1,406 research outputs found
Optimal Treatment Regimes for Proximal Causal Learning
A common concern when a policymaker draws causal inferences from and makes
decisions based on observational data is that the measured covariates are
insufficiently rich to account for all sources of confounding, i.e., the
standard no confoundedness assumption fails to hold. The recently proposed
proximal causal inference framework shows that proxy variables that abound in
real-life scenarios can be leveraged to identify causal effects and therefore
facilitate decision-making. Building upon this line of work, we propose a novel
optimal individualized treatment regime based on so-called outcome and
treatment confounding bridges. We then show that the value function of this new
optimal treatment regime is superior to that of existing ones in the
literature. Theoretical guarantees, including identification, superiority,
excess value bound, and consistency of the estimated regime, are established.
Furthermore, we demonstrate the proposed optimal regime via numerical
experiments and a real data application.Comment: NeurIPS 202
Relativistic Stochastic Dynamics I: Langevin Equation from Observer's Perspective
Two different versions of relativistic Langevin equation in generic curved
spacetime background are constructed, both are manifestly general covariant. It
is argued that, from the observer's point of view, the version which takes the
proper time of the Brownian particle as evolution parameter contains some
conceptual issues, while the one which makes use of the proper time of the
observer is more physically sound. The two versions of the relativistic
Langevin equation are connected by a reparametrization scheme. In spite of the
issues contained in the first version of the relativistic Langevin equation, it
still permits to extract the physical probability distributions of the Brownian
particles, as is shown by Monte Carlo simulation in the example case of
Brownian motion in -dimensional Minkowski spacetime.Comment: 23 page
Optimizing Guided Traversal for Fast Learned Sparse Retrieval
Recent studies show that BM25-driven dynamic index skipping can greatly
accelerate MaxScore-based document retrieval based on the learned sparse
representation derived by DeepImpact. This paper investigates the effectiveness
of such a traversal guidance strategy during top k retrieval when using other
models such as SPLADE and uniCOIL, and finds that unconstrained BM25-driven
skipping could have a visible relevance degradation when the BM25 model is not
well aligned with a learned weight model or when retrieval depth k is small.
This paper generalizes the previous work and optimizes the BM25 guided index
traversal with a two-level pruning control scheme and model alignment for fast
retrieval using a sparse representation. Although there can be a cost of
increased latency, the proposed scheme is much faster than the original
MaxScore method without BM25 guidance while retaining the relevance
effectiveness. This paper analyzes the competitiveness of this two-level
pruning scheme, and evaluates its tradeoff in ranking relevance and time
efficiency when searching several test datasets.Comment: This paper is published in WWW'2
A Unified Model for the Two-stage Offline-then-Online Resource Allocation
With the popularity of the Internet, traditional offline resource allocation
has evolved into a new form, called online resource allocation. It features the
online arrivals of agents in the system and the real-time decision-making
requirement upon the arrival of each online agent. Both offline and online
resource allocation have wide applications in various real-world matching
markets ranging from ridesharing to crowdsourcing. There are some emerging
applications such as rebalancing in bike sharing and trip-vehicle dispatching
in ridesharing, which involve a two-stage resource allocation process. The
process consists of an offline phase and another sequential online phase, and
both phases compete for the same set of resources. In this paper, we propose a
unified model which incorporates both offline and online resource allocation
into a single framework. Our model assumes non-uniform and known arrival
distributions for online agents in the second online phase, which can be
learned from historical data. We propose a parameterized linear programming
(LP)-based algorithm, which is shown to be at most a constant factor of
from the optimal. Experimental results on the real dataset show that our
LP-based approaches outperform the LP-agnostic heuristics in terms of
robustness and effectiveness.Comment: Accepted by IJCAI 2020
(http://static.ijcai.org/2020-accepted_papers.html) and SOLE copyright holder
is IJCAI (International Joint Conferences on Artificial Intelligence), all
rights reserve
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