dan Alistarh, Rati Gelashvili, Milan Vojnovic
2015	A*	`conf`	How To Elect a Leader Faster than a Tournament. PODC Dan Alistarh, Rati Gelashvili, Adrian Vladu
2015	A	`conf`	Inherent Limitations of Hybrid Transactional Memory. DISC Dan Alistarh, Justin Kopinsky, Petr Kuznetsov, Srivatsan Ravi, Nir Shavit
2015	A*	`conf`	Lock-Free Algorithms under Stochastic Schedulers. PODC Dan Alistarh, Thomas Sauerwald, Milan Vojnovic
2015	J	`jnl`	Polylogarithmic-Time Leader Election in Population Protocols Using Polylogarithmic States. CoRR Dan Alistarh, Rati Gelashvili
2015	—	`conf`	Polylogarithmic-Time Leader Election in Population Protocols. ICALP (2) Dan Alistarh, Rati Gelashvili
2015	—	`conf`	Streaming Min-max Hypergraph Partitioning. NIPS Dan Alistarh, Jennifer Iglesias, Milan Vojnovic
2015	J	`jnl`	The Renaming Problem: Recent Developments and Open Questions. Bull. EATCS Dan Alistarh
2015	B	`conf`	The SprayList: a scalable relaxed priority queue. PPoPP Dan Alistarh, Justin Kopinsky, Jerry Li, Nir Shavit
2015	B	`conf`	ThreadScan: Automatic and Scalable Memory Reclamation. SPAA Dan Alistarh, William M. Leiserson, Alexander Matveev, Nir Shavit
2014	A*	`conf`	Are lock-free concurrent algorithms practically wait-free? STOC Dan Alistarh, Keren Censor-Hillel, Nir Shavit
2014	A*	`conf`	Balls-into-leaves: sub-logarithmic renaming in synchronous message-passing systems. PODC Dan Alistarh, Oksana Denysyuk, Luís E. T. Rodrigues, Nir Shavit
2014	A*	`conf`	Brief announcement: are lock-free concurrent algorithms practically wait-free? PODC Dan Alistarh, Keren Censor-Hillel, Nir Shavit
2014	A	`conf`	Communication-Efficient Randomized Consensus. DISC Dan Alistarh, James Aspnes, Valerie King, Jared Saia
2014	—	`ch.`	Distributed Algorithms. Computing Handbook, 3rd ed. (1) Dan Alistarh, Rachid Guerraoui
2014	A*	`conf`	Dynamic Task Allocation in Asynchronous Shared Memory. SODA Dan Alistarh, James Aspnes, Michael A. Bender, Rati Gelashvili, Seth Gilbert
2014	J	`jnl`	How to Elect a Leader Faster than a Tournament. CoRR Dan Alistarh, Rati Gelashvili, Adrian Vladu
2014	J	`jnl`	Inherent Limitations of Hybrid Transactional Memory. CoRR Dan Alistarh, Justin Kopinsky, Petr Kuznetsov, Srivatsan Ravi, Nir Shavit
2014	A	`conf`	StackTrack: an automated transactional approach to concurrent memory reclamation. EuroSys Dan Alistarh, Patrick Eugster, Maurice Herlihy, Alexander Matveev, Nir Shavit
2014	A	`conf`	The LevelArray: A Fast, Practical Long-Lived Renaming Algorithm. ICDCS Dan Alistarh, Justin Kopinsky, Alexander Matveev, Nir Shavit
2014	J	`jnl`	The LevelArray: A Fast, Practical Long-Lived Renaming Algorithm. CoRR Dan Alistarh, Justin Kopinsky, Alexander Matveev, Nir Shavit
2014	J	`jnl`	Tight Bounds for Asynchronous Renaming. J. ACM Dan Alistarh, James Aspnes, Keren Censor-Hillel, Seth Gilbert, Rachid Guerraoui
2013	J	`jnl`	Are Lock-Free Concurrent Algorithms Practically Wait-Free? CoRR Dan Alistarh, Keren Censor-Hillel, Nir Shavit
2013	A*	`conf`	Randomized loose renaming in PODC Dan Alistarh, James Aspnes, George Giakkoupis, Philipp Woelfel
2012	B	`conf`	Early Deciding Synchronous Renaming in O( logf ) Rounds or Less. SIROCCO Dan Alistarh, Hagit Attiya, Rachid Guerraoui, Corentin Travers
2012	J	`jnl`	Generating Fast Indulgent Algorithms. Theory Comput. Syst. Dan Alistarh, Seth Gilbert, Rachid Guerraoui, Corentin Travers
2012	A*	`conf`	How to Allocate Tasks Asynchronously. FOCS Dan Alistarh, Michael A. Bender, Seth Gilbert, Rachid Guerraoui
2012	J	`jnl`	Of Choices, Failures and Asynchrony: The Many Faces of Set Agreement. Algorithmica Dan Alistarh, Seth Gilbert, Rachid Guerraoui, Corentin Travers
2012	B	`conf`	On the cost of composing shared-memory algorithms. SPAA Dan Alistarh, Rachid Guerraoui, Petr Kuznetsov, Giuliano Losa
2012	—	`—`	Randomized versus Deterministic Implementations of Concurrent Data Structures. Dan Alistarh
2011	Misc	`conf`	Generating Fast Indulgent Algorithms. ICDCN Dan Alistarh, Seth Gilbert, Rachid Guerraoui, Corentin Travers
2011	A*	`conf`	Optimal-time adaptive strong renaming, with applications to counting. PODC Dan Alistarh, James Aspnes, Keren Censor-Hillel, Seth Gilbert, Morteza Zadimoghaddam
2011	A	`conf`	Sub-logarithmic Test-and-Set against a Weak Adversary. DISC Dan Alistarh, James Aspnes
2011	A*	`conf`	The Complexity of Renaming. FOCS Dan Alistarh, James Aspnes, Seth Gilbert, Rachid Guerraoui
2010	A	`conf`	Brief Announcement: New Bounds for Partially Synchronous Set Agreement. DISC Dan Alistarh, Seth Gilbert, Rachid Guerraoui, Corentin Travers
2010	A	`conf`	Fast Randomized Test-and-Set and Renaming. DISC Dan Alistarh, Hagit Attiya, Seth Gilbert, Andrei Giurgiu, Rachid Guerraoui
2010	—	`conf`	How Efficient Can Gossip Be? (On the Cost of Resilient Information Exchange). ICALP (2) Dan Alistarh, Seth Gilbert, Rachid Guerraoui, Morteza Zadimoghaddam
2010	B	`conf`	Securing every bit: authenticated broadcast in radio networks. SPAA Dan Alistarh, Seth Gilbert, Rachid Guerraoui, Zarko Milosevic, Calvin C. Newport
2009	B	`conf`	Of Choices, Failures and Asynchrony: The Many Faces of Set Agreement. ISAAC Dan Alistarh, Seth Gilbert, Rachid Guerraoui, Corentin Travers
2008	A	`conf`	How to Solve Consensus in the Smallest Window of Synchrony. DISC Dan Alistarh, Seth Gilbert, Rachid Guerraoui, Corentin Travers