arXiv subjects

Shaunak Mishra

Publications and source records attributed to Shaunak Mishra.

17 recordsLinked to original sources

arXiv2026

The World Won't Stay Still: Programmable Evolution for Agent Benchmarks

LLM-powered tool-calling agents fulfill user requests by interacting with environments, querying data, and invoking tools in a multi-turn process. Yet, most existing benchmarks evaluate these systems under static environment interfaces, with fixed schemas and toolsets, making it difficult to assess how agents behave as environments evolves -- when capabilities are added, reorganized, or deprecated across successive environment versions. In this paper, we study structured environment evolution as a benchmark-construction problem for tool-calling agents. We propose ProEvolve, a graph-based framework that makes environment evolution programmable. At its core, a typed relational graph provides a unified, explicit representation of the environment - data, tools, and schema. Under this formalism, adding, removing, or modifying capabilities are expressed as graph transformations that coherently propagate updates across tools, schemas, and data access. Building on this, ProEvolve supports (1) automatic generation of evolved executable environments through explicit graph transformations, and (2) graph-grounded construction of task sandboxes via subgraph sampling and instantiation. We validate ProEvolve in two tool-calling domains, e-commerce and airline booking, in terms of quality, implementation validity, and failure modes. Finally, we use the generated benchmark as a downstream diagnostic to study how representative agents behave under structured environment evolution.

cs.AI↗

arXiv2026

Impact of Charge Transfer Inefficiency on transit light-curves: A correction strategy for PLATO

PLATO is designed to detect Earth-sized exoplanets around solar-type stars and to measure their radii with accuracy better than $2\%$ via the transit method. Charge transfer inefficiency (CTI), a by-product of radiation damage to CCDs, can jeopardise this accuracy and therefore must be corrected. We assessed and quantified the impact of CTI on transit-depth measurements and developed a correction strategy that restores CTI-biased depths within the accuracy budget. Using a calibration dataset generated with PLATOSim to simulate a realistic stellar field, we modelled the parallel overscan signal as a sum of exponential decays and used least-squares fitting to infer the number of trap species and initial estimates for the release times ($τ_{r,k}$). Smearing was modelled with an exponential-plus-constant function and removed on a column-wise basis. We modelled the spatial variation in trap density with a quadratic polynomial in radial distance from the focal-plane center. The polynomial coefficients ($a_{p,k}$), the well-fill power index ($β$), and the release times ($τ_{r,k}$) were adjusted via an iterative application of the extended pixel edge response (EPER) method combined with a CTI correction algorithm, yielding the final calibration model. In the worst-case scenario (8-year mission, high-CTI zone), CTI induced a bias of about $4\%$ in measured transit depth, reduced to a residual of $0.06\%$ after correction - well within PLATO's accuracy requirements. From the calibrated parameters, we derived a correction scheme that brought the photometric measurements within PLATO's noise budget, ensuring that the mission's precision requirements are met.

astro-ph.EP↗

arXiv2024

Salient Object-Aware Background Generation using Text-Guided Diffusion Models

Generating background scenes for salient objects plays a crucial role across various domains including creative design and e-commerce, as it enhances the presentation and context of subjects by integrating them into tailored environments. Background generation can be framed as a task of text-conditioned outpainting, where the goal is to extend image content beyond a salient object's boundaries on a blank background. Although popular diffusion models for text-guided inpainting can also be used for outpainting by mask inversion, they are trained to fill in missing parts of an image rather than to place an object into a scene. Consequently, when used for background creation, inpainting models frequently extend the salient object's boundaries and thereby change the object's identity, which is a phenomenon we call "object expansion." This paper introduces a model for adapting inpainting diffusion models to the salient object outpainting task using Stable Diffusion and ControlNet architectures. We present a series of qualitative and quantitative results across models and datasets, including a newly proposed metric to measure object expansion that does not require any human labeling. Compared to Stable Diffusion 2.0 Inpainting, our proposed approach reduces object expansion by 3.6x on average with no degradation in standard visual metrics across multiple datasets.

cs.CV↗

arXiv2023

Staging E-Commerce Products for Online Advertising using Retrieval Assisted Image Generation

