Shifting network tomography toward a practical goal. - Denisa Ghita; Can Karakus; Katerina J. Argyraki; Patrick Thiran (CoNEXT, 2011)

An achievable rate region for Gaussian interference channel with intermittent feedback. - Can Karakus; I-Hsiang Wang; Suhas N. Diggavi (Allerton, 2013)

Interference channel with intermittent feedback. - Can Karakus; I-Hsiang Wang; Suhas N. Diggavi (ISIT, 2013)

Rate splitting is approximately optimal for fading Gaussian interference channels. - Joyson Sebastian; Can Karakus; Suhas N. Diggavi; I-Hsiang Wang (Allerton, 2015)

Opportunistic scheduling for full-duplex uplink-downlink networks. - Can Karakus; Suhas N. Diggavi (ISIT, 2015)

Gaussian Interference Channel With Intermittent Feedback. - Can Karakus; I-Hsiang Wang; Suhas N. Diggavi (IEEE Trans. Inf. Theory, 2015)

Approximately achieving the feedback interference channel capacity with point-to-point codes. - Joyson Sebastian; Can Karakus; Suhas N. Diggavi (ISIT, 2016)

Encoded distributed optimization. - Can Karakus; Yifan Sun 001; Suhas N. Diggavi (ISIT, 2017)

Straggler Mitigation in Distributed Optimization Through Data Encoding. - Can Karakus; Yifan Sun 001; Suhas N. Diggavi; Wotao Yin (NIPS, 2017)

Approximate capacity of Fading Gaussian Interference Channels with Point-to-Point codes. - Joyson Sebastian; Can Karakus; Suhas N. Diggavi (CoRR, 2017)

Enhancing Multiuser MIMO Through Opportunistic D2D Cooperation. - Can Karakus; Suhas N. Diggavi (IEEE Trans. Wirel. Commun., 2017)

Privacy-Utility Trade-off of Linear Regression under Random Projections and Additive Noise. - Mehrdad Showkatbakhsh; Can Karakus; Suhas N. Diggavi (ISIT, 2018)

Approximate Capacity of Fast Fading Interference Channels With no Instantaneous CSIT. - Joyson Sebastian; Can Karakus; Suhas N. Diggavi (IEEE Trans. Commun., 2018)

Robust Communication and Optimization over Dynamic Networks. - Can Karakus (2018)

Densifying Assumed-Sparse Tensors - Improving Memory Efficiency and MPI Collective Performance During Tensor Accumulation for Parallelized Training of Neural Machine Translation Models. - Derya Cavdar; Valeriu Codreanu; Can Karakus; John A. Lockman III; Damian Podareanu; Vikram A. Saletore; Alexander Sergeev; Don D. Smith II; Victor Suthichai; Quy Ta; Srinivas Varadharajan; Lucas A. Wilson; et al. (ISC, 2019)

Differentially Private Consensus-Based Distributed Optimization. - Mehrdad Showkatbakhsh; Can Karakus; Suhas N. Diggavi (CoRR, 2019)

Redundancy Techniques for Straggler Mitigation in Distributed Optimization and Learning. - Can Karakus; Yifan Sun 001; Suhas N. Diggavi; Wotao Yin (J. Mach. Learn. Res., 2019)

Herring: rethinking the parameter server at scale for the cloud. - Indu Thangakrishnan; Derya Cavdar; Can Karakus; Piyush Ghai; Yauheni Selivonchyk; Cory Pruce (SC, 2020)

Qsparse-Local-SGD: Distributed SGD With Quantization, Sparsification, and Local Computations. - Debraj Basu 001; Deepesh Data; Can Karakus; Suhas N. Diggavi (IEEE J. Sel. Areas Inf. Theory, 2020)

Amazon SageMaker Model Parallelism: A General and Flexible Framework for Large Model Training. - Can Karakus; Rahul Huilgol; Fei Wu; Anirudh Subramanian; Cade Daniel; Derya Çavdar; Teng Xu 010; Haohan Chen; Arash Rahnama; Luis Quintela (CoRR, 2021)

MADA: Meta-Adaptive Optimizers Through Hyper-Gradient Descent. - Kaan Ozkara; Can Karakus; Parameswaran Raman; Mingyi Hong 001; Shoham Sabach; Branislav Kveton; Volkan Cevher (ICML, 2024)

Marconi: Prefix Caching for the Era of Hybrid LLMs. - Rui Pan 003; Zhuang Wang; Zhen Jia; Can Karakus; Luca Zancato; Tri Dao; Ravi Netravali; Yida Wang (CoRR, 2024)