Ashish J. Khisti
Professor
Directs the Signals, Multimedia and Security Laboratory, working on information theory, learned compression and machine learning.
Ashish J. Khisti's lab at Toronto works on learning theory & optimization, trustworthy & safe ML and efficient training & inference. 30 papers since 2023, 13 at top venues, mostly ISIT and NeurIPS. 2 PhD students and 1 master's student. 10 alumni. Applications close .
At a glance
- papers, 2023–2026
- 8/ year
- at T1 venues
- 43%
- PhD students
- 2
- MS students
- 1
- alumni listed
- 10
Papers by yearfrom DBLP, 2023–2026
- 2 papers2026 · T1 1 · collecting
- 9 papers2025 · T1 4
- 10 papers2024 · T1 4
- 9 papers2023 · T1 4
T1 venuesOtherVenues: ISIT, NeurIPS, ICLR, AISTATS, ICASSP
Research focuspaper titles that use each topic's words
- Learning theory & optimization3 papers
- Efficient training & inference2 papers
- learned1 paper
- Trustworthy & safe ML0 papers
- signals0 papers
Paper list
From DBLP.
20262 papers
20259 papers
- NeurIPS (T1)Channel Simulation and Distributed Compression with Ensemble Rejection Sampling (opens in a new tab)
- IEEE J. Sel. Areas Commun.Guest Editorial: Rethinking the Information Identification, Representation, and Transmission Pipeline: New Approaches to Data Compression and Communication (opens in a new tab)
- IEEE J. Sel. Areas Commun.Information Compression in the AI Era: Recent Advances and Future Challenges (opens in a new tab)
- NeurIPS (T1)List-Level Distribution Coupling with Applications to Speculative Decoding and Lossy Compression (opens in a new tab)
- ICLR (T1)Multi-Draft Speculative Sampling: Canonical Decomposition and Theoretical Limits (opens in a new tab)
- ISITOn List Decoding With Importance Sampling (opens in a new tab)
- IEEE J. Sel. Areas Inf. TheoryRate-Distortion-Perception Tradeoff for Gaussian Vector Sources (opens in a new tab)
- NeurIPS (T1)Robust Federated Finetuning of LLMs via Alternating Optimization of LoRA (opens in a new tab)
- IEEE Trans. Inf. TheoryUniversal Rate-Distortion-Perception Representations for Lossy Compression (opens in a new tab)
202410 papers
- AISTATS (T1)Importance Matching Lemma for Lossy Compression with Side Information (opens in a new tab)
- NeurIPS (T1)Minimum Entropy Coupling with Bottleneck (opens in a new tab)
- JMLR (T1)On the Generalization of Stochastic Gradient Descent with Momentum (opens in a new tab)
- NeurIPS (T1)Random Cycle Coding: Lossless Compression of Cluster Assignments via Bits-Back Coding (opens in a new tab)
- ISIT WorkshopRate-Distortion-Perception Function of Gaussian Vector Sources (opens in a new tab)
- IEEE Trans. Inf. TheoryRate-Distortion-Perception Tradeoff Based on the Conditional-Distribution Perception Measure (opens in a new tab)
- ISITRate-Distortion-Perception Tradeoff for Lossy Compression Using Conditional Perception Measure (opens in a new tab)
- ISITSecure Inference for Vertically Partitioned Data Using Multiparty Homomorphic Encryption (opens in a new tab)
- ISITSubset Adaptive Relaying for Streaming Erasure Codes (opens in a new tab)
- ISITUnequal Message Protection: One-Shot analysis via Poisson Matching Lemma (opens in a new tab)
20239 papers
- IEEE Trans. Inf. TheoryAdaptive Relaying for Streaming Erasure Codes in a Three Node Relay Network (opens in a new tab)
- IEEE Trans. Commun.Deep Reinforcement Learning for Latency-Sensitive Communication With Adaptive Redundant Retransmissions (opens in a new tab)
- NeurIPS (T1)On the choice of Perception Loss Function for Learned Video Compression (opens in a new tab)
- ICML (T1)One-Shot Compression of Large Edge-Exchangeable Graphs using Bits-Back Coding (opens in a new tab)
- ISITQuadratic Functional Encryption for Secure Training in Vertical Federated Learning (opens in a new tab)
- ICLR (T1)Sequential Gradient Coding For Straggler Mitigation (opens in a new tab)
- IEEE Trans. Inf. TheoryStreaming Erasure Codes Over Multi-Access Relayed Networks (opens in a new tab)
- ISITStreaming Erasure Codes over Multicast Relayed Networks (opens in a new tab)
- ICASSP (T1)Time-Resolved FMRI Shared Response Model Using Gaussian Process Factor Analysis (opens in a new tab)
Where alumni went10 listed
Apply to TorontoFall 2027
Computer Science MSc/PhD
- Round
- Single round (Dec 1)
- Opens
- —
- GRE
- optional
- English
- TOEFL/IELTS if applicable
- Fee
- CAD 135
Confirmed on the program page.
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