Online ads showing e-commerce products typically rely on the product images in a catalog sent to the advertising platform by an e-commerce platform. In the broader ads industry such ads are called dynamic product ads (DPA). It is common for DPA catalogs to be in the scale of millions (corresponding to the scale of products which can be bought from the e-commerce platform). However, not all product images in the catalog may be appealing when directly re-purposed as an ad image, and this may lead to lower click-through rates (CTRs). In particular, products just placed against a solid background may not be as enticing and realistic as a product staged in a natural environment. To address such shortcomings of DPA images at scale, we propose a generative adversarial network (GAN) based approach to generate staged backgrounds for un-staged product images. Generating the entire staged background is a challenging task susceptible to hallucinations. To get around this, we introduce a simpler approach called copy-paste staging using retrieval assisted GANs. In copy paste staging, we first retrieve (from the catalog) staged products similar to the un-staged input product, and then copy-paste the background of the retrieved product in the input image. A GAN based in-painting model is used to fill the holes left after this copy-paste operation. We show the efficacy of our copy-paste staging method via offline metrics, and human evaluation. In addition, we show how our staging approach can enable animations of moving products leading to a video ad from a product image.

cs.CV↗

arXiv2022

Fast Parallel Hypertree Decompositions in Logarithmic Recursion Depth

Modern trends in data collection are bringing current mainstream techniques for database query processing to their limits. Consequently, various novel approaches for efficient query processing are being actively studied. One such approach is based on hypertree decompositions (HDs), which have been shown to carry great potential to process complex queries more efficiently and with stronger theoretical guarantees. However, using HDs for query execution relies on the difficult task of computing decompositions of the query structure, which guides the efficient execution of the query. From theoretical results we know that the performance of purely sequential methods is inherently limited, yet the problem is susceptible to parallelisation. In this paper we propose the first algorithm for computing hypertree decompositions that is well-suited for parallelisation. The proposed algorithm log-k-decomp requires only a logarithmic number of recursion levels and additionally allows for highly parallelised pruning of the search space by restriction to balanced separators. We provide detailed experimental evaluation over the HyperBench benchmark and demonstrate that our approach is highly effective especially for complex queries.

cs.DB↗

arXiv2021

TSI: an Ad Text Strength Indicator using Text-to-CTR and Semantic-Ad-Similarity

Coming up with effective ad text is a time consuming process, and particularly challenging for small businesses with limited advertising experience. When an inexperienced advertiser onboards with a poorly written ad text, the ad platform has the opportunity to detect low performing ad text, and provide improvement suggestions. To realize this opportunity, we propose an ad text strength indicator (TSI) which: (i) predicts the click-through-rate (CTR) for an input ad text, (ii) fetches similar existing ads to create a neighborhood around the input ad, (iii) and compares the predicted CTRs in the neighborhood to declare whether the input ad is strong or weak. In addition, as suggestions for ad text improvement, TSI shows anonymized versions of superior ads (higher predicted CTR) in the neighborhood. For (i), we propose a BERT based text-to-CTR model trained on impressions and clicks associated with an ad text. For (ii), we propose a sentence-BERT based semantic-ad-similarity model trained using weak labels from ad campaign setup data. Offline experiments demonstrate that our BERT based text-to-CTR model achieves a significant lift in CTR prediction AUC for cold start (new) advertisers compared to bag-of-words based baselines. In addition, our semantic-textual-similarity model for similar ads retrieval achieves a precision@1 of 0.93 (for retrieving ads from the same product category); this is significantly higher compared to unsupervised TF-IDF, word2vec, and sentence-BERT baselines. Finally, we share promising online results from advertisers in the Yahoo (Verizon Media) ad platform where a variant of TSI was implemented with sub-second end-to-end latency.

cs.CL↗

arXiv2021

Powering COVID-19 community Q&A with Curated Side Information

Community question answering and discussion platforms such as Reddit, Yahoo! answers or Quora provide users the flexibility of asking open ended questions to a large audience, and replies to such questions maybe useful both to the user and the community on certain topics such as health, sports or finance. Given the recent events around COVID-19, some of these platforms have attracted 2000+ questions from users about several aspects associated with the disease. Given the impact of this disease on general public, in this work we investigate ways to improve the ranking of user generated answers on COVID-19. We specifically explore the utility of external technical sources of side information (such as CDC guidelines or WHO FAQs) in improving answer ranking on such platforms. We found that ranking user answers based on question-answer similarity is not sufficient, and existing models cannot effectively exploit external (side) information. In this work, we demonstrate the effectiveness of different attention based neural models that can directly exploit side information available in technical documents or verified forums (e.g., research publications on COVID-19 or WHO website). Augmented with a temperature mechanism, the attention based neural models can selectively determine the relevance of side information for a given user question, while ranking answers.

cs.IR↗

arXiv2020

Learning to Create Better Ads: Generation and Ranking Approaches for Ad Creative Refinement

