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07/20/2023
Invalid Logic, Equivalent Gains: The Bizarreness of Reasoning in Language Model Prompting
Language models can be prompted to reason through problems in a manner t...
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07/20/2023
Deceptive Alignment Monitoring
As the capabilities of large machine learning models continue to grow, a...
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07/20/2023
FACADE: A Framework for Adversarial Circuit Anomaly Detection and Evaluation
We present FACADE, a novel probabilistic and geometric framework designe...
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06/24/2023
Is Pre-training Truly Better Than Meta-Learning?
In the context of few-shot learning, it is currently believed that a fix...
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06/24/2023
Beyond Scale: the Diversity Coefficient as a Data Quality Metric Demonstrates LLMs are Pre-trained on Formally Diverse Data
Current trends to pre-train capable Large Language Models (LLMs) mostly ...
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06/22/2023
Communication-Efficient Federated Learning through Importance Sampling
The high communication cost of sending model updates from the clients to...
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06/20/2023
DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models
Generative Pre-trained Transformer (GPT) models have exhibited exciting ...
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06/01/2023
Pairwise Ranking Losses of Click-Through Rates Prediction for Welfare Maximization in Ad Auctions
We study the design of loss functions for click-through rates (CTR) to o...
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04/28/2023
Are Emergent Abilities of Large Language Models a Mirage?
Recent work claims that large language models display emergent abilities...
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04/05/2023
Globalizing Fairness Attributes in Machine Learning: A Case Study on Health in Africa
With growing machine learning (ML) applications in healthcare, there hav...
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03/01/2023
Finite-sample Guarantees for Nash Q-learning with Linear Function Approximation
Nash Q-learning may be considered one of the first and most known algori...
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12/21/2022
Adapting to Latent Subgroup Shifts via Concepts and Proxies
We address the problem of unsupervised domain adaptation when the source...
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10/04/2022
Invariant Aggregator for Defending Federated Backdoor Attacks
Federated learning is gaining popularity as it enables training of high-...
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10/04/2022
Coordinated Science Laboratory 70th Anniversary Symposium: The Future of Computing
In 2021, the Coordinated Science Laboratory CSL, an Interdisciplinary Re...
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08/02/2022
The Curse of Low Task Diversity: On the Failure of Transfer Learning to Outperform MAML and Their Empirical Equivalence
Recently, it has been observed that a transfer learning solution might b...
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05/31/2022
One Policy is Enough: Parallel Exploration with a Single Policy is Minimax Optimal for Reward-Free Reinforcement Learning
While parallelism has been extensively used in Reinforcement Learning (R...
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05/01/2022
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Prediction
More and more investors and machine learning models rely on social media...
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01/31/2022
Fair Wrapping for Black-box Predictions
We introduce a new family of techniques to post-process ("wrap") a black...
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12/24/2021
Does MAML Only Work via Feature Re-use? A Data Centric Perspective
Recent work has suggested that a good embedding is all we need to solve ...
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12/24/2021
The Curse of Zero Task Diversity: On the Failure of Transfer Learning to Outperform MAML and their Empirical Equivalence
Recently, it has been observed that a transfer learning solution might b...
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07/10/2020
EMIXER: End-to-end Multimodal X-ray Generation via Self-supervision
Deep generative models have enabled the automated synthesis of high-qual...
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06/24/2020
Fairness with Overlapping Groups
In algorithmically fair prediction problems, a standard goal is to ensur...
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12/10/2019
Advances and Open Problems in Federated Learning
Federated learning (FL) is a machine learning setting where many clients...
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07/13/2019
FMRI data augmentation via synthesis
We present an empirical evaluation of fMRI data augmentation via synthes...
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04/11/2019
Max-Sliced Wasserstein Distance and its use for GANs
Generative adversarial nets (GANs) and variational auto-encoders have si...
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03/16/2019
SLSGD: Secure and Efficient Distributed On-device Machine Learning
We consider distributed on-device learning with limited communication an...
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03/16/2019
Practical Distributed Learning: Secure Machine Learning with Communication-Efficient Local Updates
Federated learning on edge devices poses new challenges arising from wor...
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03/10/2019
Fall of Empires: Breaking Byzantine-tolerant SGD by Inner Product Manipulation
Recently, new defense techniques have been developed to tolerate Byzanti...
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03/10/2019
Asynchronous Federated Optimization
Federated learning enables training on a massive number of edge devices....
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01/30/2019
On the Consistency of Top-k Surrogate Losses
The top-k error is often employed to evaluate performance for challengin...
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09/01/2018
