4 citations · 7 across the 8 of their papers we have counts for
10 papers
Sharing standardized image-derived data in computational pathology using DICOM
Daniela P. Schacherer, Christopher P. Bridge, David Clunie +13
Development and evaluation of computational pathology methods require access to large and diverse datasets. Over the past decade, various initiatives invested significantly into co…
Almost-Optimal Upper and Lower Bounds for Clustering in Low Dimensional Euclidean Spaces
Vincent Cohen-Addad, Karthik C. S., David Saulpic +1
The -median and -means clustering objectives are classic objectives for modeling clustering in a metric space. Given a set of points in a metric space, the goal of the -me…
EUGens: Efficient, Unified, and General Dense Layers
Sang Min Kim, Byeongchan Kim, Arijit Sehanobish +7
Efficient neural networks are essential for scaling machine learning models to real-time applications and resource-constrained environments. Fully-connected feedforward layers (FFL…
Small steps no more: Global convergence of stochastic gradient bandits for arbitrary learning rates
Jincheng Mei, Bo Dai, Alekh Agarwal +4
We provide a new understanding of the stochastic gradient bandit algorithm by showing that it converges to a globally optimal policy almost surely using \emph{any} constant learnin…
The FACTS Grounding Leaderboard: Benchmarking LLMs' Ability to Ground Responses to Long-Form Input
Alon Jacovi, Andrew Wang, Chris Alberti +23
We introduce FACTS Grounding, an online leaderboard and associated benchmark that evaluates language models' ability to generate text that is factually accurate with respect to giv…
EUGens: Efficient, Unified, and General Dense Layers
Sang Min Kim, Byeongchan Kim, Arijit Sehanobish +7
Efficient neural networks are essential for scaling machine learning models to real-time applications and resource-constrained environments. Fully-connected feedforward layers (FFL…
Federated Learning in Practice: Reflections and Projections
Katharine Daly, Hubert Eichner, Peter Kairouz +3
Federated Learning (FL) is a machine learning technique that enables multiple entities to collaboratively learn a shared model without exchanging their local data. Over the past de…
Rate-Optimal Online Convex Optimization in Adaptive Linear Control
Asaf Cassel, Alon Cohen, Tomer Koren
We consider the problem of controlling an unknown linear dynamical system under adversarially changing convex costs and full feedback of both the state and cost function. We presen…
Efficient Online Linear Control with Stochastic Convex Costs and Unknown Dynamics
Asaf Cassel, Alon Cohen, Tomer Koren
We consider the problem of controlling an unknown linear dynamical system under a stochastic convex cost and full feedback of both the state and cost function. We present a computa…
CSI: Contrastive Data Stratification for Interaction Prediction and its Application to Compound-Protein Interaction Prediction
Apurva Kalia, Dilip Krishnan, Soha Hassoun
Accurately predicting the likelihood of interaction between two objects (compound-protein sequence, user-item, author-paper, etc.) is a fundamental problem in Computer Science. Cur…