In the online advertising industry, the process of designing an ad creative (i.e., ad text and image) requires manual labor. Typically, each advertiser launches multiple creatives via online A/B tests to infer effective creatives for the target audience, that are then refined further in an iterative fashion. Due to the manual nature of this process, it is time-consuming to learn, refine, and deploy the modified creatives. Since major ad platforms typically run A/B tests for multiple advertisers in parallel, we explore the possibility of collaboratively learning ad creative refinement via A/B tests of multiple advertisers. In particular, given an input ad creative, we study approaches to refine the given ad text and image by: (i) generating new ad text, (ii) recommending keyphrases for new ad text, and (iii) recommending image tags (objects in image) to select new ad image. Based on A/B tests conducted by multiple advertisers, we form pairwise examples of inferior and superior ad creatives, and use such pairs to train models for the above tasks. For generating new ad text, we demonstrate the efficacy of an encoder-decoder architecture with copy mechanism, which allows some words from the (inferior) input text to be copied to the output while incorporating new words associated with higher click-through-rate. For the keyphrase and image tag recommendation task, we demonstrate the efficacy of a deep relevance matching model, as well as the relative robustness of ranking approaches compared to ad text generation in cold-start scenarios with unseen advertisers. We also share broadly applicable insights from our experiments using data from the Yahoo Gemini ad platform.

cs.CL↗

arXiv2020

Recommending Themes for Ad Creative Design via Visual-Linguistic Representations

There is a perennial need in the online advertising industry to refresh ad creatives, i.e., images and text used for enticing online users towards a brand. Such refreshes are required to reduce the likelihood of ad fatigue among online users, and to incorporate insights from other successful campaigns in related product categories. Given a brand, to come up with themes for a new ad is a painstaking and time consuming process for creative strategists. Strategists typically draw inspiration from the images and text used for past ad campaigns, as well as world knowledge on the brands. To automatically infer ad themes via such multimodal sources of information in past ad campaigns, we propose a theme (keyphrase) recommender system for ad creative strategists. The theme recommender is based on aggregating results from a visual question answering (VQA) task, which ingests the following: (i) ad images, (ii) text associated with the ads as well as Wikipedia pages on the brands in the ads, and (iii) questions around the ad. We leverage transformer based cross-modality encoders to train visual-linguistic representations for our VQA task. We study two formulations for the VQA task along the lines of classification and ranking; via experiments on a public dataset, we show that cross-modal representations lead to significantly better classification accuracy and ranking precision-recall metrics. Cross-modal representations show better performance compared to separate image and text representations. In addition, the use of multimodal information shows a significant lift over using only textual or visual information.

cs.CL↗

arXiv2019

Learning from Multi-User Activity Trails for B2B Ad Targeting

Online purchase decisions in organizations can go through a complex journey with multiple agents involved in the decision making process. Depending on the product being purchased, and the organizational structure, the process may involve employees who first conduct market research, and then influence decision makers who place the online purchase order. In such cases, the online activity trail of a single individual in the organization may only provide partial information for predicting purchases (conversions). To refine conversion prediction for business-to-business (B2B) products using online activity trails, we introduce the notion of relevant users in an organization with respect to a given B2B advertiser, and leverage the collective activity trails of such relevant users to predict conversions. In particular, our notion of relevant users is tied to a seed list of relevant activities for a B2B advertiser, and we propose a method using distributed activity representations to build such a seed list. Experiments using data from Yahoo Gemini demonstrate that the proposed methods can improve conversion prediction AUC by 8.8%, and provide an interpretable advertiser specific list of activities useful for B2B ad targeting.

cs.IR↗

arXiv2018

Managing App Install Ad Campaigns in RTB: A Q-Learning Approach

Real time bidding (RTB) enables demand side platforms (bidders) to scale ad campaigns across multiple publishers affiliated to an RTB ad exchange. While driving multiple campaigns for mobile app install ads via RTB, the bidder typically has to: (i) maintain each campaign's efficiency (i.e., meet advertiser's target cost-per-install), (ii) be sensitive to advertiser's budget, and (iii) make profit after payouts to the ad exchange. In this process, there is a sense of delayed rewards for the bidder's actions; the exchange charges the bidder right after the ad is shown, but the bidder gets to know about resultant installs after considerable delay. This makes it challenging for the bidder to decide beforehand the bid (and corresponding cost charged to advertiser) for each ad display opportunity. To jointly handle the objectives mentioned above, we propose a state space based policy which decides the exchange bid and advertiser cost for each opportunity. The state space captures the current efficiency, budget utilization and profit. The policy based on this state space is trained on past decisions and outcomes via a novel Q-learning algorithm which accounts for the delay in install notifications. In our experiments based on data from app install campaigns managed by Yahoo's Gemini advertising platform, the Q-learning based policy led to a significant increase in the profit and number of efficient campaigns.

