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Found 14 papers, 2 papers with code
Privacy-preserving logistic regression
This paper addresses the important tradeoff between privacy and learnability, when designing algorithms for learning from private databases.
Streaming k-means approximation
We provide a clustering algorithm that approximately optimizes the k-means objective, in the one-pass streaming setting.
Convergence rate of stochastic k-means
We analyze online \cite{BottouBengio} and mini-batch \cite{Sculley} $k$-means variants.
Demystifying overcomplete nonlinear auto-encoders: fast SGD convergence towards sparse representation from random initialization
Auto-encoders are commonly used for unsupervised representation learning and for pre-training deeper neural networks.
Cooperative Online Learning: Keeping your Neighbors Updated
However, when agents can choose to ignore some of their neighbors based on the knowledge of the network structure, we prove a $O(\sqrt{\overline{\chi} T})$ sublinear regret bound, where $\overline{\chi} \ge \alpha$ is the clique-covering number of the network.
Evaluating the distribution learning capabilities of GANs
We evaluate the distribution learning capabilities of generative adversarial networks by testing them on synthetic datasets.
Tropical Cyclone Track Forecasting using Fused Deep Learning from Aligned Reanalysis Data
The forecast of tropical cyclone trajectories is crucial for the protection of people and property.
ClimAlign: Unsupervised statistical downscaling of climate variables via normalizing flows
Downscaling is a landmark task in climate science and meteorology in which the goal is to use coarse scale, spatio-temporal data to infer values at finer scales.
Reflections from the Workshop on AI-Assisted Decision Making for Conservation
In this white paper, we synthesize key points made during presentations and discussions from the AI-Assisted Decision Making for Conservation workshop, hosted by the Center for Research on Computation and Society at Harvard University on October 20-21, 2022.
Sea level Projections with Machine Learning using Altimetry and Climate Model ensembles
We use machine learning (ML) to investigate future patterns of sea level change.
STint: Self-supervised Temporal Interpolation for Geospatial Data
Nevertheless, most prevailing temporal interpolation techniques hinge on optical flow, which encodes the motion of pixels between video frames.
Multi-decadal Sea Level Prediction using Neural Networks and Spectral Clustering on Climate Model Large Ensembles and Satellite Altimeter Data
Our results demonstrate that segmenting the spatial dataset with spectral clustering improves the ML predictions.
Application-Driven Innovation in Machine Learning
As applications of machine learning proliferate, innovative algorithms inspired by specific real-world challenges have become increasingly important.
