Benchmarking the Robustness of Image Watermarks

B. An*, M. Ding*, T. Rabbani* et al., 2024

We systematically reveal weaknesses in modern image-based watermarking protocols, including those of a generative variety. Check out our benchmark and toolkit at wavesbench.github.io/.

Large-Scale Distributed Learning via Private On-Device LSH

T. Rabbani*, M. Bornstein*, F. Huang. NeurIPS, 2023.

Using a new family of hash functions, we develop one of the first private, personalized, and memory-efficient on-device LSH frameworks for training recommender DNNs on extreme multi-label datasets.

SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communication

M. Bornstein, T. Rabbani*, AS Bedhi, F. Huang. ICLR, 2023.

We enable wait-free model training for peer-to-peer FL using model caching. Our algorithm provable convergence at a SotA rate and empirically significantly speeds up global model convergence.

Sketch-GNN: Scalable Graph Neural Networks with Sublinear Training Complexity

M. Ding, T. Rabbani, B. An, EZ Wang, F. Huang. NeurIPS, 2022.

This paper proposes a sketch-based algorithm whose training time and memory grow sublinearly with respect to graph size by training GNNs atop a few compact sketches of graph adjacency and node embeddings.

Constructions of difference sets in nonabelian 2-groups

A. Applebaum, J. Clikeman, J. Davis, J. Dillon, J. Jedwab, T. Rabbani, K. Smith, W. Yolland. Algebra & Number Theory, 2023.

We determine that all groups of order 256 not excluded by the two classical nonexistence criteria contain a difference set, resolving a 25 year old question posed by John Dillon.

Practical and Fast Momentum-Based Power Methods

T. Rabbani*, A. Jain, A. Rajkumar, F. Huang. PMLR, 2022.

We provide a pair of novel momentum-based power methods, DMPower and a streaming variant, DMStream. In contrast with prior art, these accelerated methods do not depend on spectral knowledge.

Fast GPU Convolution for CP-Decomposed Tensorial Neural Networks

T. Rabbani*, A. Reustle*, F. Huang. IntelliSys, 2022.

We present a GPU algorithm for performing convolution with decomposed tensor products. We experimentally find up to 4.85x faster execution times than cuDNN for some tensors.

Nonabelian Orthogonal Building Sets

T. Rabbani*, K. Smith. Proceedings of the 14th International Conference on Finite Fields and their Applications, 2022.

We examine recent construction techniques of Hadamard difference sets in 2-groups and an extension of orthogonal building sets to nonabelian groups.

Unique minimal forcing sets and forced representation of integers by quadratic forms

T. Rabbani*. Rose-Hulman Undergraduate Mathematics Journal, 2016.

We use Bhargava’s theory of escalators to establish several infinite familes of positive integers, interpreted as singletons in N, without unique minimal forcing sets in T.

Improving the error-correcting code used in 3-G communication

T. Rabbani*. SIURO, 2015.

In 2011, Samsung Electronics Co. filed a complaint against Apple Inc. for alleged infringement of patents described in US 7706348, including a [30,10,10] code. We give a construction of an even better [30,10,11] non-cyclic code, which is distinct from the conventional BCH construction.

Fast Evaluation of Multilinear Operations in Convolutional Tensorial Neural Networks

T. Rabbani*, J. Su*, X. Liu, D. Chan, G. Sangston, F. Huang. Third Workshop on Seeking Low‑Dimensionality in Deep Neural Networks, 2023.

Faster Hyperparameter Search for GNNs via Calibrated Dataset Condensation

M. Ding*, Y. Xu, T. Rabbani, X. Liu, T. Ranadive, TC Tuan, F. Huang. New Frontiers in Graph Learning, 2023.

Comfetch: Federated Learning of Large Networks on Constrained Clients via Sketching

T. Rabbani*, B. Feng*, M. Bornstein, K. Sang, Y. Yang, A. Rajkumar, A. Varshney, F. Huang. International Workshop on Trustable, Verifiable and Auditable Federated Learning in Conjunction with AAAI, 2022.