cs.GT↗

arXiv2016

Group secret key agreement over state-dependent wireless broadcast channels

We consider a group of $m$ trusted and authenticated nodes that aim to create a shared secret key $K$ over a wireless channel in the presence of an eavesdropper Eve. We assume that there exists a state dependent wireless broadcast channel from one of the honest nodes to the rest of them including Eve. All of the trusted nodes can also discuss over a cost-free, noiseless and unlimited rate public channel which is also overheard by Eve. For this setup, we develop an information-theoretically secure secret key agreement protocol. We show the optimality of this protocol for "linear deterministic" wireless broadcast channels. This model generalizes the packet erasure model studied in literature for wireless broadcast channels. For "state-dependent Gaussian" wireless broadcast channels, we propose an achievability scheme based on a multi-layer wiretap code. Finding the best achievable secret key generation rate leads to solving a non-convex power allocation problem. We show that using a dynamic programming algorithm, one can obtain the best power allocation for this problem. Moreover, we prove the optimality of the proposed achievability scheme for the regime of high-SNR and large-dynamic range over the channel states in the (generalized) degrees of freedom sense.

cs.CR↗

arXiv2015

Secure State Estimation against Sensor Attacks in the Presence of Noise

We consider the problem of estimating the state of a noisy linear dynamical system when an unknown subset of sensors is arbitrarily corrupted by an adversary. We propose a secure state estimation algorithm, and derive (optimal) bounds on the achievable state estimation error given an upper bound on the number of attacked sensors. The proposed state estimator involves Kalman filters operating over subsets of sensors to search for a sensor subset which is reliable for state estimation. To further improve the subset search time, we propose Satisfiability Modulo Theory based techniques to exploit the combinatorial nature of searching over sensor subsets. Finally, as a result of independent interest, we give a coding theoretic view of attack detection and state estimation against sensor attacks in a noiseless dynamical system.

math.OC↗

arXiv2015

Secure State Estimation: Optimal Guarantees against Sensor Attacks in the Presence of Noise

Motivated by the need to secure cyber-physical systems against attacks, we consider the problem of estimating the state of a noisy linear dynamical system when a subset of sensors is arbitrarily corrupted by an adversary. We propose a secure state estimation algorithm and derive (optimal) bounds on the achievable state estimation error. In addition, as a result of independent interest, we give a coding theoretic interpretation for prior work on secure state estimation against sensor attacks in a noiseless dynamical system.

math.OC↗

arXiv2014

Harnessing Bursty Interference in Multicarrier Systems with Feedback

We study parallel symmetric 2-user interference channels when the interference is bursty and feedback is available from the respective receivers. Presence of interference in each subcarrier is modeled as a memoryless Bernoulli random state. The states across subcarriers are drawn from an arbitrary joint distribution with the same marginal probability for each subcarrier and instantiated i.i.d. over time. For the linear deterministic setup, we give a complete characterization of the capacity region. For the setup with Gaussian noise, we give outer bounds and a tight generalized degrees of freedom characterization. We propose a novel helping mechanism which enables subcarriers in very strong interference regime to help in recovering interfered signals for subcarriers in strong and weak interference regimes. Depending on the interference and burstiness regime, the inner bounds either employ the proposed helping mechanism to code across subcarriers or treat the subcarriers separately. The outer bounds demonstrate a connection to a subset entropy inequality by Madiman and Tetali.

cs.IT↗

arXiv2013

Using Feedback for Secrecy over Graphs

We study the problem of secure message multicasting over graphs in the presence of a passive (node) adversary who tries to eavesdrop in the network. We show that use of feedback, facilitated through the existence of cycles or undirected edges, enables higher rates than possible in directed acyclic graphs of the same mincut. We demonstrate this using code constructions for canonical combination networks (CCNs). We also provide general outer bounds as well as schemes for node adversaries over CCNs.

cs.IT↗

arXiv2013

Opportunistic Interference Management for Multicarrier systems

We study opportunistic interference management when there is bursty interference in parallel 2-user linear deterministic interference channels. A degraded message set communication problem is formulated to exploit the burstiness of interference in M subcarriers allocated to each user. We focus on symmetric rate requirements based on the number of interfered subcarriers rather than the exact set of interfered subcarriers. Inner bounds are obtained using erasure coding, signal-scale alignment and Han-Kobayashi coding strategy. Tight outer bounds for a variety of regimes are obtained using the El Gamal-Costa injective interference channel bounds and a sliding window subset entropy inequality. The result demonstrates an application of techniques from multilevel diversity coding to interference channels. We also conjecture outer bounds indicating the sub-optimality of erasure coding across subcarriers in certain regimes.

cs.IT↗