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Found 33 papers, 9 papers with code
Optimization and Analysis of the pAp@k Metric for Recommender Systems
Modern recommendation and notification systems must be robust to data imbalance, limitations on the number of recommendations/notifications, and heterogeneous engagement profiles across users.
FACADE: A Framework for Adversarial Circuit Anomaly Detection and Evaluation
We present FACADE, a novel probabilistic and geometric framework designed for unsupervised mechanistic anomaly detection in deep neural networks.
Deceptive Alignment Monitoring
As the capabilities of large machine learning models continue to grow, and as the autonomy afforded to such models continues to expand, the spectre of a new adversary looms: the models themselves.
Invalid Logic, Equivalent Gains: The Bizarreness of Reasoning in Language Model Prompting
We find that the logically \textit{invalid} reasoning prompts do indeed achieve similar performance gains on BBH tasks as logically valid reasoning prompts.
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 focus on scaling of model and dataset size.
Is Pre-training Truly Better Than Meta-Learning?
Using this analysis, we demonstrate the following: 1. when the formal diversity of a data set is low, PT beats MAML on average and 2. when the formal diversity is high, MAML beats PT on average.
Communication-Efficient Federated Learning through Importance Sampling
The high communication cost of sending model updates from the clients to the server is a significant bottleneck for scalable federated learning (FL).
DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models
Generative Pre-trained Transformer (GPT) models have exhibited exciting progress in capabilities, capturing the interest of practitioners and the public alike.
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 optimize (social) welfare in advertising auctions.
Are Emergent Abilities of Large Language Models a Mirage?
Recent work claims that large language models display emergent abilities, abilities not present in smaller-scale models that are present in larger-scale models.
Globalizing Fairness Attributes in Machine Learning: A Case Study on Health in Africa
With growing machine learning (ML) applications in healthcare, there have been calls for fairness in ML to understand and mitigate ethical concerns these systems may pose.
Finite-sample Guarantees for Nash Q-learning with Linear Function Approximation
Nash Q-learning may be considered one of the first and most known algorithms in multi-agent reinforcement learning (MARL) for learning policies that constitute a Nash equilibrium of an underlying general-sum Markov game.
Adapting to Latent Subgroup Shifts via Concepts and Proxies
We show that the optimal target predictor can be non-parametrically identified with the help of concept and proxy variables available only in the source domain, and unlabeled data from the target.
Latent Multimodal Functional Graphical Model Estimation
Joint multimodal functional data acquisition, where functional data from multiple modes are measured simultaneously from the same subject, has emerged as an exciting modern approach enabled by recent engineering breakthroughs in the neurological and biological sciences.
Invariant Aggregator for Defending against Federated Backdoor Attacks
Empirical results on three datasets with different modalities and varying numbers of clients further demonstrate that our approach mitigates a broad class of backdoor attacks with a negligible cost on the model utility.
The Curse of Low Task Diversity: On the Failure of Transfer Learning to Outperform MAML and Their Empirical Equivalence
This novel insight contextualizes claims that transfer learning solutions are better than meta-learned solutions in the regime of low diversity under a fair comparison.
One Policy is Enough: Parallel Exploration with a Single Policy is Near-Optimal for Reward-Free Reinforcement Learning
Although parallelism has been extensively used in reinforcement learning (RL), the quantitative effects of parallel exploration are not well understood theoretically.
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Predictions
More and more investors and machine learning models rely on social media (e. g., Twitter and Reddit) to gather real-time information and sentiment to predict stock price movements.
Fair Wrapping for Black-box Predictions
We introduce a new family of techniques to post-process ("wrap") a black-box classifier in order to reduce its bias.
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 many few-shot learning benchmarks.
The Curse of Zero Task Diversity: On the Failure of Transfer Learning to Outperform MAML and their Empirical Equivalence
We hypothesize that the diversity coefficient of the few-shot learning benchmark is predictive of whether meta-learning solutions will succeed or not.
A Field Guide to Federated Optimization
Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data, motivated by and designed for privacy protection.
EMIXER: End-to-end Multimodal X-ray Generation via Self-supervision
EMIXER is an conditional generative adversarial model by 1) generating an image based on a label, 2) encoding the image to a hidden embedding, 3) producing the corresponding text via a hierarchical decoder from the image embedding, and 4) a joint discriminator for assessing both the image and the corresponding text.
Fairness with Overlapping Groups
In algorithmically fair prediction problems, a standard goal is to ensure the equality of fairness metrics across multiple overlapping groups simultaneously.
Advances and Open Problems in Federated Learning
FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches.
FMRI data augmentation via synthesis
Thus, our results suggest that data augmentation via synthesis is a promising approach to address the limited availability of fMRI data, and to improve the quality of predictive fMRI models.
Max-Sliced Wasserstein Distance and its use for GANs
Generative adversarial nets (GANs) and variational auto-encoders have significantly improved our distribution modeling capabilities, showing promise for dataset augmentation, image-to-image translation and feature learning.
Zeno++: Robust Fully Asynchronous SGD
We propose Zeno++, a new robust asynchronous Stochastic Gradient Descent~(SGD) procedure which tolerates Byzantine failures of the workers.
SLSGD: Secure and Efficient Distributed On-device Machine Learning
We consider distributed on-device learning with limited communication and security requirements.
Fall of Empires: Breaking Byzantine-tolerant SGD by Inner Product Manipulation
Recently, new defense techniques have been developed to tolerate Byzantine failures for distributed machine learning.
Asynchronous Federated Optimization
Federated learning enables training on a massive number of edge devices.
On the Consistency of Top-k Surrogate Losses
Our analysis continues by showing previously proposed hinge-like top-$k$ surrogate losses are not top-$k$ calibrated and suggests no convex hinge loss is top-$k$ calibrated.
A Contextual-bandit-based Approach for Informed Decision-making in Clinical Trials
However, those approaches have failed to account for the variability among participants that is becoming increasingly evident as a result of recent clinical-trial-based studies.
