Search Results for author:
Found 36 papers, 9 papers with code
Kernel Methods for Cooperative Multi-Agent Learning with Delays
We propose Coop-KernelUCB that provides near-optimal bounds on the per-agent regret in this setting, and is both computationally and communicatively efficient.
Robust Multi-Agent Decision-Making with Heavy-Tailed Payoffs
We study the heavy-tailed stochastic bandit problem in the cooperative multiagent setting, where a group of agents interact with a common bandit problem, while communicating on a network with delays.
Context Diffusion: In-Context Aware Image Generation
We propose Context Diffusion, a diffusion-based framework that enables image generation models to learn from visual examples presented in context.
Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack
Training text-to-image models with web scale image-text pairs enables the generation of a wide range of visual concepts from text.
Filtering, Distillation, and Hard Negatives for Vision-Language Pre-Training
Vision-language models trained with contrastive learning on large-scale noisy data are becoming increasingly popular for zero-shot recognition problems.
PACO: Parts and Attributes of Common Objects
This motivates the need for large datasets which go beyond traditional object masks and provide richer annotations such as part masks and attributes.
Private and Byzantine-Proof Cooperative Decision-Making
In this paper, we investigate the stochastic bandit problem under two settings - (a) when the agents wish to make their communication private with respect to the action sequence, and (b) when the agents can be byzantine, i. e., they provide (stochastically) incorrect information.
Scalable Interpretability via Polynomials
We demonstrate by human subject evaluations that SPAMs are demonstrably more interpretable in practice, and are hence an effortless replacement for DNNs for creating interpretable and high-performance systems suitable for large-scale machine learning.
Neural Basis Models for Interpretability
However, these models are typically black-box deep neural networks, explained post-hoc via methods with known faithfulness limitations.
Adaptive Methods for Aggregated Domain Generalization
In this paper, we propose a domain-adaptive approach to this problem, which operates in two steps: (a) we cluster training data within a carefully chosen feature space to create pseudo-domains, and (b) using these pseudo-domains we learn a domain-adaptive classifier that makes predictions using information about both the input and the pseudo-domain it belongs to.
Ranked #16 on
Domain Generalization
on PACS
One More Step Towards Reality: Cooperative Bandits with Imperfect Communication
However, most research for this problem focuses exclusively on the setting with perfect communication, whereas in most real-world distributed settings, communication is often over stochastic networks, with arbitrary corruptions and delays.
Social influence leads to the formation of diverse local trends
How does the visual design of digital platforms impact user behavior and the resulting environment?
Adaptive Methods for Real-World Domain Generalization
We show that the existing approaches either do not scale to this dataset or underperform compared to the simple baseline of training a model on the union of data from all training domains.
Provably Efficient Cooperative Multi-Agent Reinforcement Learning with Function Approximation
Reinforcement learning in cooperative multi-agent settings has recently advanced significantly in its scope, with applications in cooperative estimation for advertising, dynamic treatment regimes, distributed control, and federated learning.
No-Regret Algorithms for Private Gaussian Process Bandit Optimization
In this paper, we consider the ubiquitous problem of gaussian process (GP) bandit optimization from the lens of privacy-preserving statistics.
Differentially-Private Federated Linear Bandits
The rapid proliferation of decentralized learning systems mandates the need for differentially-private cooperative learning.
Kernel Methods for Cooperative Multi-Agent Contextual Bandits
For this problem, we propose \textsc{Coop-KernelUCB}, an algorithm that provides near-optimal bounds on the per-agent regret, and is both computationally and communicatively efficient.
Cooperative Multi-Agent Bandits with Heavy Tails
We study the heavy-tailed stochastic bandit problem in the cooperative multi-agent setting, where a group of agents interact with a common bandit problem, while communicating on a network with delays.
Smaller Models, Better Generalization
Reducing network complexity has been a major research focus in recent years with the advent of mobile technology.
Thompson Sampling on Symmetric $α$-Stable Bandits
Thompson Sampling provides an efficient technique to introduce prior knowledge in the multi-armed bandit problem, along with providing remarkable empirical performance.
Defense Against Adversarial Images using Web-Scale Nearest-Neighbor Search
Empirical evaluations of this defense strategy on ImageNet suggest that it is very effective in attack settings in which the adversary does not have access to the image database.
Leveraging Communication Topologies Between Learning Agents in Deep Reinforcement Learning
A common technique to improve learning performance in deep reinforcement learning (DRL) and many other machine learning algorithms is to run multiple learning agents in parallel.
Evaluating Generative Adversarial Networks on Explicitly Parameterized Distributions
The true distribution parameterizations of commonly used image datasets are inaccessible.
No Peek: A Survey of private distributed deep learning
We survey distributed deep learning models for training or inference without accessing raw data from clients.
Maximum-Entropy Fine Grained Classification
Fine-Grained Visual Classification (FGVC) is an important computer vision problem that involves small diversity within the different classes, and often requires expert annotators to collect data.
Ranked #19 on
Fine-Grained Image Classification
on NABirds
(using extra training data)
How to Organize your Deep Reinforcement Learning Agents: The Importance of Communication Topology
This is an important problem because a common technique to improve speed and robustness of learning in deep reinforcement learning and many other machine learning algorithms is to run multiple learning agents in parallel.
Maximum-Entropy Fine-Grained Classification
Fine-Grained Visual Classification (FGVC) is an important computer vision problem that involves small diversity within the different classes, and often requires expert annotators to collect data.
Coreset-Based Neural Network Compression
We propose a novel Convolutional Neural Network (CNN) compression algorithm based on coreset representations of filters.
Closing the AI Knowledge Gap
AI researchers employ not only the scientific method, but also methodology from mathematics and engineering.
MemeSequencer: Sparse Matching for Embedding Image Macros
The analysis of the creation, mutation, and propagation of social media content on the Internet is an essential problem in computational social science, affecting areas ranging from marketing to political mobilization.
Modeling Image Virality with Pairwise Spatial Transformer Networks
The study of virality and information diffusion online is a topic gaining traction rapidly in the computational social sciences.
Learning Neural Network Classifiers with Low Model Complexity
Our proposed approach yields benefits across a wide range of architectures, in comparison to and in conjunction with methods such as Dropout and Batch Normalization, and our results strongly suggest that deep learning techniques can benefit from model complexity control methods such as the LCNN learning rule.
Pairwise Confusion for Fine-Grained Visual Classification
Fine-Grained Visual Classification (FGVC) datasets contain small sample sizes, along with significant intra-class variation and inter-class similarity.
Ranked #16 on
Fine-Grained Image Classification
on Stanford Dogs
Examining Representational Similarity in ConvNets and the Primate Visual Cortex
We compare several ConvNets with different depth and regularization techniques with multi-unit macaque IT cortex recordings and assess the impact of the same on representational similarity with the primate visual cortex.
Deep Learning the City : Quantifying Urban Perception At A Global Scale
Computer vision methods that quantify the perception of urban environment are increasingly being used to study the relationship between a city's physical appearance and the behavior and health of its residents.
Coreset-Based Adaptive Tracking
We propose a method for learning from streaming visual data using a compact, constant size representation of all the data that was seen until a given moment.
