| 2026 | COLT | Steering diffusion models with quadratic rewards: a fine-grained analysis. | Ankur Moitra, Andrej Risteski, Dhruv Rohatgi |
| 2025 | ICLR | Progressive distillation induces an implicit curriculum. | Abhishek Panigrahi, Bingbin Liu, Sadhika Malladi, Andrej Risteski, Surbhi Goel |
| 2025 | ICLR | On the Benefits of Memory for Modeling Time-Dependent PDEs. | Ricardo Buitrago Ruiz, Tanya Marwah, Albert Gu, Andrej Risteski |
| 2025 | ICML | On the Query Complexity of Verifier-Assisted Language Generation. | Edoardo Botta, Yuchen Li, Aashay Mehta, Jordan T. Ash, Cyril Zhang, Andrej Risteski |
| 2025 | ICML | Towards characterizing the value of edge embeddings in Graph Neural Networks. | Dhruv Rohatgi, Tanya Marwah, Zachary Chase Lipton, Jianfeng Lu, Ankur Moitra, Andrej Risteski |
| 2024 | COLT | Fit Like You Sample: Sample-Efficient Generalized Score Matching from Fast Mixing Diffusions. | Yilong Qin, Andrej Risteski |
| 2024 | ICLR | Outliers with Opposing Signals Have an Outsized Effect on Neural Network Optimization. | Elan Rosenfeld, Andrej Risteski |
| 2024 | ICLR | Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation and Regression. | Runtian Zhai, Bingbin Liu, Andrej Risteski, J. Zico Kolter, Pradeep Kumar Ravikumar |
| 2024 | ICML | Promises and Pitfalls of Generative Masked Language Modeling: Theoretical Framework and Practical Guidelines. | Yuchen Li, Alexandre Kirchmeyer, Aashay Mehta, Yilong Qin, Boris Dadachev, Kishore Papineni, Sanjiv Kumar, Andrej Risteski |
| 2023 | ICLR | Statistical Efficiency of Score Matching: The View from Isoperimetry. | Frederic Koehler, Alexander Heckett, Andrej Risteski |
| 2023 | ICLR | Pitfalls of Gaussians as a noise distribution in NCE. | Holden Lee, Chirag Pabbaraju, Anish Prasad Sevekari, Andrej Risteski |
| 2023 | ICML | How Do Transformers Learn Topic Structure: Towards a Mechanistic Understanding. | Yuchen Li, Yuanzhi Li, Andrej Risteski |
| 2023 | ICML | Neural Network Approximations of PDEs Beyond Linearity: A Representational Perspective. | Tanya Marwah, Zachary Chase Lipton, Jianfeng Lu, Andrej Risteski |
| 2022 | AISTATS | Contrasting the landscape of contrastive and non-contrastive learning. | Ashwini Pokle, Jinjin Tian, Yuchen Li, Andrej Risteski |
| 2022 | AISTATS | An Online Learning Approach to Interpolation and Extrapolation in Domain Generalization. | Elan Rosenfeld, Pradeep Ravikumar, Andrej Risteski |
| 2022 | COLT | Sampling Approximately Low-Rank Ising Models: MCMC meets Variational Methods. | Frederic Koehler, Holden Lee, Andrej Risteski |
| 2022 | ICLR | Variational autoencoders in the presence of low-dimensional data: landscape and implicit bias. | Frederic Koehler, Viraj Mehta, Chenghui Zhou, Andrej Risteski |
| 2022 | ICLR | Analyzing and Improving the Optimization Landscape of Noise-Contrastive Estimation. | Bingbin Liu, Elan Rosenfeld, Pradeep Kumar Ravikumar, Andrej Risteski |
| 2022 | ICLR | The Effects of Invertibility on the Representational Complexity of Encoders in Variational Autoencoders. | Divyansh Pareek, Andrej Risteski |
| 2021 | ACL | The Limitations of Limited Context for Constituency Parsing. | Yuchen Li, Andrej Risteski |
| 2021 | AISTATS | Contrastive learning of strong-mixing continuous-time stochastic processes. | Bingbin Liu, Pradeep Ravikumar, Andrej Risteski |
| 2021 | ALT | Efficient sampling from the Bingham distribution. | Rong Ge, Holden Lee, Jianfeng Lu, Andrej Risteski |
| 2021 | ICLR | The Risks of Invariant Risk Minimization. | Elan Rosenfeld, Pradeep Kumar Ravikumar, Andrej Risteski |
| 2021 | ICML | Representational aspects of depth and conditioning in normalizing flows. | Frederic Koehler, Viraj Mehta, Andrej Risteski |
| 2020 | ICML | On Learning Language-Invariant Representations for Universal Machine Translation. | Han Zhao, Junjie Hu, Andrej Risteski |
| 2020 | ICML | Empirical Study of the Benefits of Overparameterization in Learning Latent Variable Models. | Rares-Darius Buhai, Yoni Halpern, Yoon Kim, Andrej Risteski, David A. Sontag |
| 2019 | COLT | Sum-of-squares meets square loss: Fast rates for agnostic tensor completion. | Dylan J. Foster, Andrej Risteski |
| 2019 | ICLR | Approximability of Discriminators Implies Diversity in GANs. | Yu Bai, Tengyu Ma, Andrej Risteski |
| 2019 | ICLR | The Comparative Power of ReLU Networks and Polynomial Kernels in the Presence of Sparse Latent Structure. | Frederic Koehler, Andrej Risteski |
| 2019 | STOC | Mean-field approximation, convex hierarchies, and the optimality of correlation rounding: a unified perspective. | Vishesh Jain, Frederic Koehler, Andrej Risteski |
| 2018 | ICLR | Do GANs learn the distribution? Some Theory and Empirics. | Sanjeev Arora, Andrej Risteski, Yi Zhang |
| 2017 | COLT | On the Ability of Neural Nets to Express Distributions. | Holden Lee, Rong Ge, Tengyu Ma, Andrej Risteski, Sanjeev Arora |
| 2017 | STOC | Provable learning of noisy-OR networks. | Sanjeev Arora, Rong Ge, Tengyu Ma, Andrej Risteski |
| 2016 | COLT | How to calculate partition functions using convex programming hierarchies: provable bounds for variational methods. | Andrej Risteski |
| 2016 | ICML | Recovery guarantee of weighted low-rank approximation via alternating minimization. | Yuanzhi Li, Yingyu Liang, Andrej Risteski |
| 2015 | COLT | Label optimal regret bounds for online local learning. | Pranjal Awasthi, Moses Charikar, Kevin A. Lai, Andrej Risteski |