# About me

Welcome to my site. My name is Grigoris and I am an Assistant Professor in University of Wisconsin-Madison.

My research focuses on reliable machine learning and the design/study of expressive models that are robust to noise and generalize well in out-of-distribution data. Concretely,

- I have worked extensively on polynomial networks (PNs) that capture high-degree interactions between inputs. My short-term goals are to understand the inductive bias and properties of existing architectures through empirical and theoretical studies. I am interested in the complete theoretical understanding of (neural/polynomial) networks, including their expressivity, trainability, generalization properties, and inductive biases. My recent work has provided the first characterization of the generalization of this class of functions, and I have focused on the spectral bias of high-degree polynomials, highlighting how PNs can learn higher frequency functions faster than regular feed-forward networks.
- My goal is to understand the extrapolation properties of existing networks and make improvements to their performance, especially in the context of conditional generative models. In the short-term, I will continue to explore the robustness of these models to malicious attacks, as well as the impact of adversarial perturbations on different classes. In the long-term, I plan to design models that are both robust and fair, and can generalize well to unseen attribute combinations.

## News

- May 2024: The following papers are accepted at
**ICML 2024**(more information soon): - April 2024: We are organizing a tutorial titled 'Scaling and Reliability Foundations in Machine Learning' in conjunction with ISIT 2024 on July.
- January 2024: The following papers are accepted at
**ICLR 2024**: - January 2024: The following paper has been accepted at
**Transactions on Machine Learning Research (TMLR)**: '*PNeRV: A Polynomial Neural Representation for Videos*'. - November 2023: I was recognized as a
**top reviewer**at**NeurIPS 2023**. - October 2023: The following papers are accepted at
**NeurIPS 2023**: `Maximum Independent Set: Self-Training through Dynamic Programming' and `On the Convergence of Encoder-Only Shallow Transformers'. - June 2023: The slides and the recording of our tutorial titled `Deep Learning Theory for Vision' at CVPR'23 are available: Slides and recording. More information: https://dl-theory.github.io/.
- May 2023: The following paper has been accepted at
**Transactions on Machine Learning Research (TMLR)**: `Federated Learning under Covariate Shifts with Generalization Guarantees'. - April 2023: The following paper has been accepted at
**ICML 2023**: `Benign Overfitting in Deep Neural Networks under Lazy Training'. - April 2023: Awarded the DAAD AInet Fellowship, which is awarded to outstanding early career researchers. Topic: generative models in ML.
- March 2023: The following paper has been accepted at
**CVPR 2023**: '*Regularization of polynomial networks for image recognition*'. - February 2023: Organizer of the tutorial on 'Polynomial Nets' in conjunction with AAAI'23: https://polynomial-nets.github.io/.
- January 2023: The following paper has been accepted at
**Transactions on Machine Learning Research (TMLR)**: '*Revisiting adversarial training for the worst-performing class*'. - December 2022: The following paper has been accepted at
**Transactions on Pattern Analysis and Machine Intelligence**: '*Linear Complexity Self-Attention with 3rd Order Polynomials*'. - October 2022: I was recognized as a
**best reviewer**at**NeurIPS 2022**. - September 2022: The following papers have been accepted at
**NeurIPS 2022**:- 'Robustness in deep learning: The good (width), the bad (depth), and the ugly (initialization)',
- 'Generalization Properties of NAS under Activation and Skip Connection Search',
- 'Sound and Complete Verification of Polynomial Networks',
- 'Extrapolation and Spectral Bias of Neural Nets with Hadamard Product: a Polynomial Net Study'.

- August 2022: The slides used in the tutorial on polynomial networks (organized at CVPR'22) have been released.
- July 2022: I was awarded a
**best reviewer award (top 10%)**at**ICML 2022**. - July 2022: The following papers have been accepted at
**ECCV 2022**: 'Augmenting Deep Classifiers with Polynomial Neural Networks' and 'MimicME: A Large Scale Diverse 4D Database for Facial Expression Analysis'. More information soon. - June 2022: Organizer of the tutorial on 'Polynomial Nets' in conjunction with CVPR'22: https://polynomial-nets.github.io/previous_versions/index.html.
- April 2022: I was awarded a
**highlighted reviewer award**at**ICLR 2022**. - March 2022: The following paper has been accepted at
**CVPR 2022**: '*Cluster-guided Image Synthesis with Unconditional Models*'. - February 2022:
**My talk**on polynomial networks at the UCL Centre for Artificial Intelligence has been uploaded online. - January 2022: The following papers have been accepted at
**ICLR 2022**: '*Controlling the Complexity and Lipschitz Constant improves Polynomial Nets*' and '*The Spectral Bias of Polynomial Neural Networks*'. - October 2021: The following paper has been accepted at
**NeurIPS 2021**: 'Conditional Generation Using Polynomial Expansions'. - July 2021: I was awarded a
**best reviewer award (top 10%)**at**ICML 2021**.