Tolga Ergen

I am a Ph.D. student in the department of Electrical Engineering at Stanford University, where I am advised by Prof. Mert Pilanci. I received my B.S. and M.S. degrees in Electrical and Electronics Engineering from Bilkent University in 2016 and 2018, respectively, where I worked with Prof. Suleyman Serdar Kozat.

Email / CV / Google Scholar

Research

My research interests lie at intersection of convex optimization and deep learning. Currently, I focus on the understanding of neural network through the lens of convex optimization. Previously, I also developed efficient second order randomized algorithms to train neural networks.

Publications

	Demystifying Batch Normalization in ReLU Networks: Equivalent Convex Optimization Models and Implicit Regularization Tolga Ergen, Arda Sahiner, Batu Ozturkler, John Pauly, Morteza Mardani, Mert Pilanci PDF neural networks, convex analysis
	Implicit Convex Regularizers of CNN Architectures: Convex Optimization of Two- and Three-Layer Networks in Polynomial Time Tolga Ergen, Mert Pilanci ICLR 2021 (Spotlight Presentation) PDF convolutional neural networks, convex optimization, deep learning
	Vector-output ReLU Neural Network Problems are Copositive Programs: Convex Analysis of Two Layer Networks and Polynomial-time Algorithms Arda Sahiner, Tolga Ergen, John Pauly, Mert Pilanci ICLR 2021 PDF neural networks, convex analysis, non-convex optimization
	Convex Programs for Global Optimization of Convolutional Neural Networks in Polynomial-Time Tolga Ergen, Mert Pilanci NeurIPS 2020 Workshop on Optimization for Machine Learning (Oral Presentation) PDF convolutional neural networks, convex optimization, deep learning
	Neural Networks are Convex Regularizers: Exact Polynomial-time Convex Optimization Formulations for Two-layer Networks Mert Pilanci, Tolga Ergen ICML 2020 PDF neural networks, convex analysis, non-convex optimization
	Revealing the Structure of Deep Neural Networks via Convex Duality Tolga Ergen, Mert Pilanci arXiv deep neural networks, convex analysis, non-convex optimization
	Convex Geometry of Two-Layer ReLU Networks: Implicit Autoencoding and Interpretable Models Tolga Ergen, Mert Pilanci AISTATS 2020 PDF neural networks, convex analysis, non-convex optimization
	Exact and Relaxed Convex Formulations for Shallow Neural Autoregressive Models Vikul Gupta, Burak Bartan, Tolga Ergen, Mert Pilanci ICASSP 2021 PDF generative models, neural networks, convex optimization
	Convex Geometry and Duality of Over-parameterized Neural Networks Tolga Ergen, Mert Pilanci arXiv neural networks, convex analysis, non-convex optimization
	A Novel Distributed Anomaly Detection Algorithm Based on Support Vector Machines Tolga Ergen, Serdar Kozat Digital Signal Processing PDF support vector machines, distributed optimization
	Convex Duality and Cutting Plane Methods for Over-parameterized Neural Networks Tolga Ergen, Mert Pilanci NeurIPS 2019 Workshop on Optimization for Machine Learning PDF neural networks, convex analysis, non-convex optimization
	Random Projections for Learning Non-convex Models Tolga Ergen, Mert Pilanci NeurIPS 2019 Workshop on Beyond First Order Methods in Machine Learning PDF randomized algorithms, non-convex optimization
	Convex Optimization for Shallow Neural Networks Tolga Ergen, Mert Pilanci ALLERTON 2019 PDF neural networks, convex optimization
	Energy-Efficient LSTM Networks for Online Learning Tolga Ergen, Ali Mirza, Serdar Kozat IEEE TNNNLS PDF recurrent neural networks, online learning, non-convex optimization
	Unsupervised Anomaly Detection with LSTM Neural Networks Tolga Ergen, Serdar Kozat IEEE TNNLS PDF recurrent neural networks, support vector machines, non-convex optimization
	Team-Optimal Online Estimation of Dynamic Parameters over Distributed Tree Networks Fatih Kilic, Tolga Ergen, Muhammed Sayin, Serdar Kozat. Signal Processing PDF online learning, distributed optimization
	Online Training of LSTM Networks in Distributed Systems for Variable Length Data Sequences Tolga Ergen, Serdar Kozat IEEE TNNLS PDF recurrent neural networks, distributed optimization
	Efficient Online Learning Algorithms Based on LSTM Neural Networks Tolga Ergen, Serdar Kozat IEEE TNNLS PDF recurrent neural networks, online learning
	A Highly Efficient Recurrent Neural Network Architecture for Data Regression Tolga Ergen, Emir Ceyani IEEE SIU 2018 PDF recurrent neural networks, online learning
	A Novel Anomaly Detection Approach Based on Neural Networks Tolga Ergen, Mine Kerpicci IEEE SIU 2018 PDF neural networks, non-convex optimization
	Computationally Efficient Online Regression via LSTM Neural Networks Tolga Ergen, Serdar Kozat EUSIPCO 2017 PDF recurrent neural networks, online learning
	An Efficient Bandit Algorithm for General Weight Assignments Kaan Gokcesu, Tolga Ergen, Selami Ciftci, Serdar Kozat IEEE SIU 2017 PDF adversarial multi armed bandit, non-convex optimization
	Neural Networks Based Online Learning. Tolga Ergen, Serdar Kozat IEEE SIU 2017 PDF neural networks, online learning
	Novelty Detection Using Soft Partitioning and Hierarchical Models Tolga Ergen, Kaan Gokcesu, Mustafa Simsek, Serdar Kozat IEEE SIU 2017 PDF online learning, non-convex optimization
	Online Distributed Nonlinear Regression via Neural Networks Tolga Ergen, Serdar Kozat IEEE SIU 2017 PDF neural networks, distributed optimization

Teaching

	(Fall 2019, 2020, 2021) EE269, Signal Processing for Machine Learning, TA (Winter 2020, 2021) EE270, Large Scale Matrix Computation, Optimization and Learning, TA (Spring 2020, 2021) EE364B, Convex Optimization II, TA
	(Fall 2016, 2017, Spring 2018) EEE424, Digital Signal Processing, TA (Spring 2018) EEE102, Introduction to Digital Circuit Design, TA

Academic Service

Reviewer for NeurIPS, ICML, IEEE TNNLS, IEEE SPL

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