519 research outputs found
Representation Reliability and Its Impact on Downstream Tasks
Self-supervised pre-trained models extract general-purpose representations
from data, and quantifying how reliable they are is crucial because many
downstream models use these representations as input for their own tasks. To
this end, we first introduce a formal definition of representation reliability:
the representation for a given test input is considered to be reliable if the
downstream models built on top of that representation can consistently generate
accurate predictions for that test point. It is desired to estimate the
representation reliability without knowing the downstream tasks a priori. We
provide a negative result showing that existing frameworks for uncertainty
quantification in supervised learning are not suitable for this purpose. As an
alternative, we propose an ensemble-based method for quantifying representation
reliability, based on the concept of neighborhood consistency in the
representation spaces across various pre-trained models. More specifically, the
key insight is to use shared neighboring points as anchors to align different
representation spaces. We demonstrate through comprehensive numerical
experiments that our method is capable of predicting representation reliability
with high accuracy
Some Results on Skorokhod Embedding and Robust Hedging with Local Time
In this paper, we provide some results on Skorokhod embedding with local time
and its applications to the robust hedging problem in finance. First we
investigate the robust hedging of options depending on the local time by using
the recently introduced stochastic control approach, in order to identify the
optimal hedging strategies, as well as the market models that realize the
extremal no-arbitrage prices. As a by-product, the optimality of Vallois'
Skorokhod embeddings is recovered. In addition, under appropriate conditions,
we derive a new solution to the two-marginal Skorokhod embedding as a
generalization of the Vallois solution. It turns out from our analysis that one
needs to relax the monotonicity assumption on the embedding functions in order
to embed a larger class of marginal distributions. Finally, in a full-marginal
setting where the stopping times given by Vallois are well-ordered, we
construct a remarkable Markov martingale which provides a new example of fake
Brownian motion
Probabilistic Concept Bottleneck Models
Interpretable models are designed to make decisions in a human-interpretable
manner. Representatively, Concept Bottleneck Models (CBM) follow a two-step
process of concept prediction and class prediction based on the predicted
concepts. CBM provides explanations with high-level concepts derived from
concept predictions; thus, reliable concept predictions are important for
trustworthiness. In this study, we address the ambiguity issue that can harm
reliability. While the existence of a concept can often be ambiguous in the
data, CBM predicts concepts deterministically without considering this
ambiguity. To provide a reliable interpretation against this ambiguity, we
propose Probabilistic Concept Bottleneck Models (ProbCBM). By leveraging
probabilistic concept embeddings, ProbCBM models uncertainty in concept
prediction and provides explanations based on the concept and its corresponding
uncertainty. This uncertainty enhances the reliability of the explanations.
Furthermore, as class uncertainty is derived from concept uncertainty in
ProbCBM, we can explain class uncertainty by means of concept uncertainty. Code
is publicly available at https://github.com/ejkim47/prob-cbm.Comment: International Conference on Machine Learning (ICML) 202
Introspective Deep Metric Learning for Image Retrieval
This paper proposes an introspective deep metric learning (IDML) framework
for uncertainty-aware comparisons of images. Conventional deep metric learning
methods produce confident semantic distances between images regardless of the
uncertainty level. However, we argue that a good similarity model should
consider the semantic discrepancies with caution to better deal with ambiguous
images for more robust training. To achieve this, we propose to represent an
image using not only a semantic embedding but also an accompanying uncertainty
embedding, which describes the semantic characteristics and ambiguity of an
image, respectively. We further propose an introspective similarity metric to
make similarity judgments between images considering both their semantic
differences and ambiguities. The proposed IDML framework improves the
performance of deep metric learning through uncertainty modeling and attains
state-of-the-art results on the widely used CUB-200-2011, Cars196, and Stanford
Online Products datasets for image retrieval and clustering. We further provide
an in-depth analysis of our framework to demonstrate the effectiveness and
reliability of IDML. Code is available at: https://github.com/wzzheng/IDML.Comment: The extended version of this paper is accepted to T-PAMI. Source code
available at https://github.com/wzzheng/IDM
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