9 citations · 20 across the 8 of their papers we have counts for
8 papers
BanditPAM++: Faster -medoids Clustering
Mo Tiwari, Ryan Kang, Donghyun Lee +4
Clustering is a fundamental task in data science with wide-ranging applications. In -medoids clustering, cluster centers must be actual datapoints and arbitrary distance metrics…
Accelerating Machine Learning Algorithms with Adaptive Sampling
Mo Tiwari
The era of huge data necessitates highly efficient machine learning algorithms. Many common machine learning algorithms, however, rely on computationally intensive subroutines that…
Bayesian Decision Trees via Tractable Priors and Probabilistic Context-Free Grammars
Colin Sullivan, Mo Tiwari, Sebastian Thrun +1
Decision Trees are some of the most popular machine learning models today due to their out-of-the-box performance and interpretability. Often, Decision Trees models are constructed…
MABSplit: Faster Forest Training Using Multi-Armed Bandits
Mo Tiwari, Ryan Kang, Je-Yong Lee +4
Random forests are some of the most widely used machine learning models today, especially in domains that necessitate interpretability. We present an algorithm that accelerates the…
Faster Maximum Inner Product Search in High Dimensions
Mo Tiwari, Ryan Kang, Je-Yong Lee +5
Maximum Inner Product Search (MIPS) is a ubiquitous task in machine learning applications such as recommendation systems. Given a query vector and atom vectors in -dimension…
GFlowNet Foundations
Yoshua Bengio, Salem Lahlou, Tristan Deleu +3
Generative Flow Networks (GFlowNets) have been introduced as a method to sample a diverse set of candidates in an active learning context, with a training objective that makes them…
Image Compression and Classification Using Qubits and Quantum Deep Learning
Ali Mohsen, Mo Tiwari
Recent work suggests that quantum machine learning techniques can be used for classical image classification by encoding the images in quantum states and using a quantum neural net…
BanditPAM: Almost Linear Time -Medoids Clustering via Multi-Armed Bandits
Mo Tiwari, Martin Jinye Zhang, James Mayclin +3
Clustering is a ubiquitous task in data science. Compared to the commonly used -means clustering, -medoids clustering requires the cluster centers to be actual data points an…