Andrej Risteski
48 papers · 2015–2025 · 8 conferences · across top CS/AI conferences
Achievements
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🌍 Conference Polyglot (8) 🐣 Hot Topic Early Bird 🧭 Keyword Pioneer 🌉 Interdisciplinary Bridge 🏃 Academic Marathon (10)
🌍
Conference Polyglot
(8)
🏃
Academic Marathon
(10)
🐣
Hot Topic Early Bird
🏆
Keyword Champion
(3)
👑
Triple Crown
🗃️
Keyword Collector
(142)
⚡
Prolific Year
(8)
🚀
Conference Pioneer
💎
Century Club
(48)
🔥
Unstoppable
(11)
📈
Trend Setter
❓
The Questioner
Conferences
NIPS (14)
ICLR (13)
ICML (9)
COLT (6)
AISTATS (3)
ACL (1)
ALT (1)
IJCNLP (1)
Top co-authors
Keywords
variational inference
(6)
representation learning
(4)
gradient descent
(4)
representational power
(3)
ising model
(3)
latent variable model
(3)
neural network
(3)
partial differential equation
(3)
partition function
(3)
online learning
(3)
generative model
(2)
domain generalization
(2)
constituency parsing
(2)
wasserstein distance
(2)
exponential family
(2)
curse of dimensionality
(2)
markov chain monte carlo
(2)
self-supervised learning
(2)
unsupervised learning
(2)
unsupervised parsing
(2)
Papers
Towards characterizing the value of edge embeddings in Graph Neural Networks
ICML 2025
Progressive distillation induces an implicit curriculum
ICLR 2025
On the Benefits of Memory for Modeling Time-Dependent PDEs
ICLR 2025
On the Query Complexity of Verifier-Assisted Language Generation
ICML 2025
Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation and Regression
ICLR 2024
Promises and Pitfalls of Generative Masked Language Modeling: Theoretical Framework and Practical Guidelines
ICML 2024
Outliers with Opposing Signals Have an Outsized Effect on Neural Network Optimization
ICLR 2024
Fit Like You Sample: Sample-Efficient Generalized Score Matching from Fast Mixing Diffusions
COLT 2024
Provable benefits of annealing for estimating normalizing constants: Importance Sampling, Noise-Contrastive Estimation, and beyond
NIPS 2023
Provable benefits of score matching
NIPS 2023
How Do Transformers Learn Topic Structure: Towards a Mechanistic Understanding
ICML 2023
Pitfalls of Gaussians as a noise distribution in NCE
ICLR 2023
Statistical Efficiency of Score Matching: The View from Isoperimetry
ICLR 2023
Deep Equilibrium Based Neural Operators for Steady-State PDEs
NIPS 2023
Neural Network Approximations of PDEs Beyond Linearity: A Representational Perspective
ICML 2023
Transformers are uninterpretable with myopic methods: a case study with bounded Dyck grammars
NIPS 2023
Masked Prediction: A Parameter Identifiability View
NIPS 2022
Iterative Feature Matching: Toward Provable Domain Generalization with Logarithmic Environments
NIPS 2022
Continual learning: a feature extraction formalization, an efficient algorithm, and fundamental obstructions
NIPS 2022
Sampling Approximately Low-Rank Ising Models: MCMC meets Variational Methods
COLT 2022
An Online Learning Approach to Interpolation and Extrapolation in Domain Generalization
AISTATS 2022
Contrasting the landscape of contrastive and non-contrastive learning
AISTATS 2022
The Effects of Invertibility on the Representational Complexity of Encoders in Variational Autoencoders
ICLR 2022
Variational autoencoders in the presence of low-dimensional data: landscape and implicit bias
ICLR 2022
Analyzing and Improving the Optimization Landscape of Noise-Contrastive Estimation
ICLR 2022
The Limitations of Limited Context for Constituency Parsing
IJCNLP 2021
Universal Approximation Using Well-Conditioned Normalizing Flows
NIPS 2021
Parametric Complexity Bounds for Approximating PDEs with Neural Networks
NIPS 2021
The Limitations of Limited Context for Constituency Parsing
ACL 2021
Contrastive learning of strong-mixing continuous-time stochastic processes
AISTATS 2021
Efficient sampling from the Bingham distribution
ALT 2021
The Risks of Invariant Risk Minimization
ICLR 2021
Representational aspects of depth and conditioning in normalizing flows
ICML 2021
On Learning Language-Invariant Representations for Universal Machine Translation
ICML 2020
Empirical Study of the Benefits of Overparameterization in Learning Latent Variable Models
ICML 2020
Approximability of Discriminators Implies Diversity in GANs
ICLR 2019
Sum-of-squares meets square loss: Fast rates for agnostic tensor completion
COLT 2019
The Comparative Power of ReLU Networks and Polynomial Kernels in the Presence of Sparse Latent Structure
ICLR 2019
Beyond Log-concavity: Provable Guarantees for Sampling Multi-modal Distributions using Simulated Tempering Langevin Monte Carlo
NIPS 2018
Do GANs learn the distribution? Some Theory and Empirics
ICLR 2018
On the Ability of Neural Nets to Express Distributions
COLT 2017
How to calculate partition functions using convex programming hierarchies: provable bounds for variational methods
COLT 2016
Approximate maximum entropy principles via Goemans-Williamson with applications to provable variational methods
NIPS 2016
Recovery guarantee of weighted low-rank approximation via alternating minimization
ICML 2016
Recovery Guarantee of Non-negative Matrix Factorization via Alternating Updates
NIPS 2016
Algorithms and matching lower bounds for approximately-convex optimization
NIPS 2016
Label optimal regret bounds for online local learning
COLT 2015
On some provably correct cases of variational inference for topic models
NIPS 2015