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08/08/2023
Cumulative Reasoning with Large Language Models
While language models are powerful and versatile, they often fail to add...
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05/27/2023
Kernel-SSL: Kernel KL Divergence for Self-Supervised Learning
Contrastive learning usually compares one positive anchor sample with lo...
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05/17/2023
RelationMatch: Matching In-batch Relationships for Semi-supervised Learning
Semi-supervised learning has achieved notable success by leveraging very...
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05/02/2023
On Uni-Modal Feature Learning in Supervised Multi-Modal Learning
We abstract the features (i.e. learned representations) of multi-modal d...
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03/27/2023
Contrastive Learning Is Spectral Clustering On Similarity Graph
Contrastive learning is a powerful self-supervised learning method, but ...
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03/08/2023
A Categorical Framework of General Intelligence
Can machines think? Since Alan Turing asked this question in 1950, nobod...
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03/01/2023
Succinct Representations for Concepts
Foundation models like chatGPT have demonstrated remarkable performance ...
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11/29/2022
On the power of foundation models
With infinitely many high-quality data points, infinite computational po...
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10/31/2022
Consistent and Truthful Interpretation with Fourier Analysis
For many interdisciplinary fields, ML interpretations need to be consist...
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06/06/2022
Anomaly Detection with Test Time Augmentation and Consistency Evaluation
Deep neural networks are known to be vulnerable to unseen data: they may...
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06/11/2021
Towards Understanding Generalization via Decomposing Excess Risk Dynamics
Generalization is one of the critical issues in machine learning. Howeve...
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03/08/2021
T-SCI: A Two-Stage Conformal Inference Algorithm with Guaranteed Coverage for Cox-MLP
It is challenging to deal with censored data, where we only have access ...
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09/24/2020
Secure Data Sharing With Flow Model
In the classical multi-party computation setting, multiple parties joint...
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06/04/2020
Inject Machine Learning into Significance Test for Misspecified Linear Models
Due to its strong interpretability, linear regression is widely used in ...
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02/10/2020
Adversarial Data Encryption
In the big data era, many organizations face the dilemma of data sharing...
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10/30/2019
Learning-Based Low-Rank Approximations
We introduce a "learning-based" algorithm for the low-rank decomposition...
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07/12/2019
System-Level Development of a User-Integrated Semi-Autonomous Lawn Mowing System: Problem Overview, Basic Requirements, and Proposed Architecture
This concept paper outlines some recent efforts toward the design and de...
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06/12/2019
A Stratified Approach to Robustness for Randomly Smoothed Classifiers
Strong theoretical guarantees of robustness can be given for ensembles o...
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02/02/2019
Asymmetric Valleys: Beyond Sharp and Flat Local Minima
Despite the non-convex nature of their loss functions, deep neural netwo...
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06/19/2018
An empirical study on evaluation metrics of generative adversarial networks
Evaluating generative adversarial networks (GANs) is inherently challeng...
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02/17/2018
An Alternative View: When Does SGD Escape Local Minima?
Stochastic gradient descent (SGD) is widely used in machine learning. Al...
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06/02/2017
Hyperparameter Optimization: A Spectral Approach
We give a simple, fast algorithm for hyperparameter optimization inspire...
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02/05/2016
Exploiting the Structure: Stochastic Gradient Methods Using Raw Clusters
The amount of data available in the world is growing faster than our abi...
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12/30/2015
Even Faster Accelerated Coordinate Descent Using Non-Uniform Sampling
Accelerated coordinate descent is widely used in optimization due to its...
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06/05/2015
Improved SVRG for Non-Strongly-Convex or Sum-of-Non-Convex Objectives
Many classical algorithms are found until several years later to outlive...
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03/06/2015
