Yury Polyanskiy
Leverett Howell Cutten ’07 And William King Cutten ’39 Professor
Studies information theory, statistical machine learning, error-correcting codes, and wireless communication.
Yury Polyanskiy's lab at MIT works on signal processing & information theory and learning theory & optimization. 41 papers since 2023, 3 at top venues, mostly ICASSP and JMLR. 36 alumni. Applications close .
At a glance
- papers, 2023–2026
- 11/ year
- at T1 venues
- 7%
- PhD students
- 0
- MS students
- 0
- alumni listed
- 36
Papers by yearfrom the lab homepage, 2023–2026
- 4 papers2026 · T1 0 · collecting
- 14 papers2025 · T1 1
- 6 papers2024 · T1 1
- 17 papers2023 · T1 1
T1 venuesOtherVenues: ICASSP, JMLR
Research focuspaper titles that use each topic's words
- Learning theory & optimization5 papers
- Signal processing & information theory4 papers
- codes0 papers
- wireless communication0 papers
- studies information0 papers
Paper list
From the lab homepage.
20264 papers
- Proc. Int. Conf. Learning Representations (ICLR- ), Apr.Critical attention scaling in long-context transformers (opens in a new tab)
- Proc. Conf. on Learning Theory (COLT- )Price of metric universality in vector quantization is at most 0.11 bit (opens in a new tab)
- Proc. Conf. on Learning Theory (COLT- )Universal priors: Solving empirical Bayes via Bayesian inference and pretraining (opens in a new tab)
- Proc. Int. Conf. Machine Learning (ICML- ), Spotlight Jul. .WaterSIC: Information-theoretically (near) optimal linear layer quantization (opens in a new tab)
202514 papers
- Bull. AMSA mathematical perspective on Transformers (opens in a new tab)
- JMLR (T1)Density estimation using the perceptron (opens in a new tab)
- Annals of Statistics, to appearDualizing Le Cam's method for functional estimation I: General theory (opens in a new tab)
- Electronic Journal of ProbabilityEntropy contractions in Markov chains: Half-step, full-step and continuous-time (opens in a new tab)
- Proc. Adv. in Neural Inform. Proc. Syst. (NeurIPS- ), Dec.Global minimizers of sigmoid contrastive loss (opens in a new tab)
- MOSS workshop, ICML- , Jul.Learning Gaussian mixture models via Transformer measure flows (opens in a new tab)
- Proc. Int. Conf. Machine Learning (ICML- ), Jul.NestQuant: Nested lattice quantization for matrix products and LLMs (opens in a new tab)
- Proc. Adv. in Neural Inform. Proc. Syst. (NeurIPS- ), Dec.Normalization in attention dynamics (opens in a new tab)
- Proc. Conf. on Learning Theory (COLT- )On the minimax regret of sequential probability assignment via square-root entropy (opens in a new tab)
- Information and Inference: A Journal of the IMAOptimal empirical Bayes estimation for the Poisson model via minimum-distance methods (opens in a new tab)
- IEEE Int. Symp. Inf. Theory (ISIT), Ann Arbor, USA, Jun.Optimal quantization for matrix multiplication (opens in a new tab)
- Proc. Conf. on Learning Theory (COLT- )Partial and exact recovery of a random hypergraph from its graph projection (opens in a new tab)
20246 papers
- Proc. Adv. in Neural Inform. Proc. Syst. (NeurIPS- ), Dec.Clustering in causal attention masking (opens in a new tab)
- IEEE Trans. Inf. TheoryIsing model on locally tree-like graphs: uniqueness of solutions to cavity equations (opens in a new tab)
- IEEE Trans. Inf. TheoryLikelihood-free hypothesis testing (opens in a new tab)
- ICASSP (T1)The data-driven radio frequency signal separation challenge (opens in a new tab)
- Proc. Conf. on Learning Theory (COLT- ), Jul.Thresholds for reconstruction of random hypergraphs from graph projections (opens in a new tab)
- Proc. IEEEUnsourced multiple access: a coding paradigm for massive random access (opens in a new tab)
202317 papers
- Proc. Symp. Found. Comp. Sci (FOCS- ), Nov.Algorithmic decorrelation and planted clique in dependent random graphs: the case of extra triangles (opens in a new tab)
- IEEE Trans. Inf. TheoryCapacity of noisy permutation channels (opens in a new tab)
- IEEE Comm. Theory Workshop (CTW- ), Taiwan, Jul.Coded orthogonal modulation for the multi-antenna multiple-access channel (opens in a new tab)
- IEEE Int. Symp. Inf. Theory (ISIT), Taipei, Taiwan, Jun.Comparing Poisson and Gaussian channels (extended) (opens in a new tab)
- Proc. Conf. on Learning Theory (COLT- ), Jul.Empirical Bayes via ERM and Rademacher complexities: the Poisson model (opens in a new tab)
- Proc. Conf. on Learning Theory (COLT- ), Jul.Entropic characterization of optimal rates for learning Gaussian mixtures (opens in a new tab)
- Proc. Adv. in Neural Inform. Proc. Syst. (NeurIPS- ), Dec.Kernel-based tests for likelihood-free hypothesis testing (opens in a new tab)
- Proc. Conf. on Learning Theory (COLT- ), Jul.Minimax optimal testing by classification (opens in a new tab)
- Commun. Math. Phys.Non-linear log-Sobolev inequalities for the Potts semigroup and applications to reconstruction problems (opens in a new tab)
- ICASSP (T1)On neural architectures for deep learning-based source separation of co-channel OFDM signals (opens in a new tab)
- IEEE Int. Symp. Inf. Theory (ISIT), Taipei, Taiwan, Jun.On the advantages of asynchrony in the unsourced MAC (opens in a new tab)
- Proc. Adv. in Neural Inform. Proc. Syst. (NeurIPS- ), Dec.Score-based source separation with applications to digital communication signals (opens in a new tab)
Where alumni went36 listed
Apply to MITFall 2027
MIT EECS Computer Science PhD
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