34 citations · 60 across the 13 of their papers we have counts for
17 papers
KForge: LLM-Driven Cross-Platform Kernel Generation for AI Accelerators
Taras Sereda, Burak Bartan, Ankita Nayak +3
Production inference increasingly targets a heterogeneous mix of accelerators. Agentic pipelines interleave reasoning, tool calls, and multi-agent coordination, each with distinct…
KForge: Program Synthesis for Diverse AI Hardware Accelerators
Taras Sereda, Tom St. John, Burak Bartan +3
GPU kernels are critical for ML performance but difficult to optimize across diverse accelerators. We present KForge, a platform-agnostic framework built on two collaborative LLM-b…
Randomized Polar Codes for Anytime Distributed Machine Learning
Burak Bartan, Mert Pilanci
We present a novel distributed computing framework that is robust to slow compute nodes, and is capable of both approximate and exact computation of linear operations. The proposed…
Moccasin: Efficient Tensor Rematerialization for Neural Networks
Burak Bartan, Haoming Li, Harris Teague +2
The deployment and training of neural networks on edge computing devices pose many challenges. The low memory nature of edge devices is often one of the biggest limiting factors en…
Distributed Sketching for Randomized Optimization: Exact Characterization, Concentration and Lower Bounds
Burak Bartan, Mert Pilanci
We consider distributed optimization methods for problems where forming the Hessian is computationally challenging and communication is a significant bottleneck. We leverage random…
Hidden Convexity of Wasserstein GANs: Interpretable Generative Models with Closed-Form Solutions
Arda Sahiner, Tolga Ergen, Batu Ozturkler +4
Generative Adversarial Networks (GANs) are commonly used for modeling complex distributions of data. Both the generators and discriminators of GANs are often modeled by neural netw…
Training Quantized Neural Networks to Global Optimality via Semidefinite Programming
Burak Bartan, Mert Pilanci
Neural networks (NNs) have been extremely successful across many tasks in machine learning. Quantization of NN weights has become an important topic due to its impact on their ener…
Neural Spectrahedra and Semidefinite Lifts: Global Convex Optimization of Polynomial Activation Neural Networks in Fully Polynomial-Time
Burak Bartan, Mert Pilanci
The training of two-layer neural networks with nonlinear activation functions is an important non-convex optimization problem with numerous applications and promising performance i…
Debiasing Distributed Second Order Optimization with Surrogate Sketching and Scaled Regularization
Michał Dereziński, Burak Bartan, Mert Pilanci +1
In distributed second order optimization, a standard strategy is to average many local estimates, each of which is based on a small sketch or batch of the data. However, the local…
Distributed Averaging Methods for Randomized Second Order Optimization
Burak Bartan, Mert Pilanci
We consider distributed optimization problems where forming the Hessian is computationally challenging and communication is a significant bottleneck. We develop unbiased parameter…
Distributed Sketching Methods for Privacy Preserving Regression
Burak Bartan, Mert Pilanci
In this work, we study distributed sketching methods for large scale regression problems. We leverage multiple randomized sketches for reducing the problem dimensions as well as pr…
Distributed Black-Box Optimization via Error Correcting Codes
Burak Bartan, Mert Pilanci
We introduce a novel distributed derivative-free optimization framework that is resilient to stragglers. The proposed method employs coded search directions at which the objective…
Straggler Resilient Serverless Computing Based on Polar Codes
Burak Bartan, Mert Pilanci
We propose a serverless computing mechanism for distributed computation based on polar codes. Serverless computing is an emerging cloud based computation model that lets users run…
Convex Relaxations of Convolutional Neural Nets
Burak Bartan, Mert Pilanci
We propose convex relaxations for convolutional neural nets with one hidden layer where the output weights are fixed. For convex activation functions such as rectified linear units…
Discrete Scaling Based on Operator Theory
Aykut Koç, Burak Bartan, Haldun M. Ozaktas
Signal scaling is a fundamental operation of practical importance in which a signal is enlarged or shrunk in the coordinate direction(s). Scaling or magnification is not trivial fo…
Discrete Linear Canonical Transform Based on Hyperdifferential Operators
Aykut Koç, Burak Bartan, Haldun M. Ozaktas
Linear canonical transforms (LCTs) are of importance in many areas of science and engineering with many applications. Therefore a satisfactory discrete implementation is of conside…
Repairing Multiple Failures for Scalar MDS Codes
Jay Mardia, Burak Bartan, Mary Wootters
In distributed storage, erasure codes -- like Reed-Solomon Codes -- are often employed to provide reliability. In this setting, it is desirable to be able to repair one or more fai…