Search Results for author:
Found 17 papers, 1 papers with code
Online learning with graph-structured feedback against adaptive adversaries
When the adversary is allowed a bounded memory of size 1, we show that a matching lower bound of $\widetilde\Omega(T^{2/3})$ is achieved in the case of full-information feedback.
CogCompTime: A Tool for Understanding Time in Natural Language
Automatic extraction of temporal information is important for natural language understanding.
Does Data Augmentation Lead to Positive Margin?
Data augmentation (DA) is commonly used during model training, as it significantly improves test error and model robustness.
A Structured Learning Approach to Temporal Relation Extraction
Identifying temporal relations between events is an essential step towards natural language understanding.
Ranked #1 on
Temporal Information Extraction
on TempEval-3
Joint Reasoning for Temporal and Causal Relations
Understanding temporal and causal relations between events is a fundamental natural language understanding task.
CogCompTime: A Tool for Understanding Time in Natural Language Text
Automatic extraction of temporal information in text is an important component of natural language understanding.
Provable Adaptation across Multiway Domains via Representation Learning
This paper studies zero-shot domain adaptation where each domain is indexed on a multi-dimensional array, and we only have data from a small subset of domains.
Non-PSD Matrix Sketching with Applications to Regression and Optimization
In this paper, we present novel dimensionality reduction methods for non-PSD matrices, as well as their ``square-roots", which involve matrices with complex entries.
Learning-Augmented $k$-means Clustering
$k$-means clustering is a well-studied problem due to its wide applicability.
Text Descriptions are Compressive and Invariant Representations for Visual Learning
This method first automatically generates multiple visual descriptions of each class via a large language model (LLM), then uses a VLM to translate these descriptions to a set of visual feature embeddings of each image, and finally uses sparse logistic regression to select a relevant subset of these features to classify each image.
Monotone deep Boltzmann machines
Deep Boltzmann machines (DBMs), one of the first ``deep'' learning methods ever studied, are multi-layered probabilistic models governed by a pairwise energy function that describes the likelihood of all variables/nodes in the network.
On the Neural Tangent Kernel of Equilibrium Models
This work studies the neural tangent kernel (NTK) of the deep equilibrium (DEQ) model, a practical ``infinite-depth'' architecture which directly computes the infinite-depth limit of a weight-tied network via root-finding.
TOFU: A Task of Fictitious Unlearning for LLMs
Large language models trained on massive corpora of data from the web can memorize and reproduce sensitive or private data raising both legal and ethical concerns.
An Axiomatic Approach to Model-Agnostic Concept Explanations
Concept explanation is a popular approach for examining how human-interpretable concepts impact the predictions of a model.
RankCLIP: Ranking-Consistent Language-Image Pretraining
Among the ever-evolving development of vision-language models, contrastive language-image pretraining (CLIP) has set new benchmarks in many downstream tasks such as zero-shot classifications by leveraging self-supervised contrastive learning on large amounts of text-image pairs.
Rethinking LLM Memorization through the Lens of Adversarial Compression
We outline the limitations of existing notions of memorization and show how the ACR overcomes these challenges by (i) offering an adversarial view to measuring memorization, especially for monitoring unlearning and compliance; and (ii) allowing for the flexibility to measure memorization for arbitrary strings at a reasonably low compute.
